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Negociação de reversão à média nos mercados futuros de energia.

Destaques.

Estudamos se a negociação técnica simples pode ser empregada de forma lucrativa para futuros de energia.

Estratégias com spreads de calendário com reversão à média com taxas de hedge dinâmicas são testadas.

Vinte e dois anos de dados históricos são testados com custos de transação e bootstrap.

Sinais de entrada e saída são gerados por Bollinger Bands.

Os melhores resultados são obtidos para Petróleo Bruto e Gás Natural.

Estudamos se estratégias comerciais simples que gozam de grande popularidade entre os profissionais podem ser empregadas de forma lucrativa no contexto de carteiras de hedge para futuros de petróleo bruto, gás natural, gasolina e óleo de aquecimento. As estratégias testadas baseiam-se em carteiras de spread de calendário com reversão da média estabelecidas com taxas de hedge dinâmicas. Os sinais de entrada e saída são gerados pelos chamados Bollinger Bands. O sistema de negociação é aplicado a vinte e dois anos de dados históricos de 1992 a 2013 para várias especificações, levando em conta os custos de transação. A significância dos resultados é avaliada com um teste de bootstrap no qual os pedidos gerados aleatoriamente são comparados com pedidos feitos pelo sistema de negociação. Considerando que encontramos a maioria das combinações envolvendo os futuros frente mês e segundo mês para ser significativamente lucrativa para todas as commodities testadas, os melhores resultados para o Índice de Sharpe ajustado ao risco são obtidos para WTI Crude Oil and Natural Gas, com Sharpe Ratios acima de 2 para a maioria das combinações e um desempenho bastante suave para todos os spreads do calendário. Com base em nossos resultados, há uma séria dúvida se os mercados futuros de energia podem ser considerados fracamente eficientes no curto prazo.

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Tag: Sistema de varejo de FX.

Entendendo o Mercado de Câmbio Internacional OTC (FX).

Entendendo o Mercado de Câmbio Internacional OTC (FX).

OTC FX Market é o maior mercado do mundo. Cerca de 5,1 trilhões de dólares são negociados nesse mercado todos os dias.

Originalmente, todas as transações de câmbio eram para negócios transfronteiriços de bens e serviços, mas posteriormente os desenvolvimentos levaram a atividades de investimentos especulativos em moedas estrangeiras.

OTC FX Market é descentralizado. Isso significa que não há troca em que as moedas são negociadas. O mercado interbancário de FX está entre os bancos de dealers. Dealer Banks são os maiores bancos globais. Os 10 principais bancos que negociam em FX têm um volume de comércio total de 67%.

O USD é a moeda dominante no mercado de câmbio global. O Reino Unido é o maior local para negociação de câmbio, seguido pelos EUA e Cingapura. A região metropolitana de Hong Kong e o Japão são outros importantes centros comerciais de câmbio.

Mercados operam 24/7 ao contrário de outros mercados financeiros que abrem e fecham em determinados momentos.

O Bank of International Settlement publica sua pesquisa trienal sobre os mercados globais de câmbio. A pesquisa de 2016 mostrou um volume de negócios de 5,1 trilhões de USD / dia de FX abaixo de 5,3 T / dia atrás na pesquisa de 2013. Os mercados atingiram o pico em setembro de 2014, com 6,5 trilhões de USD / dia. Desde então, a tendência está em declínio. O risco de descarte pelos bancos globais e o declínio no comércio global são citados como principais razões para o declínio. Tentará entender esse problema em uma data posterior.

Os problemas a seguir surgem desta postagem, mas não são discutidos aqui em detalhes.

Mercado de varejo de varejo Algorithmic Trading Non Bank Provedores de liquidez de alta frequência FX Prime Brokerage Estabilidade financeira no mercado OTC & # 8211; Caso da CCP China Compensação e liquidação de internacionalização RMB em mercados de FX & # 8211; CLS Bank e CLSNet Liquidity for FX trades & # 8211; Financiamento e Liquidez do Mercado.

Destaques do Inquérito Trienal 2016 do volume de negócios nos mercados cambiais OTC:

 A negociação nos mercados de câmbio alcançou uma média de US $ 5,1 trilhões por dia em abril de 2016. Isso caiu de US $ 5,4 trilhões em abril de 2013, um mês que havia visto uma maior atividade no iene japonês no contexto da evolução da política monetária naquela época.  Pela primeira vez desde 2001, o volume de negócios spot diminuiu. As transações à vista caíram para US $ 1,7 trilhão por dia em abril de 2016, de US $ 2,0 trilhões em 2013. Em contraste, o volume de negócios de swaps cambiais subiu ainda mais, alcançando US $ 2,4 trilhões por dia em abril de 2016. Esse aumento foi impulsionado em grande parte pelo aumento das negociações de swaps cambiais. envolvendo ienes.  O dólar dos EUA continuou a ser a moeda dominante, estando em um lado de 88% de todos os negócios em abril de 2016. O euro, o iene e o dólar australiano perderam participação de mercado. Em contraste, muitas moedas de mercados emergentes aumentaram sua participação. O renminbi dobrou sua participação, para 4%, para se tornar a oitava moeda mais negociada do mundo e a moeda de mercado emergente mais ativamente negociada, superando o peso mexicano. O aumento da participação do renminbi deveu-se principalmente ao aumento das negociações em relação ao dólar norte-americano. Em abril de 2016, até 95% do volume negociado em renminbi foi em relação ao dólar americano.  A quota de negociação entre os distribuidores inquiridos cresceu ao longo do período de três anos, representando 42% do volume de negócios em abril de 2016, em comparação com 39% em abril de 2013. Os outros bancos que não os revendedores contribuíram com mais 22% do volume de negócios. Os investidores institucionais foram o terceiro maior grupo de contrapartes nos mercados de câmbio, com 16%.  Em abril de 2016, os balcões de vendas em cinco países - Reino Unido, Estados Unidos, Cingapura, Hong Kong e Japão - intermediaram 77% do mercado de câmbio, ante 75% em abril de 2013 e 71% em abril de 2010.

Infraestrutura e Instituições Interbancárias de Mercado (OTC).

De todas as alterações nos rankings Euromoney FX de 2016.

O Citi mantém a classificação máxima do ranking de câmbio do Euromoney deste ano, mas em outros lugares houve mudanças sem precedentes.

Mudanças estruturais nos mercados, reviravoltas gerenciais entre muitos bancos grandes, novos entrantes não bancários e falta de volumes e volatilidade parecem ter nivelado o campo de disputa entre as maiores empresas do setor.

A maior mudança no ranking este ano é o declínio da participação de mercado combinada dos cinco principais bancos globais. Sua participação de mercado na pesquisa atingiu o pico em 2009 em 61,5% e ainda estava acima de 60% em 2014.

Este ano, os cinco principais bancos respondem por apenas 44,7% do volume total. As esperanças de muitos chefes de FX globais e seus chefes de banco de investimento - de que a participação dos grandes bancos aumentaria inexoravelmente à medida que o mercado se tornasse mais automatizado e que eles pudessem se beneficiar do poder de precificação oligopolista - agora parece distante e sonhos iludidos.

Um veterano do FX diz à Euromoney que o declínio da quota de mercado combinada dos cinco principais bancos é exatamente o que os reguladores gostariam de ter num mercado em que continuam a estar muito atentos ". ###

Embora a participação de mercado das 10 maiores casas de FX também caia, de mais de 75% no ano passado para apenas 66% este ano, a queda deve-se inteiramente ao desempenho dos cinco maiores bancos. Os bancos classificados do sexto ao décimo lugar no geral produziram uma quota de mercado combinada de 22%, praticamente em linha com os últimos cinco anos do inquérito e consideravelmente superior aos 14% que geriram em 2008.

O Citi amplia sua liderança sobre o segundo colocado na pesquisa, que os participantes do mercado consideram o reflexo mais preciso da atividade baseada em clientes nos mercados globais de câmbio, para mais de quatro pontos percentuais - mesmo que a participação de mercado do banco diminuiu em mais de três pontos percentuais, de 16,11% na pesquisa de 2015 para 12,91% das negociações em 2016.

Essa participação de mercado vencedora é a menor para qualquer banco de primeira linha na pesquisa desde que o UBS ganhou a pesquisa em 2004.

O Citi manteve sua liderança global em importantes áreas de produtos, como spot / forwards e swaps, bem como nas principais categorias de dinheiro real e clientes de bancos. Ele sobe um lugar este ano para ganhar corporações e participação no mercado eletrônico global, embora caia para terceiro em geral para as opções.

Uma grande história no ranking deste ano é o declínio do Deutsche Bank. Ele já foi o líder indiscutível no mercado global de divisas, perdendo a primeira posição no ranking Euromoney há três anos, após quase uma década de domínio.

Enquanto o novo CEO do grupo, John Cryan, se esforçou publicamente e privativamente para descrever o negócio FX como uma das joias da coroa do banco sitiado, os dias em que o Deutsche Bank conseguiu uma quota de mercado global de mais de 20% recentemente, em 2009) já se foram há muito.

No último conjunto de classificações, o Deutsche cai do segundo para o quarto lugar geral: sua participação de mercado de 7,86% é quase a metade do que era um ano atrás. O declínio do Deutsche Bank é generalizado, e os concorrentes dizem que isso foi motivado em parte pelo fato de o banco ter reduzido o número de clientes que cobre. Ele cai do segundo para o quinto ponto / frente; do segundo ao oitavo entre os clientes de dinheiro real e perde o primeiro lugar entre os clientes do banco. Continua a ser a principal casa de opções globais.

Talvez a queda mais surpreendente de todas seja sua participação no mercado eletrônico global. O sistema Autobahn da Deutsche revolucionou o comércio global de câmbio e, nos anos de bandeira, representou mais de um quarto de todo o comércio eletrônico. Este ano, o Deutsche só pode administrar o quarto lugar em participação de mercado eletrônico, de manter o topo do ranking no ano passado, e sua participação caiu de 17,5% para 8,73%.

Dois bancos ultrapassam o Deutsche para passar para os três primeiros lugares, mas as semelhanças terminam aí: os dois bancos em questão têm históricos recentes muito diferentes no mercado global de divisas.

O JPMorgan subiu para o segundo lugar na pesquisa, com uma participação de mercado de 8,77%, acima do quarto lugar, com 7,65% no ano passado. Por muitos anos, os concorrentes disseram que o JPMorgan não conseguiu aumentar seu peso em FX; classificou-se tipicamente fora dos cinco principais bancos na pesquisa da Euromoney na última década. Essas acusações têm menos peso agora, apesar de terem sido substituídas por rumores sobre a estratégia de preços competitivos do banco.

O banco dos EUA aumenta em várias categorias. Seus sucessos mais notáveis ​​estão ganhando a categoria de fundos alavancados, com uma vantagem sobre o segundo colocado UBS de quase oito pontos percentuais e uma participação de mercado de mais de 18%; e saltando do quinto para o segundo lugar no comércio eletrônico geral. O ranking ruim do JPMorgan está agora em opções, onde vem um humilde oitavo.

O UBS retorna aos três principais bancos globais de FX este ano. Vencedora em 2004, está fora dos três primeiros desde 2009 e, pela última vez, disputada pela liderança no ano anterior, quando sua participação de mercado de quase 16% foi vencida pelo Deutsche. No ano passado caiu para o quinto lugar, seu pior desempenho em uma década, com uma participação de mercado de 7,3%.

Dado que a liderança do banco passou os últimos anos a não enfatizar o papel do seu banco de investimento, alguns concorrentes acreditavam que o UBS estava em um longo e lento declínio no câmbio.

Mas, de forma silenciosa e consistente, os negócios de mercado do UBS estão se recalibrando para o novo ambiente de capital e mercados, bem como mantendo um compromisso com as melhores plataformas eletrônicas da categoria. Sua participação no mercado total sobe para 8,76%; e ele se classifica entre os três principais globais em spot / forward, swaps, market share eletrônico e para clientes bancários. Como o JPMorgan, é um retardatário nas opções, onde ocupa o sétimo lugar.

O JPMorgan e o UBS têm uma outra coisa importante em comum: enquanto outros bancos perderam bancos inteiros da alta gerência de suas equipes de FX nos últimos anos, o JPMorgan e o UBS têm estado relativamente estáveis.

No primeiro, Troy Rohrbach supervisiona o negócio de FX desde 2005 (agora ele também administra taxas e finanças públicas globalmente); no UBS, Chris Murphy e George Athanasopoulos, os co-chefes globais de câmbio, taxas e crédito, entraram no banco há mais de cinco anos e, juntos, comandam a divisão desde 2013. A liderança, ao que parece, conta.

O Bank of America Merrill Lynch continua sua ascensão constante no ranking dos últimos anos, de um nadir de 12º lugar de 2009 para 2012. Ele finalmente entra no ranking das cinco principais casas globais de FX, acima do sexto lugar no ano passado.

A BAML subiu no ranking entre as cinco maiores para as corporações e as contas de dinheiro real, e ganhou terreno tanto em swaps como em opções - no segundo, ocupa o segundo lugar globalmente. Mas a BAML ainda tem trabalho a fazer no mercado eletrônico, onde sua classificação geral caiu do sexto para o sétimo lugar. Outros bancos dos EUA também tiveram bom desempenho.

O Goldman Sachs subiu do nono para o sétimo lugar e Morgan Stanley saltou três posições para voltar ao top 10.

Não foi um bom ano em FX para o Barclays. Talvez a decisão do banco de não ter um chefe global de divisas tenha saído pela culatra. O banco britânico-transatlântico caiu do terceiro para o sexto, e sua participação de mercado passou de 8,11% para 5,67%.

O Barclays caiu três posições para o sétimo lugar no spot / forward, quatro posições para sétimo nos swaps e três posições para nono nas opções. Entre os grupos de clientes, sua maior reversão veio entre as contas de dinheiro real, caindo do quarto lugar no ano passado para fora do top 10.

O HSBC também teve um ano decepcionante, caindo do sétimo para o oitavo lugar no geral. Ele também perde seu ranking no ranking das corporações no ano passado, caindo dos cinco primeiros da categoria de clientes no total. O comércio eletrônico continua sendo o elo mais fraco do banco e pode até estar enfraquecendo, já que o banco cai para o nono lugar na participação geral do mercado eletrônico.

Os bancos sempre subiram e caíram nos rankings da Euromoney nos últimos 40 anos, mas este ano vê um novo fenômeno - o advento do provedor de liquidez não bancário. Liderando o caminho está o XTX Markets, um derivado da GSA Capital, cujo co-CEO, Zar Amrolia, foi um freqüente vencedor do ranking Euromoney FX em seu papel anterior como chefe do negócio de FX do Deutsche Bank.

Em seu primeiro ano de elegibilidade, a XTX apenas no local faz uma estréia impressionante: o nono lugar no ranking geral com uma participação de mercado de 3,87%; quarto no spot / forwards; quinto para clientes bancários; terceiro para plataformas de negociação de FX; quinto no geral para a participação no mercado eletrônico; e terceiro para negociação eletrônica de vendas à vista, à frente do Deutsche Bank, com uma participação de mercado de mais de 10%.

XTX é o líder, mas não o único participante não bancário da pesquisa. A Tower Research Capital, a Jump Trading, a Virtu Financial, a Lucid Markets e a Citadel Securities são as 50 maiores no ranking geral de participação de mercado.

O nono lugar geral da XTX parece uma linha na areia para os mercados de FX. Os bancos acima dele são, na maioria das vezes, os demais tomadores de preços; os bancos abaixo muitas vezes tomadores de preços, com a capacidade de fazer mercados em determinadas moedas ou produtos.

Muitos dos bancos classificados fora do top 10 no geral este ano estão entendendo a liquidez de provedores não-bancários como XTX, Tower e Jump. Eles parecem preparados para ganhar mais participação de mercado no futuro, ajudados por novas tecnologias, modelos de negócios mais definidos e uma base de infra-estrutura de custo mais baixo do que os tradicionais bancos de FX. Eles poderiam procurar construir capacidade nos mercados futuros e outros no futuro próximo.

Entre as plataformas multi-dealer, a Thomson Reuters - através do seu serviço FXall - continua a ser a líder clara com uma quota de mercado de 30%, embora a sua margem em relação ao segundo classificado FXConnect tenha diminuído quase pela metade. O grande aumento entre os MDPs foi o terceiro colocado HotspotFXi, que aumentou sua participação de mercado de menos de 7% para quase 18% este ano.

Os volumes totais da pesquisa da Euromoney FX chegaram a quase US $ 95 trilhões, enquanto o número de votos se manteve estável em comparação com o ano passado, em cerca de 3.500 clientes. Isso representa uma queda de volume de cerca de 23% no ano passado, em linha com as expectativas do mercado.

Existem três tipos de plataformas de negociação.

As plataformas de negociação podem ser divididas em três tipos diferentes:

Plataformas eletrônicas de corretagem. Essas plataformas foram desenvolvidas na década de 1990 e são consideradas, segundo o Bank for international Settlements (BiS, 2010), como a fonte dominante de liquidez interbancária no mercado de câmbio. Eles mediam informações sobre os preços indicativos de vários criadores de mercado. EBS e Reuters, com sede em Londres, são as duas plataformas dominantes dentro desta categoria. Plataformas multi-banco. Essas plataformas também são conhecidas como ECNs de vários bancos (redes de comunicação eletrônica). Eles foram criados na primeira década deste século e assemelham-se à categoria anterior, na medida em que mediam os preços de vários criadores de mercado. Uma diferença é que eles têm regulamentações de acesso mais livres para os criadores de mercado, o que facilita a participação dos criadores de mercado nessas plataformas. Outra diferença é que eles são amplamente utilizados fora do mercado interbancário, ou seja, pelos participantes do mercado que não são bancos. As plataformas americanas Fx All, currenex, Hotspot Fx, State Street e Fx connect são exemplos desse tipo de plataforma de negociação. Existem também plataformas que fornecem funções padronizadas de negociação algorítmica como um serviço. O currenex é uma dessas plataformas. Plataformas de banco único. Esse tipo de plataforma é executado por um banco individual. A plataforma medeia apenas os preços do banco individual para vários pares de moedas, ao contrário das plataformas de negociação discutidas acima, que medeiam os preços de vários criadores de mercado. na Suécia, a SeB tem uma plataforma desse tipo, a SeB Trading Station. Outros exemplos de bancos com essas plataformas são o JP Morgan, o Deutsche Bank e o Citibank.

O J. P. Morgan aumentou significativamente sua presença nessas plataformas nos últimos dois anos e agora ocupa o primeiro lugar em penetração global, seguido de perto pelo Citi. O Bank of America Merrill Lynch, o Deutsche Bank e o HSBC completam os cinco principais bancos mais proeminentes em MDPs.

B. Plataformas de Revendedor Único.

Embora os sistemas multi-dealers estejam claramente em ascensão, uma média de mais de 20% do volume de transações de bancos e fundos de hedge ainda é executada em plataformas de banco único. O Barclays, o Citi e o Deutsche Bank são as três principais plataformas mais ativamente utilizadas no mundo.

As plataformas proprietárias proporcionam aos bancos um meio de reter volumes de negociação lucrativos, de modo que os revendedores estão expandindo esses sistemas para fornecer uma gama de opções de liquidez que permitem aos clientes acessar o mercado de várias maneiras, incluindo liquidez divulgada e não divulgada. negócios de agência e principal, e links para execução baseada em troca, & # 8221; diz o diretor gerente da Greenwich Associates, Woody Canaday.

Os revendedores também estão nos primeiros dias do que promete ser uma corrida armamentista total em negociações algorítmicas. Atualmente, apenas 13% dos clientes FX de primeira linha usam modelos de negociação algorítmica. No entanto, essa parcela se aproxima de um quarto entre os maiores participantes do mercado de buy-side e 30% entre os hedge funds.

Duas tendências sugerem que o comércio algorítmico está ganhando força no FX. Primeiro, os participantes do mercado que usam modelos algorítmicos estão usando um número crescente de revendedores para algoritmos. Em segundo lugar, os usuários atuais estão direcionando as ações crescentes de volume de negócios através dos modelos, de 25% em 2014 para 28% em 2015. Os fundos de hedge que comercializam agora usam esses modelos para cerca de metade do volume total de negociações.

A. Plataformas de corretagem eletrônicas entre revendedores.

Reuters Dealing 3000 ICAP EBS.

EBS é o principal local de negociação para EUR / USD, USD / JPY, EUR / JPY, USD / CHF, EUR / CHF e USD / CNH.

O Thomson Reuters Matching é o principal local de negociação para pares Commonwealth (AUD / USD, NZD / USD, USD / CAD) e de mercados emergentes.

A EBS foi criada por uma parceria do maior mercado de câmbio (FX) do mundo, fazendo com que os bancos em 1990 desafiassem a Reuters & # 8217; ameaçaram o monopólio das divisas interbancárias no mercado local e proporcionam uma concorrência efectiva. Em 2007, aproximadamente US $ 164 bilhões em transações de câmbio à vista foram negociadas todos os dias sobre a carteira de pedidos de limite central da EBS, EBS Market.

O concorrente mais próximo da EBS é a Reuters Dealing 3000 Spot Matching. A decisão de um operador de FX de usar o EBS ou o Thomson Reuters Matching é motivada principalmente por pares de moedas. Na prática, a EBS é a principal plataforma de negociação para EUR / USD, USD / JPY, EUR / JPY, USD / CHF, EUR / CHF e USD / CNH, e a Thomson Reuters Matching é o principal local de negociação da Commonwealth (AUD / USD, NZD / USD, USD / CAD) e pares de moedas de mercados emergentes.

A EBS iniciou o comércio eletrônico de metais preciosos, incluindo ouro, prata, platina e paládio, e continua sendo o principal corretor eletrônico de ouro e prata no mercado Loco de Londres.

Eles foram a primeira organização a facilitar negociações ordenadas de caixa preta ou algorítmica em FX spot, por meio de uma interface de programação de aplicativos (API). Em 2007, isso representou 60% de todo o fluxo de EBS.

Além de câmbio spot e metais preciosos, a EBS expandiu os produtos de trading através de seus locais para incluir opções NDFs, forwards e FX. Também aumentou o alcance do estilo de negociação para incluir RFQ e streaming em ambientes divulgados e não divulgados.

A EBS foi adquirida pela ICAP, a maior corretora inter-concessionária do mundo, em junho de 2006. A ICAP disse que a aquisição combinaria os pontos fortes da EBS em câmbio eletrônico com o negócio de Broking Eletrônico da ICAP para criar um único negócio global de multiprodutos com maior crescimento economias de escala potenciais e significativas. Continuou dizendo que forneceria aos clientes uma execução mais eficiente do comércio eletrônico, reduziria os custos de integração e daria acesso a ampla liquidez em uma ampla gama de produtos. [1]

Em 2014, a EBS fundiu-se com a BrokerTec - uma provedora líder de serviços nos mercados de renda fixa - para formar a EBS BrokerTec. A oferta da BrokerTec inclui soluções de negociação para muitos produtos de renda fixa dos EUA e da Europa, incluindo títulos do Tesouro dos EUA, títulos do governo europeu e European Repo.

A EBS BrokerTec é agora reconhecida como provedora de tecnologia e soluções de comércio eletrônico líder de mercado, oferecendo acesso a múltiplas opções de execução e liquidez diversificada e valiosa em todos os mercados de FX e de renda fixa.

A ICAP EBS é uma das principais corretoras intermediárias do mundo, com um volume médio diário de transações superior a US $ 2,3 trilhões. A plataforma eletrônica EBS da ICAP fornece o principal mercado de interesse natural para mais de 2800 traders globais de FX, Precious Metals e NDF. A plataforma de acesso global ICAP EBS oferece Liquidez FX anônima, transparente e confiável. Dados de mercado históricos e em tempo real autorizados. Disponível apenas para compensação. Relação com EBS requerido.

B. Plataformas Multidealer & # 8211; FX ECNs.

Desde 1999, os bancos vêm desenvolvendo sistemas proprietários para seus clientes para negociar divisas e acessar material de pesquisa pela Internet. Para negociar com vários bancos online, os clientes precisam usar uma variedade de métodos de autenticação, sites e métodos de solicitação de preço. As plataformas multibanco evoluíram para permitir que os clientes usem um único site para solicitar preços simultaneamente de vários bancos e visualizar material de pesquisa on-line. Plataformas multibanco (também conhecidas como ECNs ou redes de comunicação eletrônica) oferecem vantagens significativas para os clientes, mas menos vantagens para os bancos e, portanto, a participação ativa dos bancos nas plataformas de múltiplos bancos é impulsionada principalmente pela demanda dos clientes. No entanto, para os bancos, continua a ser preferível que os seus clientes negociem através de sistemas proprietários de bancos, uma vez que os bancos evitam pagar corretagem e os clientes são encorajados a concentrarem-se apenas nos preços de um banco particular.

Existem cinco principais ECNs FX voltadas para o cliente:

FXall & # 8211; fundada pelo Bank of America, pelo Credit Suisse First Boston, pelo Goldman Sachs, pelo HSBC, pelo JP Morgan, pelo Morgan Stanley Dean Witter e pelo UBS.

Currenex & # 8211; independente e empreendimento apoiado pelos principais participantes do mercado, e. Barclays Capital e Royal Dutch / Shell.

Conexão FX & # 8211; de propriedade da State Street.

360T & # 8211; independente e de risco apoiado por investidores financeiros e grandes investidores privados.

Hotspot FXi & # 8211; empresa independente de capital de risco de capital fechado.

Câmbio de moeda muda para sistemas multi-distribuidor, diz Greenwich.

14 de julho de 2015, 11:28 AM EDT.

Investidores cambiais estão usando cada vez mais sistemas eletrônicos conectados a múltiplos revendedores, já que o mercado é submetido a um maior escrutínio pelos reguladores, segundo a Greenwich Associates.

Investidores institucionais e grandes corporações executaram 49 por cento de seus volumes negociados em plataformas de negociação no ano passado, ante 45 por cento em 2013 e 38 por cento em 2008, disse o consultor em Stamford, Connecticut. O aumento vem à medida que a negociação por métodos tradicionais, como telefone, mensagens instantâneas e plataformas de revendedores, caiu.

"O 'escândalo de conserto' da FX e as multas bancárias relacionadas já desempenharam um papel importante na mudança do comportamento de compra", escreveu Kevin McPartland, chefe de pesquisa de estrutura de mercado e tecnologia em Greenwich, co-autor do relatório baseado em entrevistas com mais mais de 1.600 pessoas participando dos mercados de câmbio do mundo todo.

As empresas de gestão de ativos estão impulsionando o comércio eletrônico, uma vez que o escrutínio regulatório desestimula bancos e revendedores a fornecer “cor de mercado” aos clientes para evitar qualquer percepção de impropriedade, de acordo com Greenwich. As plataformas também estão se tornando mais populares à medida que os bancos se tornam menos ativos nos mercados de câmbio, devido à crescente necessidade de capital.

“Os gerentes de ativos trabalharam proativamente para reforçar as políticas internas para garantir o máximo retorno dos fundos afetados e para tranquilizar os clientes, como fundos de pensão e fundos soberanos, que estão obtendo o melhor que o mercado tem a oferecer naquele momento. tempo ”, escreveu McPartland.

A plataforma FXall da Thomson Reuters Corp. teve a maior participação ponderada em volume de negociação no ano passado, com 21%, segundo a Greenwich. Ele é seguido em popularidade por 360T, Currenex da State Street Corp., FXGO da Bloomberg LP e FX Connect.

Conexão de FX da State Street FX Thomson Reuters Currenex CBOE / BATS da FXall State Street FX Bloomberg FXGO.

C. Plataformas de Negociação FX de Revenda Única.

Barclays BARX Citi Velocity Deutsche Autobahn Morgan Stanley Matriz UBS Neo.

Melhor Plataforma de Negociação FX de Revenda Única.

Financial News tem o prazer de anunciar o. Os vencedores serão anunciados em um jantar de gala em Londres em outubro.

Aqui estão os indicados na categoria de Melhor Plataforma Única de Negociação FX:

A plataforma BARX continua sendo uma força dominante entre as plataformas de revendedores únicos, com preços de streaming em mais de 80 moedas e 480 pares de moedas, com uma ampla gama de produtos disponíveis. Após o lançamento do BARX Gator, um agregador de liquidez, o Barclays agora oferece aos clientes acesso ao estilo de agência cada vez mais popular.

Desde o seu relançamento em 2012, o Citi Velocity 2.0 tornou-se uma das principais fontes de liquidez de um único banco em operações cambiais, opções e taxas cambiais. O Citi também liderou a adoção da tecnologia de celulares e tablets nesse espaço e concentrou seus esforços com a Velocity no fornecimento de velocidade, menores custos de transação, informações de ativos cruzados, negociação de ativos cruzados, liquidez profunda e eficiência de desktop.

O Deutsche Bank canalizou recursos significativos para sua franquia de negociação eletrônica nos últimos anos, e a Autobahn continua sendo um importante player em todas as classes de ativos. No FX, Autobahn fornece um único blotter para negociações executadas via canais de voz e eletrônicos. Os usuários podem, assim, se beneficiar de uma visão combinada e assumir maior controle sobre seus portfólios.

Embora não seja um banco de primeira linha em FX, o Morgan Stanley tem procurado agregar valor único com sua plataforma Matrix. Isso foi conseguido em parte por meio de serviços de execução e pós-negociação, mas também por meio do grupo de soluções quantitativas e inovações do banco, que desenvolve ferramentas analíticas exclusivas para ajudar os usuários a tomarem decisões de investimento e negociação mais informadas.

Lançado em 2013, o UBS Neo é uma plataforma de classe entre ativos que oferece um único ponto de acesso com uma forte experiência do usuário, restabelecendo o banco suíço como um participante significativo no comércio eletrônico. O UBS Neo FX abrange 550 pares de moedas, com acesso a dinheiro, NDFs e opções disponíveis através da plataforma.

Tendências no uso de plataformas eletrônicas.

Da pergunta de US $ 4 trilhões: o que explica o crescimento do câmbio desde a pesquisa de 2007?

Métodos de execução eletrônica estão transformando o mercado de câmbio A maior atividade de todos os três tipos de clientes mencionados acima - traders de alta frequência, bancos como clientes e investidores de varejo - está intimamente relacionada ao crescimento dos métodos de execução eletrônica nos mercados de câmbio. A Greenwich Associates estima que mais de 50% do volume total de negociações em moeda estrangeira está sendo executado eletronicamente (Gráfico 3, painel à esquerda).

Os métodos de execução eletrônica podem ser divididos em três categorias: corretores eletrônicos, sistemas de negociação de vários bancos e sistemas de negociação de banco único. Os corretores eletrônicos foram introduzidos no mercado de câmbio entre os dois setores em 1992. Para os clientes, no entanto, o principal canal de negociação continuou sendo o contato direto com os revendedores por telefone. No mercado de FX bastante opaco e fragmentado da década de 1990, as barreiras à entrada eram altas e a concorrência era limitada. Os clientes geralmente pagam grandes spreads em suas transações de FX.

O primeiro sistema de negociação de múltiplos bancos foi o Currenex, lançado em 1999. Ao oferecer aos clientes cotações concorrentes de diferentes revendedores de FX em uma única página, a Currenex aumentou a transparência, reduziu os custos de transação e atraiu uma base crescente de clientes. O FXConnect da State Street, que havia sido lançado em 1996 como um sistema de negociação de banco único, atendendo apenas aos clientes da State Street, abriu em 2000 e tornou-se um ECN de vários bancos.

Em resposta ao aumento da concorrência, os principais revendedores de FX lançaram sistemas de negociação de banco único para seus clientes, como o BARX do Barclays em 2001, o Autobahn do Deutsche Bank em 2002 e o Velocity do Citigroup em 2006. De acordo com os dados fornecidos ao BIS, os volumes médios diários de negociação nos principais sistemas de negociação de banco único aumentaram em até 200% nos últimos três anos.

O mercado Forex é um mercado internacional over-the-counter (OTC). Isso significa que é um mercado descentralizado, autorregulado, sem central de câmbio ou câmara de compensação, ao contrário dos mercados de ações e futuros. Essa estrutura elimina as taxas de troca e compensação, reduzindo assim os custos de transação.

O mercado Forex OTC é formado por diferentes participantes - com necessidades e interesses variados - que negociam diretamente entre si. Esses participantes podem ser divididos em dois grupos: o mercado interbancário e o mercado de varejo.

O mercado interbancário designa transações Forex que ocorrem entre bancos centrais, bancos comerciais e instituições financeiras.

Bancos Centrais & # 8211; Os bancos centrais nacionais (como o Fed dos EUA e o BCE) desempenham um papel importante no mercado Forex. Como principal autoridade monetária, seu papel consiste em alcançar estabilidade de preços e crescimento econômico. Para isso, eles regulam toda a oferta monetária na economia, estabelecendo taxas de juros e reservas mínimas. Eles também administram as reservas cambiais do país que podem usar para influenciar as condições de mercado e as taxas de câmbio.

Bancos Comerciais & # 8211; Os bancos comerciais (como o Deutsche Bank e o Barclays) fornecem liquidez ao mercado Forex devido ao volume de negociação que controlam todos os dias. Parte dessa negociação representa conversões de moeda estrangeira em nome de clientes & # 8217; precisa enquanto alguns é realizado pelos bancos & # 8217; mesa de negociação proprietária para fins especulativos.

Instituições Financeiras & # 8211; Instituições financeiras, como gestores de recursos, fundos de investimento, fundos de pensão e corretoras, negociam moedas estrangeiras como parte de suas obrigações de buscar as melhores oportunidades de investimento para seus clientes. Por exemplo, um gerente de uma carteira internacional de ações terá que se envolver em operações de câmbio para comprar e vender ações estrangeiras.

O mercado de varejo designa transações feitas por especuladores e investidores menores. Estas transacções são executadas através de corretores Forex que actuam como mediadores entre o mercado retalhista e o mercado interbancário. Os participantes do mercado de varejo são os fundos de hedge, corporações e indivíduos.

Fundos de hedge & # 8211; Os fundos de hedge são fundos de investimento privado que especulam em várias classes de ativos usando alavancagem. Fundos de hedge macro perseguem oportunidades de negociação no mercado Forex. Eles projetam e executam operações depois de realizar uma análise macroeconômica que analisa os desafios que afetam um país e sua moeda. Devido às suas grandes quantidades de liquidez e suas estratégias agressivas, eles são um dos principais contribuintes para a dinâmica do mercado Forex.

Corporações & # 8211; Eles representam as empresas que estão envolvidas em atividades de importação / exportação com contrapartes estrangeiras. Seu principal negócio exige que eles comprem e vendam moedas estrangeiras em troca de mercadorias, expondo-as a riscos cambiais. Através do mercado Forex, eles convertem moedas e se protegem contra flutuações futuras.

Indivíduos & # 8211; Comerciantes individuais ou investidores negociam Forex em seu próprio capital, a fim de lucrar com a especulação sobre as taxas de câmbio futuras. They mainly operate through Forex platforms that offer tight spreads, immediate execution and highly leveraged margin accounts.

Only 200 billion daily turnover using exchanges.

Exchanges are staking out the $5tn a day global currency market as part of their latest efforts to tap this lucrative and booming sector that has long been dominated by global banks.

This week Bats Global Markets, the US’s second largest equities exchange, fired the latest salvo by offering three months of free trading on its forthcoming London-based Hotspot currency trading platform, the centrepiece of Bats’ $365m purchase of the venue from KCG Holdings in March.

That came only days after Deutsche Börse, Europe’s largest exchanges operator, bought 360T, one of the world’s largest currency trading networks, for €725m.

Their moves are audacious attempts to break into the world’s most liquid over-the-counter market, where a notional $5.3tn a day is traded in cash, or spot, and derivatives trades. It is dominated by banks, which continue to make billions of dollars in profits from it each year. Exchanges have generally been unable to establish a presence in this and other OTC markets, despite repeated attempts to do so.

In currencies, Chicago’s CME Group dominates futures trading , reflecting how it seized the terrain in the 1970s when the present era of floating foreign exchanges began. Markets in Moscow, Brazil and India also trade local currency, but of that $5.3tn total, global exchanges account for just $200bn according to Aite Group, a financial markets consultancy.

However, cracks are appearing in the market edifice, brought on by a combination of unlawful activity by banks, deep structural change and the emergence of cheap and reliable technology that has allowed alternative ways of trading to emerge.

“The banks as a whole will continue to have a substantial piece of the pie but the regulations will force them to let go of pieces of it,” says Javier Paz, an analyst at Aite Group.

Waves of post financial crisis regulation have accelerated change in equity and interest rate swaps markets, but global policymakers largely left the currency market alone.

However, the currency industry is mopping up after two of its own existential crises — the Wm/Reuters benchmark rate rigging scandal, which resulted in multibillion-dollar fines for banks, and the sudden move by the Swiss franc in January when the national central bank abolished its ceiling against the euro.

Market observers say that end users such as corporations, hedge funds and asset managers are now taking far more care with their orders, and they have the tools to do it, turning the banks more into agency brokers.

“End users are getting used to technology where they have a full view of the market. They are accessing more markets than they could ever do 10 years ago,” says Chris Concannon, chief executive of Bats Global Markets.

At the same time, incidents like the Swiss move have also raised the alarm among banks. By the end of that day in January some smaller retail brokers faced ruin but even several larger broker-dealers such as Barclays, Citigroup and Deutsche Bank nursed tens of millions of dollars in losses. That has also left the market seeking as many different venues as possible where they can offset their customers’ trades.

“People are not holding risk like they were a year ago. A year ago they would warehouse that risk and wait for another customer to come along,” says Mr Concannon.

Not helping matters is how foreign exchange market liquidity is highly concentrated among just a handful of trading pairs, known as the G10. Into the gap on the other side of the trade are stepping high-frequency traders such as the US’s Virtu Financial. It is one of the world’s largest currency market makers.

“Clients that are trading on anonymous platforms by definition have no insight into whom they are trading with, and as such are likely interacting with non-bank liquidity providers more often than they know,” notes a report by Greenwich Associates last month.

However, even if the diagnosis is right, e xchanges still face tough competition from well-established platforms not run by banks, such as Thomson Reuters, Bloomberg FXGO and ICAP’s EBS . These make up the majority of the $1.1tn average daily volume traded on electronic FX platforms and provide a role as a more centralised price benchmark independent of banks.

Bats, which has targeted London because it is the world’s main location for forex trading, will aim to provide a reliable venue for pricing and take more trading volume from the 220 banks, asset managers, hedge funds, dealers and retail brokers signed up to the venue.

Deutsche Börse sees 360T as a key part of its growth strategy, using it as a way to sell market data and develop futures, FX forwards and swaps trading to boost its Eurex derivatives business. But it is trading network, not an exchange-like central limit order book.

Critically, OTC markets are historically highly resistant to encroachment from exchanges and some see little sign of that changing.

The head of one currency trading platform says: “I don’t see any signs of moving to an exchange model. I don’t see a slam dunk here, I see some desperate buyers looking for a growth story.”

OTC FX trading becomes ‘exchange-like’

Thursday, April 21, 2016.

The acquisition of trading platforms Hotspot and 360T by Bats Global Markets and Deutsche Börse respectively last year were bold statements of intent by exchange operators to grab a larger chunk of the trillions of dollars traded in FX every day.

However, while consolidation in the venues supporting FX trading can be expected to result in exchanges becoming more involved in the FX space, any actual market structure change is likely to take a long time to materialize, according to.

FXSpotStream CEO Alan Schwarz.

“The FX market continues to do a good job of addressing regulatory requirements and meeting the demands of market participants,” ele diz.

“We have seen a shift in the FX market looking to trade more on a disclosed basis. Our business has continued to see year-on-year growth because there is a move taking place from exchange-like anonymous trading to bilateral, fully disclosed trading between counterparties.

“Unlike trading on an exchange, the relationship via FXSpotStream is transparent and trading with the liquidity providing banks is on a fully disclosed basis.”

Kevin McPartland, head of market structure and technology research at Greenwich Associates, believes that discussion of migration from OTC to exchange fails to take account of some of the nuances of the FX market and that the future lies in venues that support multiple trading models.

“There are a host of non-exchange electronic trading venues that allow clients to trade with each other in a variety of ways,” ele diz.

On the question of whether there is a discernible shift towards fully disclosed trading, McPartland refers to both central limit order book (CLOB) and request-for-quote (RFQ) having their merits.

Despite observations made by the likes of TeraExchange – that order book platforms offer a democratic marketplace through transparent, firm and executable prices – corporates have remained reluctant to abandon the RFQ model.

The key question for CLOB platform providers continues to be not why market participants have migrated to alternative models but rather when they will be in a position to win new business for products that are most suited for order books, such as the benchmarks and plain vanilla products.

“RGQ offers liquidity on demand and identification of counterparties, whereas CLOB is faster and its anonymity can be helpful,” says McPartland.

“But we are now seeing demand for a solution that provides the best of both worlds by enabling trading in an order book format while maintaining a bilateral relationship with counterparties.”

According to James Sinclair, CEO of MarketFactory, options and other derivatives are moving closer to an exchange model due to the direct effects of regulation and the increased costs of compliance in OTC markets.

He refers to CME FX options as an example, noting they are effectively options on futures.

“However, the situation in the spot market is more complicated – some aspects are becoming closer to an exchange, others are moving further away,” ele diz. “FX has its own market structure that is hard to fit into the OTC/exchange paradigm.”

One of the fundamental reasons why the market does not become centrally cleared, says Sinclair, is that a cleared model carries the cost of insurance against both settlement and market risk.

“CLS insures you against settlement risk but not the market risk,” he adds. “Counterparts still find it cheaper to self-insure against market risk in case of a counterparty default than to pay the extra cost of a fully cleared solution.”

A senior platform source observes that growth in exchange-traded products has largely come from futures traders who have looked for diversification and added FX as another asset class.

“Very little business has moved from OTC – some banks have added exchanges as additional liquidity sources to cover risk, but that is really the only business that has crossed the divide,” the source says.

OTC has become more exchange-like in that the largest banks have continued to extend their internalization of flow, so each now runs an order book trading structure internally.

However, our source also points out that the tightening of credit has reduced the number of prime brokers in FX and costs have risen “so the nearest thing that the FX OTC market has to centralized clearing has actually reduced its volume and capacity”, he concludes.

Evolution of Information Exchange in Trading Platforms.

Clients C Voice Broker VB Dealers D Electronic Broker EB Prime Broker PB Retail Aggregator – RA Multi Bank Trading – MBT Single Bank Trading – SBT.

Top 10 FX Turnover Locations.

Currencies and Currency Pairs.

US Dollar is the king in FX market. 87.6% of transactions include USD on one side of currency pair. Euro comes at second with 31%. Japanese Yen is at 21.6%. UK Pound Sterling is at 12.8%. Chinese Yuan has moved to 4%.

Currencies and Currencies Pairs.

Electronic Trading Algorithmic Trading High Frequency Trading Non Bank Liquidity Providers (Market Makers)

Non Bank Electronic Market Makers.

The diverse set of non-bank electronic market-makers includes.

These market-makers’ trading volume is captured in the Triennial because their trades are prime-brokered by a dealer bank. They are active on multilateral trading platforms, where they provide prices to banks’ e-trading desks, retail aggregators, hedge funds and institutional clients.

Second in Trade Finance Sixth in Payments Eighth in FX Trading.

Considering China’s Renminbi for International Settlement and Forex Trading.

On October 1, 2016, the International Monetary Fund added China’s renminbi1 (RMB) to its elite Special Drawing Right (SDR) basket of currencies, alongside the U. S. dollar, euro, yen and British pound. IMF said the change reflected China’s progress in reforming its monetary, foreign exchange and financial systems, and improving its financial market infrastructure.2 Short-term, this means countries can now include RMB assets in official FX reserves, making it easier for them to meet IMF guidelines.3 Beyond this, however, inclusion in SDR is a symbol of RMB’s emergence as an international currency for forex trading and settlement of global business transactions.

RMB’s ongoing progress is an important consideration for businesses involved in any FX trading, and particularly for those whose business or currency trading activities involve China.

O uso de RMB cresce no comércio e troca de moeda.

IMF’s decision arrives in the context of growing RMB usage in trade finance, international payments, and forex trading. In trade finance, RMB is now second amongst world currencies, reflecting enormous international trade with China.4.

Since 2013, according to the Society for Worldwide Interbank Financial Telecommunication’s (SWIFT’s) monthly Renminbi Tracker, China’s currency has risen from ninth to fifth worldwide in total payments sent and received by value, not counting payments by central banks. Durante esse período, superou a coroa sueca (SEK), o dólar canadense (CAD), o franco suíço (CHF), o dólar australiano (AUD) e, brevemente durante o verão de 2015, até mesmo o iene (JPY). RMB use is growing slowly in some markets (such as France, Switzerland and Germany), and is rapidly accelerating in others (e. g., the United Arab Emirates).5 SWIFT has elsewhere reported that 50 countries now use RMB for 10 percent or more of their trade with China.6.

Meanwhile, according to the Bank for International Settlements’ (BIS’) September 2016 Central Bank Survey, RMB has doubled its share of OTC currency trading transactions since 2013. It has surpassed Mexico’s peso to become the most active developing market currency on forex trading exchanges, and is now eighth in FX trading amongst all currencies worldwide. BIS’s report notes that “as much as 95 percent of renminbi trading volume was against the U. S. dollar.”7.

Building the Global Infrastructure for an Internationalized Currency.

To promote RMB usage abroad, the People’s Bank of China (PBOC) – China’s central bank – has authorized 18 new official clearing banks worldwide since December 2012. These have opened in locations including Toronto, Buenos Aires, London, Paris, Johannesburg, Sydney, Seoul and Taipei.8 In September 2016, PBOC announced the first RMB clearing and settlement services in the U. S.9.

Domesticamente, a China eliminou um teto para o número de empresas autorizadas a realizar assentamentos transfronteiriços de RMB. Any company permitted to engage in import-export business may settle in RMB, unless it appears on a “black name list” (in which case its transactions may be reviewed individually).10 Restrictions have also been relaxed on RMB-denominated investments by foreigners.11.

As Yu Yongding of the Asian Development Bank Institute has pointed out, China is the only country that has ever decided on its own to make internationalizing its currency a national priority.12 In determining how far RMB’s internationalization will go, China’s authorities appear to be balancing the benefits and risks of liberalization,13 carefully timing their decisions accordingly.

They face significant obstacles, not least the continuing downward pressure on the value of China’s currency on forex trading exchanges since it peaked against the U. S. dollar in early 2014. Some market observers believe RMB faces bank sector headwinds that might require a government bailout,14 as well as increased protectionist pressures in the U. S.15 and elsewhere. If these events lead to further reductions in RMB’s value, China could face accelerating capital flight,16 deepening internal opposition to the full elimination of capital controls.

China’s reforms have made it easier for companies that do business in China to settle their transactions in RMB if they so desire. Many of their Chinese trading partners would welcome this, and some may even offer discounts if they can invoice in RMB.17 China’s central bank has estimated that transacting in U. S. dollars may add 2-to-3 percent in administrative expenses alone.18.

O risco de flutuação cambial, no entanto, continua sendo uma questão importante. Existem veículos de cobertura; Claro, estes têm seus próprios custos. Ao tomar a decisão sobre transacionar negócios em RMB ou outra moeda, as empresas podem querer fazer avaliações cuidadosas e oportunas sobre o risco cambial.

As China’s financial and market reforms move forward, RMB is emerging as a leading international currency. It has become far easier for international businesses and currency traders to transact in China’s home currency. As empresas internacionais podem querer considerar cuidadosamente o risco cambial no desenvolvimento de seus próprios planos de negociação e liquidação de forex RMB.

PB (Prime Brokerages) Inter Dealer Multi Dealer Trading Single Dealer Trading HFT (High Frequency Trading) Market Makers Liquidity Providers Retail Aggregators Retail FX Systems Algorithmic Trading FX ECNs (Electronic Communication Networks) e-Trading Hedge Funds Institutional Clients Non Bank Liquidity Providers FXPB (Foreign Exchange Prime Brokerage)

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Downsized FX markets: causes and implications.

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TheForeign Exchange andInterest Rate Derivatives Markets:Turnover in the United States, April 2016.

The foreign exchange and over-the-counter interest rate derivatives market in the United Kingdom.

Quarterly Bulletin 2016 Q4.

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Deal or no deal: anatomy of an FX portal.

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Most Innovative Bank e-FX Trading Platform: Citi.

Citi sells its electronic FX platform.

Nasdaq poised to launch FX trading platform: top executive.

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The New Wall Street: Even Big Banks Want Help Navigating Markets.

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Small Fish Big Prize: Market Makers out to eat Bank’s lunch.

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High Frequency Traders Elbow Their Way Into the Currency Markets.

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Sterling’s flash crash was triggered during Asian ‘graveyard shift’ when US/European traders away.

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Supply Chain Finance (SCF) / Financial Supply Chain Management (F-SCM) February 12, 2018 Gantt Chart Simulation for Stock Flow Consistent Production Schedules February 1, 2018 Instant, Immediate, Real Time Retail Payment Systems (IIRT-RPS) January 31, 2018 Network Economics of Block Chain and Distributed Ledger Technology January 12, 2018 Consciousness of Cosmos: A Fractal, Recursive, Holographic Universe November 16, 2017 Integral Philosophy of the Rg Veda: Four Dimensional Man November 6, 2017 Meta Integral Theories: Integral Theory, Critical Realism, and Complex Thought November 3, 2017 Boundaries and Networks October 31, 2017 Regional Trading Blocs and Economic Integration October 28, 2017 Global Liquidity and Cross Border Capital Flows October 25, 2017 Production Chain Length and Boundary Crossings in Global Value Chains October 22, 2017 Intra Industry Trade and International Production and Distribution Networks October 17, 2017 Cash and Investments: Corporate Savings Glut in USA October 12, 2017 Why do Firms buyback their Shares? Causes and Consequences. October 10, 2017 Understanding Trade in Intermediate Goods October 10, 2017 Production and Distribution Planning : Strategic, Global, and Integrated October 5, 2017 Trends in Intra Firm Trade of USA September 27, 2017 FDI vs Outsourcing: Extending Boundaries or Extending Network Chains of Firms September 25, 2017 Slowdown in Global Investment (FDI) Flows September 24, 2017 Trends in Cross Border Mergers and Acquisitions September 20, 2017 Trading Down: NAFTA, TPP, TATIP and Economic Globalization September 19, 2017 Boundary Spanning in Multinational and Transnational Corporations September 18, 2017 On Inequality of Wealth and Income – Causes and Consequences September 12, 2017 Rising Profits, Rising Inequality, and Rising Industry Concentration in the USA September 3, 2017 Why are Macro-economic Growth Forecasts so wrong? August 23, 2017 Low Interest Rates and Business Investments : Update August 2017 August 1, 2017 Low Interest Rates and Monetary Policy Effectiveness July 15, 2017 Low Interest Rates and Banks’ Profitability : Update July 2017 July 9, 2017 Some of my earlier published papers June 18, 2017 Short term Thinking in Investment Decisions of Businesses and Financial Markets May 24, 2017 Systems Biology: Biological Networks, Network Motifs, Switches and Oscillators March 27, 2017 Hierarchy Theory in Biology, Ecology and Evolution March 22, 2017 Bank of Finland’s Payment And Settlement System Simulator (BoF-PSS2) March 16, 2017 On Anticipation: Going Beyond Forecasts and Scenarios March 15, 2017 Clock of the Long Now: Time and Responsibility March 10, 2017 Socio-Cybernetics and Constructivist Approaches March 8, 2017 Growth and Form in Nature: Power Laws and Fractals March 6, 2017 Shapes and Patterns in Nature March 1, 2017 TARGET2 Imbalances in European Monetary Union (EMU) February 27, 2017 Economics of Digital Globalization and Information Data Flows February 26, 2017 Development of Global Trade and Production Accounts: UN SEIGA Initiative February 24, 2017 Accounting For Global Carbon Emission Chains February 22, 2017 Stock Flow Consistent Models for Ecological Economics February 21, 2017 Currency Credit Networks of International Banks February 17, 2017 The Dollar Shortage, Again! in International Wholesale Money Markets February 15, 2017 Understanding Global OTC Foreign Exchange (FX) Market February 12, 2017 Global Financial Safety Net: Regional Reserve Pools and Currency Swap Networks of Central Banks February 10, 2017 Evolving Networks of Regional RTGS Payment and Settlement Systems February 7, 2017 Cross Border/Offshore Payment and Settlement Systems February 6, 2017 Large Value (Wholesale) Payment and Settlement Systems around the Globe February 4, 2017 Structure and Evolution of EFT Payment Networks in the USA, India, and China February 2, 2017 Next Generation of B2C Retail Payment Systems January 31, 2017 Relational Turn in Economic Geography January 29, 2017 Economics of Trade Finance January 27, 2017 Understanding Global Value Chains – G20/OECD/WB Initiative January 25, 2017 The Collapse of Global Trade during Global Financial Crisis of 2008-2009 January 24, 2017 Oscillations and Amplifications in Demand-Supply Network Chains January 22, 2017 Financial Stability and Systemically Important Countries - IMF-FSAP January 18, 2017 Balance Sheets, Financial Interconnectedness, and Financial Stability – G20 Data Gaps Initiative January 16, 2017 Integrated Macroeconomic Accounts, NIPAs, and Financial Accounts January 15, 2017 A Brief History of Macro-Economic Modeling, Forecasting, and Policy Analysis January 12, 2017 Low Interest Rates and International Investment Position of USA January 10, 2017 Jay W. Forrester and System Dynamics January 9, 2017 Increasing Returns, Path Dependence, Circular and Cumulative Causation in Economics January 7, 2017 Economic Growth Theories – Orthodox and Heterodox January 4, 2017 Long Wave Economic Cycles Theory December 30, 2016 Mergers and Acquisitions – Long Term Trends and Waves December 28, 2016 Business Investments and Low Interest Rates December 22, 2016 The Decline in Long Term Real Interest Rates December 19, 2016 Low Interest Rates and Banks Profitability: Update – December 2016 December 15, 2016 Hierarchical Planning: Integration of Strategy, Planning, Scheduling, and Execution December 4, 2016 External Balance sheets of Nations November 29, 2016 Low Interest Rates and International Capital Flows November 23, 2016 Networks and Hierarchies November 12, 2016 Systems View of Life: A Synthesis by Fritjof Capra October 27, 2016 Milankovitch Cycles: Astronomical Theory of Climate Change and Ice Ages October 2, 2016 Process Physics, Process Philosophy September 17, 2016 Shape of the Universe September 4, 2016 Myth of Invariance: Sound, Music, and Recurrent Events and Structures August 26, 2016 Sounds True: Speech, Language, and Communication August 19, 2016 Mind, Consciousness and Quantum Entanglement August 12, 2016 Society as Communication: Social Systems Theory of Niklas Luhmann August 8, 2016 Geometry of Consciousness August 5, 2016 Art of Long View: Future, Uncertainty and Scenario Planning July 31, 2016 Reflexivity, Recursion, and Self Reference July 27, 2016 Truth, Beauty, and Goodness: Integral Theory of Ken Wilber July 24, 2016 Semiotics, Bio-Semiotics and Cyber Semiotics July 22, 2016 Autocatalysis, Autopoiesis and Relational Biology July 19, 2016 Systems and Organizational Cybernetics July 17, 2016 Micro Motives, Macro Behavior: Agent Based Modeling in Economics July 15, 2016 Feedback Thought in Economics and Finance July 13, 2016 Repo Chains and Financial Instability July 11, 2016 Multiplex Financial Networks July 11, 2016 Glimpses of Ancient Indian Mathematics July 9, 2016 Bring back M3 – Monetary Aggregate July 8, 2016 Increasing Returns and Path Dependence in Economics July 7, 2016 Economics of Money, Credit and Debt July 6, 2016 Boundaries and Relational Sociology July 5, 2016 George Dantzig and History of Linear Programming July 3, 2016 Phillips Machine: Hydraulic Flows and Macroeconomics July 1, 2016 Monetary Circuit Theory June 30, 2016 Morris Copeland and Flow of Funds accounts June 30, 2016 Financial Social Accounting Matrix June 29, 2016 Classical roots of Interdependence in Economics June 28, 2016 Stock-Flow Consistent Modeling June 26, 2016 Foundations of Balance Sheet Economics June 24, 2016 Contagion in Financial (Balance sheets) Networks June 22, 2016 Interdependence in Payment and Settlement Systems June 19, 2016 Evolution of Banks Complexity June 17, 2016 Economics of Broker-Dealer Banks June 17, 2016 Shadow Banking June 13, 2016 Low Interest Rates and Risk taking channel of Monetary Policy June 4, 2016 Funding Strategies of Banks June 2, 2016 Non Interest Income of Banks: Diversification and Consolidation May 31, 2016 Impact of Low Interest Rates on Bank’s Profitability May 22, 2016.

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Trading Multiple Strategies With The Same Instrument – Part 1.

Tradestation is a pretty amazing testing and development platform. I’ve been using it for over 10 years, and I’ve been very happy overall with it. Sure, there are certain aspects of it I don’t like, and certain things that are hard to do, but I think that is true of almost any platform.

My biggest pet peeve with Tradestation is that it is hard to trade 2 automated strategies in the same market at the same time. Why is this even important? Why not just trade the best strategy, and shelve the one that does not perform as well? Many people do just that; unfortunately they usually pick the wrong strategy to trade!

I take the opposite approach in my trading – I assume that I don’t know what strategy will perform best in the next 6 months, or at any point in the future. Plus, I also assume that all strategies will eventually break, and have to be retired. So, I want plenty of strategies for each market, and I want to trade them all. This leads to diversification, which has been known for years to smooth out the overall equity curve.

With that in mind, consider the equity curves and code for two very simple strategies. Shown in Figure 1 is a momentum strategy, and Figure 2 depicts a simple moving average system. Both are applied to daily Crude Oil bars for the past few years, and do not include slippage or commissions. If you traded both at the same time, you’d want the equity curve to look like Figure 3, which is just the daily sum of the two strategies.

So, how could you do this – trade 2 strategies in the same market at the same time - in Tradestation? There are quite a few options to do this. Unfortunately, all have advantages and, most importantly, disadvantages.

Option 1 would be to insert the two strategies on the same chart. That leads to decent results in this particular case, as shown in Figure 4. But, it does not equal the equity curve we want (although in this case it is fairly similar), shown earlier in Figure 3. In general, this is not a good alternative. Too many times, inserting two strategies on the same chart will lead to terrible results.

Option 2 would be to combine the code for each strategy into one “super code” and trade that. The code for each would be cut and pasted into the “master” code, with no changes at all. With this technique, however, the resulting strategy still does not mimic the curve we want. This is especially true if the strategy uses Tradestation reserved words, such as openpositionprofit and market position . This method is just another disappointment.

Option 3 is to trade each strategy, in a separate chart, in the same account. Depending on the code, though, this option can be fraught with disaster. For example, you cannot use the Trade Manager Position Match feature any longer, so you’ll never know if your real life positions match both strategies. And that is just the tip of the iceberg. Stop losses, market position based rules and many other nuances in Tradestation make this a very difficult alternative. The end result with this option, based on my personal trading experience, is that it just is not a feasible option.

Option 4 is to trade each strategy in its own chart window, and use a third party tool to manage or enter the positions. This is a good solution, but there is a fee for this 3 rd party software. Some possible software packages that may help you with this option:

Of the three, I have only used the Ninjatrader option to send orders from Tradestation thru Ninja to another broker. But, I did not use this interface specifically to trade multiple strategies with one instrument. One of these solutions may work for your particular case.

Option 5 is to trade each strategy in its own chart, in its own account. So, if I had 3 ES strategies, I would trade each strategy in a separate account, meaning I’d need 3 accounts. This is the method I have primarily used. It has its advantages – it makes bookkeeping each strategy a bit easier, and the strategies can be tracked fairly easily in Tradestation’s Trade Manager.

But, the approach also has its downside. For instance, it is a very non-optimal way to use margin. If I go long in one account 1 contract of ES, that ties up $5060 in margin. And if I go short 1 ES in another account, it would tie up another $5060. So, I end up with 2 positions, in opposite directions, meaning in reality I am net flat, and instead of needing $10,120 in margin, I should need $0!

Although there may be other potential solutions to the problem, I am going to with Option 6 . This solution involves summing up position in multiple strategies, and then just trading to reach the net position. For example, if strategy #1 was long, that would be +1. If strategy #2 was also long, that would be +1 also. Summed up, that would mean you should be +2, or long 2 contracts.

The nice thing about this solution is that you could potentially trade many strategies at the same time, and have the result traded through and depicted on only one chart. So, each individual strategy could be evaluated separately (using the Strategy Factory process, I teach for example), and then it could be “added” to other profitable strategies.

In theory, that seems like an easy proposition. Just sum up positions, and trade the net. But, reality is quite a bit tougher, due to the way Tradestation works, and also just due to the nature of the problem.

In the next installment of this series, we will take the first step towards creating a “super” strategy that trades 2 strategies – creating strategies that perform similarly to the original strategies of Figure 1 and 2, but that will lend themselves to being combined into the super, master strategy.

If you would like to learn more about building trading systems be sure to get a copy of my latest book, Building Winning Algorithmic Trading Systems.

Other Articles in This Series.

About the Author Kevin Davey.

Kevin Davey é um operador profissional e um desenvolvedor de sistemas de alto desempenho. Kevin é o autor de "Building Algorithmic Trading Systems: Uma viagem do comerciante da mineração de dados para Monte Carlo Simulation to Live Trading" (Wiley Trading, 2014.). He generated triple digit annual returns 148 percent, 107 percent, and 112 percent in three consecutive World Cup of Futures Trading Championships® using algorithmic trading systems. His web site, kjtradingsystems, provides trading mentoring, trading signals, and free trading videos and articles. He writes extensively in industry publications such as Futures Magazine and Active Trader and was featured as a “Market Master” in the book The Universal Principles of Successful Trading by Brent Penfold (Wiley, 2010). Active in social media, Kevin has over 15,000 Twitter followers. An aerospace engineer and MBA by background, he has been an independent trader for over 20 years. Kevin continues to trade full time and develop algorithmic trading strategies.

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This is a genuine huge problem in trading.

a potential solution is to write your own position manager and persist to a file in case you restart tradestation.

i avoided this problem by writing my own traing platform.

Thanks for the comments – you bring up a couple more options that I did not mention. Pretty cool that you developed your own platform!

Please let me know what you think as I further explain the idea in Parts 2 and 3. In my testing so far, it has worked well, so it may be a decent solution for many, but it will not be ideal for everyone.

I suspect your trading platform does exactly what you want in this regard, which is awesome.

I’ve been battling this exact problem the past 2 years…I ended up going w/ option 5 as that was the only way I could figure it out with my less than stellar coding skills. Looking forward to seeing how you solved this issue.

I can’t say I’ve solved it, but as you will see, it does seem to work pretty well. Some tradeoffs are necessary, but I think the benfits outweigh the disadvantages…

Obrigado pelo comentário!

hi Kevin, will do.

another important issue I encountered when running multiple strategies; is the throughput. When more than one strategy is sending an order at the same time; latency goes much higher; and it may take few seconds to finish them up; not to mention ‘timeout errors’.

for a serious systematic trader; who wish to run multiple strategies; Tradestation, Metatrader, TradeNavigator, etc are unfortunately not suitable.

however these platforms are great for research and development. I still use some of them for that purpose.

Yes, that is a concern, especially if you have limit orders, cancel a lot of orders, run on short time frames, etc. I usually swing trade, without a lot of orders, and almost never use limit orders. So, the approach I lay out in this series can work well in those cases…

I’m not a tradestation user jet but I saw that there is a feature that says: “Allow multiple automated strategies on multiple charts using the same futures/forex symbol on the same account”. I thought that checkbox would make tradestation aware of what strategy took what trade?

I currently use Multicharts and I have not run into this problem there.

Theoretically this should work, but in reality it is tough. For example, if you use certain keywords (like openpositionprofit), there can be confusion with the combining of strategies (does openpositionprofit apply to both, just first position, just second, etc?).

I would give it a try in real time, and see though. It may work out for your strategies. My experience is that it did not work well for me.

[…] This is the approach I am using these days. You give up some features, but I've found it useful for my own trading: 3 Part Article, here is part 1 Trading Multiple Strategies With The Same Instrument ? Parte 1 e # 8211; Sucesso do comerciante do sistema [& # 8230;]

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Robot Wealth.

It's time to level the playing field.

Robot Wealth provides the knowledge, tools and support that give independent traders the edge professionals rely on.

Get the edge pro traders rely on.

Like most of us, you were probably drawn to trading at least partly due to the potential to generate income or grow your capital .

And if you’ve been trading for a while, no doubt you’ve discovered the other side of this equation: namely, that trading is difficult. Really difficult.

When I first started out, an old pro that I happened to know warned me that trading is the hardest way to make easy money . At the time, I don’t think I truly appreciated these words, but a decade later, they ring truer than ever.

Over the course of my trading journey, I spent plenty of time as an amateur retail trader trying to make a fist of it. I’ve also been a hedge fund quant trader, consulted to fund managers and trading firms of all shapes and sizes, and been a partner and researcher in a proprietary trading firm. I’ve also worked with over 200 DIY traders trying to achieve their goals.

This means that I’ve seen both sides of the fence. I’ve been up close and personal with the difficulties faced by individuals, and I’ve helped some of the biggest investment houses in the Asia-Pacific region adopt technologies like machine learning and artificial intelligence.

If there’s one thing that these experiences taught me it’s this:

Professional traders have a huge advantage over retailers.

This is something that is almost never talked about, but it is truer than you can imagine. Aqui está o porquê:

In a professional trading firm, there are teams of people with highly developed specialist skills . There are expert programmers, traders with decades of experience in the markets, and quants whose sole purpose is to hunt for new and innovative sources of alpha. An individual trader needs to be adept in all of these skills. For example, he can’t lean over to the next desk and get some advice about implementing a cross-exchange statistical arbitrage strategy. He can’t ask that seasoned discretionary trader in the corner – you know, the one who consistently makes jaw-dropping returns but struggles to find a date on the weekend – how the order book impacts his trading decisions. The retailer has to rely on his own knowledge and skills , and no individual can match the depth and breadth of knowledge you find in a professional trading shop. For the professionals, acquiring complex and expensive data sets is simply a small cost of doing business. And so is the hardware and expertise to process them and extract whatever signal they might contain. Professional traders have access to this sort of data and the secrets buried within . For an individual trader, a commercial data product is probably not an option. And if it is an option, it doesn’t come at a small cost relative to the capital tied up in the trader’s account. To acquire an innovative, signal-rich data set cheaply is difficult, and requires creativity and time. As a result, most retail traders are looking at exactly the same data that everyone has access to . It’s hard to outperform starting from there. Professional traders typically pay tiny trading costs . Since they do a lot of volume, their brokers or clearers are often happy to cut them exceptionally good deals on their brokerage rates. I’ve personally seen futures contracts traded for twelve cents a lot and heard of even lower rates than that. Combined with first-class execution (for example thanks to co-located trading servers and highly optimized data processing infrastructure), professional traders’ cost of trading is typically very low . A retailer’s brokerage on futures contracts, for example, is usually measured in dollars, not cents like it is for professionals. Not many retailers can afford co-location and often their market links and data processing code was not written by C++ gurus. That means that retailers pay much more to trade than a professional does – a difference of an order of magnitude or more is completely normal . That means that a retailer’s trading strategy actually has to make a lot more per trade than a professional trader’s strategy.

So compared to his professional counterpart, the independent trader has to build better strategies using less data and he has to do it alone .

Does that sound daunting? It should, because it really is a monumental task. But here’s the good news :

Robot Wealth exists exclusively to level the playing field between retail and professional traders.

Our courses and code library provide the knowledge and tools that you find in a professional trading firm. Our community , which consists of everyone from complete beginners to former bank traders, hedge fund managers and proprietary traders, provides the human support that professionals take for granted. We show you how to find and acquire innovative data sets that can give you an edge (and we share some of ours as well). We can even help you get a better deal on your trading costs through our broker relationships.

So if you’re serious about making it as an independent trader, consider what you’re missing out on by going it alone. Join our community now, or keep scrolling for more information.

Become brilliant at the basics .

. and set yourself up for success.

Fundamentals of Algorithmic Trading.

We have designed Fundamentals of Algorithmic Trading to get you from novice programmer to skilled trading systems researcher in as little as 11 weeks . Even if you’ve never coded before.

A fundamental prerequisite of algorithmic trading success is the ability to put your trading ideas into computer code and design scientifically robust, statistically sound experiments to test them. That means that if you can’t already, you are going to have to learn to code, and you’re going to need an accurate simulation (backtesting) tool. You’ll also need to learn the basics of robust experimentation.

In Fundamentals of Algorithmic Trading , you’ll learn the essentials of programming via the Lite-C scripting language as well as the foundations of scientifically robust experimentation and design. By the end of the course, you’ll be able to code trading ideas based on technical analysis, price action, seasonal volatility and multiple time frames. We’ll even introduce some quant-style techniques like digital signal processing and machine learning. You’ll get loads of coded examples, including ten fully coded strategies to get you started . But more importantly than putting such ideas into code, you’ll learn how to optimize and test them in a scientifically robust manner, which is crucial if your strategy is to perform in live trading as it did in simulation.

If you’re a new coder, the progress you’ll make in the 11 weeks of this course will probably blow your mind (at least that’s what our past students tell us). But this course really does just provide the foundations for algorithmic trading success – there is whole wide world to explore once you’ve got the fundamentals under control (don’t worry, we’ve got you covered there too).

Coding for Algorithmic Trading.

Get up and running with the Lite-C scripting language, a simple yet extremely powerful and flexible coding language that is perfect for research and development of trading strategies. Even complete beginners can learn Lite-C quickly – I’ve had non-coders write simple algorithms after a single weekend when delivering this content face to face. If there is a simpler way to get started with coding, I have not seen it. This module is written to get novice coders writing their own algorithms as quickly as possible, but seasoned programmers will find it a quick and efficient portal to the Zorro Trading Automation Platform – the best backtesting platform on the market for its price today. Find out more about why I recommend this under-appreciated language and it’s professional-grade trading platform in the FAQ, or check out the video below of me coding a simple momentum strategy in Lite-C and testing it out in Zorro (the video demonstrates Zorro’s optimization, walk-forward analysis, and portfolio trading tools – which are really just scratching the surface of what Zorro is capable of, but provides a nice introduction):

A Robust Approach for Developing Trading Algorithms.

You’ve learned how to operate the tools of the trade; now learn how to use them wisely. Professional trading firms use processes and systems to ensure research is efficient, robust and ultimately profitable There are just too many traps and pitfalls to not take a systematic approach to the development workflow itself. We will teach you about the subtle statistical biases that contaminate strategy research, and just how easy it is to abuse the powerful tools we have at our disposal. You will learn a professional workflow for robust strategy development that avoids these traps and leads to confidence in your decision to trade or discard a strategy.

Practical Examples of Automated Strategies.

Professionals talk to their colleagues and share their thoughts on trading strategies, which leads to new ideas that the sharer might not have thought of. The more strategies you’ve discussed or seen, the deeper your pool of inspiration. We provide 10 example strategies with detailed explanatory notes to illustrate the practicalities of using Lite-C for research and development and to use as inspiration or starting points for your own strategies. The example strategies are exactly that and we don’t recommend trading them. However, they do provide practical insights into how a strategy is put together programmatically and will likely provide frameworks for you to build upon. Strategy examples include momentum, mean-reversion, price-action, seasonal volatility and machine learning.

“The course has been a huge benefit, and my only regret is not being able to do it a few years back , because if I had, I’m sure I’d be in a much better position now.”

“I can confirm this is a great course. I now look at algorithmic trading from a totally different perspective . Definitely highly recommended.”

“I wanted to take the course to learn a more structured way to use Zorro effectively but discovered more fundamental and important knowledge with respect to robust development of trading algos.”

You're doing well with the Prius .

. next take the wheel of the Ferrari.

Comércio Algorítmico Avançado.

Now that you’re brilliant at the basics, it’s time to learn the quantitative tools that the professionals use and propel your algo trading beyond the amateur ranks.

Advanced Algorithmic Trading builds on the skills you learned in the Fundamentals course and introduces a whole range of advanced statistical, quantitative, and machine learning tools used in professional practice. You’ll learn to think like a quant and put your ideas to the test by designing statistically sound experiments. You’ll also learn how to build portfolios of trading algorithms across multiple markets and time scales that diversify risk and compound returns.

Risk Management and Quantitative Portfolio Construction.

This is where you will start learning to think like a quant. You will begin to see trading as less of a forecasting exercise and more of a problem of taking and managing risk. Seeing the markets in those terms is a sure sign of a growing sophistication and maturity as a trader – and we’ll help you get there! The course provides a holistic treatment of risk management and portfolio construction with a sharp focus on the practical. The emphasis is on empowering you with the tools to make objective, practical, and data-driven decisions about risk management and portfolio construction that you can apply in real life.

On the Shoulders of Giants.

R is a programming language for statistical computing used widely in both academia and industry. It’s power lies in the more than 13,000 (!) packages that have been contributed by users (a package is a set of pre-written functions – including documentation – related to some task or subject). This means that R is unparalleled in its capabilities for statistical computing, data science, and advanced analytics: almost anything you might care to do has likely already been implemented. The course will get you up to speed fast with the sometimes strange nuances of R and you will learn how to use any R package directly in your trading algorithms. From Kalman filters to cointegration tests to neural networks…you will learn how to stand on the shoulders of giants and leverage cutting edge tools without having to implement them from scratch.

Advanced Analytical Tools.

The Zorro trading automation platform incorporates a suite of tools for advanced analytics that originated in fields as diverse as engineering, statistics, information theory, machine learning and finance. You will learn how to leverage the power of these tools to build innovative and robust trading systems through numerous practical code examples and detailed explanations.

Trade Management Functions.

The algorithmic micro-management of individual trades is one of the most useful and frequently used tools in the algo trading arsenal. Learn how Zorro’s framework for trade micro-management works including the programming concepts that are critical for a detailed understanding. Also included are detailed code examples and descriptions of the extremely useful things you can do with trade management functions, like scaling into and out of positions, designing custom order types, adjusting stop levels based on indicators, and much more.

Advanced Algo Trading Utilities.

There are a lot of moving parts in any serious algo trading technology stack, and as time goes on you will incorporate many tools and utilities that support yours. We show you how to build and design such utilities that optimize and automate as much of the research and execution environment as possible. This part of the course is all about leveraging automation and outsourcing as much as possible to machines: you’ll learn how to incorporate web-based or other external data into your trading algorithms in real time, to build user-interfaces to control how an algorithm trades, to send warning SMS and email messages from your trading algorithms, to control aspects of the windows environment, and more.

Working With Time.

Time is a critical factor in many trading algorithms. Markets very often behave differently depending on the time: in the 24-hour world of FX and some futures markets, often day boundaries are arbitrary and trading strategies will perform very differently depending on where that boundary is set. Time is in general an under-appreciated component of trading system research, but learning to use it to your advantage can be enormously beneficial. You will learn everything you need to know to incorporate this crucial aspect of the markets in your research and development.

Education gets you started .

. community keeps you going.

Members' Only Forum and Slack Channels.

Connect with other algo traders.

Colaborar.

Share insights and ideas.

Acelere sua aprendizagem.

Join the conversation in our members’ forum and find answers, make new connections and be inspired by a thriving global community of DIY algo traders.

Exclusive Content.

Exclusive Code Downloads.

Research frameworks.

Advanced Tool Insights.

In-depth articles and bonus tools.

We are adding to our exclusive code library all the time – it is an incredible resource to take your strategy development in new directions and provide inspiration. Save time and effort by leveraging pre-written research frameworks that you can use to experiment with various approaches, like machine learning.

“This course will get you to the point of being able to test most strategies whilst understanding sound back testing methodology and optimization techniques.

“There’s always a focus on robustness… Don’t expect to be handed a couple of winning strategies so that you can sit back and trade them forever. Instead, you will learn how to develop and test strategies while taking into consideration biases and pitfalls that affect traders.

I highly recommend this course, Kris truly knows his stuff. & # 8220;

& # 8220; Working with Kris is the equivalent to having your own Quant . You have access to levels beyond simple trade automation!”

COMPARE MEMBERSHIP FEATURES.

Exclusive Members' Content.

(Coded strategies, research frameworks, data)

Members-only Slack Channel.

Algo Research Project Library*

New units released weekly.

Immediate, full access.

New units released weekly.

Immediate, full access.

Immediate, full access.

Perguntas frequentes.

What will I learn from your courses?

Simply, you’ll learn everything that I would seek to learn if I were starting my algo trading journey all over again.

The courses cover a lot of ground. At a high level, you will learn the technical skills that enable algorithmic trading. You will also learn about the various statistical biases and traps that tend to thwart aspiring amateur algo traders. You’ll also learn research processes, workflows, and quantitative tools & techniques that you would find in a professional quantitative trading firm.

For more detail, including descriptions of modules and titles of individual units, check out the curricula for the Fundamentals and Advanced courses respectively.

What prerequisite knowledge will I need?

To get the most out of the course, you will need as a minimum a beginner’s level of knowledge about the financial markets. In particular, you should know about the mechanics of trading, for example, the different order types, the difference between exchange-traded and over-the-counter markets, and stop losses and position sizing. Ideally, you will have done some manual trading already.

If you don’t have this level of knowledge, you’ll get the best value for your membership if you do some research prior to signing up. There is plenty of material available online for free that will get you up to speed with these prerequisites. Get started with our Back to Basics: Introduction to Algorithmic Trading blog series and one or two of the more introductory texts in our recommended reading list.

Note that programming knowledge is NOT required as a prerequisite – we will get you up to speed with what you need to know. See the next FAQ for more detail.

I've never coded before. Where should I start? Is this course for me?

If you have no prior coding experience, that’s OK! In fact, the first module of Fundamentals of Algorithmic Trading was designed specifically with you in mind! It will get you up to speed with Lite-C – an extremely flexible, powerful, yet simple scripting language – in a surprisingly short amount of time. In fact, you will likely be writing your first code within hours of starting the Fundamentals course.

In many ways, the first part of the Fundamentals course is an ideal introduction to programming. It will take you through all the important basic concepts and will leave you with the ability to turn simple trading ideas into code. As you progress through the remainder of the Fundamentals course and the Advanced courses, your programming skills will continue to grow. Both courses contain dozens of fully explained code examples and you will be supported via the member forums.

I'm an experienced coder, what's in it for me?

If you already know coding, that’s great! That means you already have one of the key skills for successful algo trading and can dive into the others, like statistically sound backtesting, risk management, portfolio construction, and the rest. These other skills, particularly their practical application, are the subject of the latter part of Fundamentals and the entire Advanced course. Fundamentals will help you leverage your existing coding skills by showing you how to apply them wisely and effectively in a research process for algo trading. Advanced will show you how to take that process further by incorporating quantitative, statistical and machine learning tools into your arsenal.

If you are already a coder, you can probably skip the first part of Fundamentals , however it will provide a handy reference for Lite-C syntax and have you up and running with the language in a matter of hours.

Math is not my strong suit. Can I cope with this course?

This is something I feel really strongly about, so brace yourself for a long answer.

Algo trading is first and foremost a practical endeavour. Sure, there is probably a minimum level of cross-disciplinary knowledge that is needed, but contrary to popular belief you most definitely don’t need to be a mathematician or physicist to succeed at algo trading. In fact, the theoretical grounding that comes with such a background might even hinder progress rather than help it.

My personal approach to learning algo trading was empirical and experimental in nature. I tried everything I could think of in order to discover what was worth keeping and what to throw away. This was a fascinating path of discovery and really ignited my passion for algo trading. Later, I learned a bunch of theory about why things worked and others didn’t, but if I’d started with the theory I’d have probably lost interest before I even got started.

My point is that you really shouldn’t let your lack of theoretical background stand in your way. Algo trading is all about solving practical problems in the real world, and theory might not even be the best place to start looking for solutions. As you make real progress, you’ll probably become naturally interested in the theory, as I did.

When you’re starting out, rather than having knowledge of advanced mathematics, far more important is having a good, robust research process and the ability to apply the tools that help support it. This is exactly what the Robot Wealth courses provide . We’ll teach you how to operate a tool that enables efficient, useful and above all practical research. We’ll share with you a process for using that tool wisely. We’ll provide you with dozens of coded examples so that you can implement things straight away that are beyond your current coding abilities. If you start out with these, and work hard to understand the examples line by line, you’ll be well on your way in no time.

Of course, there is a place for theory, and you can always take a top-down approach and tackle it first. After all, that’s the way most things are taught at university. But following the empirical path, you tend to learn whatever theory you need to at the right time – and you get to simultaneously test it out in real life. Further, the theory tends to stick a lot better when you are actually using it in context. If you didn’t become proficient at mathematics through formal study, I’d be willing to bet that the empirical approach leads you to a level of proficiency that you never dreamed possible. I’m absolutely serious about this – you will surprise yourself.

In my experience, the bottom up, empirical approach is a lot more fun and yields faster results, so long as you’ve got the right systems and guidance in place. Being first and foremost a practical problem, algo trading really lends itself to this approach, so don’t let your perceived lack of mathematical ability stand in your way.

I don't have a scientific background. Can I still be an algo trader?

Unlike a background in advanced mathematics, a scientific approach to algo trading is critical. But a scientific approach is a skill that can be learned. It is also highly amenable to being encapsulated in a process or a workflow. We will teach you how to approach the markets from a scientific perspective and you’ll see the processes and workflows that worked for me.

The scientific approach is actually a very practical one. After all, science is all about experimentation, observation and inference. If you don’t come from this world, you just need to be shown how it’s done. We’ll introduce you to these concepts in the Fundamentals course, and they’ll be a constant theme throughout the Advanced course as well.

How much time and effort is this going to require?

I wish I could tell you that the path to mastery is one that is quickly and easily navigated. But that’s just not true. The reality is that algo trading is HARD. It is difficult because it requires both a broad and detailed knowledge base: ideally you would possess skills in programming, risk management, statistics, finance, markets, computer simulation and data management.

Learning enough about these different fields is not something that happens overnight. If you’re new to both programming and algo trading, you are looking at a minimum of six months of consistent effort (a couple of hours a day, most days) to become competent with all our course material. It may take significantly longer, depending on how much time you can devote to it. If you already have experience with algo trading, you can probably take a couple of months off that figure, again depending on how much time you can devote to the courses.

Also remember that getting through the courses will provide you with the knowledge and tools to develop your own trading algorithms in a scientifically robust manner. You still have to do the actual research and development to get your algorithms into production.

That sounds like a lot of time and effort, but consider that it took me several years to work this stuff out on my own. Here you have a repository of everything I would learn if I were starting out again.

Just to be clear, algo trading is not a get rich quick scheme. Yes the potential rewards are significant, but you will have to work very hard over a long period of time to attain them. As one old pro once told me, “trading is the hardest way to to make easy money.” You’ll need to put in a lot of work over at least a six month period (and probably longer) to attain the skills and knowledge required of successful algo traders.

When will I start making money?

Eu não sei. It really depends on you and how much work you put in. As mentioned in the previous question, algo trading is difficult and it takes time to accumulate the multi-disciplinary skills that are needed for success. Of course, the potential rewards are enormous, but they are not easily attained.

Having said that, if you want to get up and running with an algorithmic trading system quickly, you can always take something from our members’ library and start using it immediately. We provide code for a variant of Dual Momentum, a long-term trend following strategy requiring only monthly ETF rebalancing. It tends to be a good place to start for many (but I don’t know your circumstances and this is not advice) since it is simple to implement and manage and is backed by an extensive body of research. While such a slow-moving strategy is generally not what people look for when they come to algorithmic trading, starting off slow and simple is usually a good idea.

Dual Momentum won’t make you rich overnight, and who knows, it might stop working at some point in the future. In any event, you’ll give yourself the best chance of making consistent money when you have a process for developing algorithms and adding them to a well-diversified portfolio managed with sensible oversight. That’s what you’ll be able to do having completed the Robot Wealth algorithmic trading courses; how quickly you get there is completely up to you.

What support will I have while I'm doing the course?

You will be supported via the Members’ Forum, which is currently moderated by Kris. He strives to reply to questions directed to him within 48 hours. We also offer email support, but ask that this be reserved for non-technical queries. For everything else, the strong preference is for support to be via the forums or the comments section on individual course units. Someone else might have a similar question later, and answers preserved in the forum are beneficial for the entire community. This approach also saves Kris some time, which he can better spend contributing to the community’s collaborative research and development efforts.

What are the computer and software requirements? How much do they cost?

You will need to download the Zorro automated trading platform. You can complete the course using the free version, which enables nearly all the functionality of the commercial version, but in live trading has a cap on account size and annual profit. This is a nice aspect of the Zorro platform: the developers ask for nothing until you start making some money with it. You will also need a computer that runs Windows in order to use Zorro.

If you decide to purchase the licensed version of Zorro, Robot Wealth members get 15% of the purchase price refunded. Get in touch to find out more.

Towards the end of the Advanced course, we also use the R programming language for statistical computing. R is free and open source. We use and recommend the R Studio integrated development environment.

What on earth is this Zorro platform you refer to? I've never heard of it!

The Zorro automated trading platform might just be the best kept secret in the world of algorithmic trading. Zorro is a lot of things, including:

A professional grade backtesting engine that includes support for walk-forward analysis and parameter optimization An execution engine, able to trade with multiple brokers and asset classes, out of the box A suite of advanced (but simple to use) analytical tools, including statistics, artificial intelligence and signal processing.

I have personally designed and executed algorithms in a professional setting using Zorro, and I know that I am not the only one to do so.

With some guidance, it is extremely learnable for new coders thanks to the simple syntax of Lite-C and the huge library of pre-built functionality. Zorro makes doing serious algo research and trading about as simple as it can possibly get. Note the word serious – forget drag and drop software or any tool that promises you can do algo trading without coding. The fact is that you simply can’t, at least if you don’t want to waste your time. If you’re serious about algo trading, you simply have to bite the bullet and learn to code. Zorro makes that about as painless as it could possibly be, without sacrificing performance, power and flexibility. Zorro’s simple scripting language combined with the abstraction of the vast majority of the minutiae associated with algo trading code (MQL coders – you know what I’m talking about) results in a platform that truly facilitates fast and efficient research and prototyping of trading strategies. Once proficient, you will spend 90% of your time testing ideas for trading strategies, rather than writing and debugging code. For beginners, this may not sound all that exciting, but I can assure you that it is a huge win. It wouldn’t be unreasonable to say that developing in MQL means you spend 90% of your time writing and debugging code and only 10% of your time testing ideas. Guess which option leads to faster, better results. Zorro compiles your scripts to machine code and uses a flat file structure for historical data stored in binary format. This makes Zorro backtests super fast – 10x faster than MQL, 100x faster than Python and 400x faster than R on benchmark code. The combination of fast prototyping and lightning backtests leads to more efficient research. Most individual traders are forced to use what little spare time they have for trading, therefore research efficiency is crucial for sticking with it long term, and ultimately for success. Zorro backtests are accurate. This is a big deal, as the markets are notoriously difficult to simulate with high fidelity since you have variable transaction costs and execution dynamics to contend with. Most commercial backtesters I’ve used tend to deliver optimistic backtests, which is great for getting you excited about a trading idea, but really poor for hanging onto your money. Zorro goes from research environment to execution engine with the flick of a switch. This is incredibly handy because it means that your research code can be used directly for execution. This saves a lot of time and potential errors. Out of the box, Zorro has support for trading with Interactive Brokers, Oanda, Dukascopy, FXCM and any broker offering the MetaTrader 4 platform. Plugins can be written to trade with any broker. Zorro is highly extensible and customizable. While it is a very advanced piece of software out of the box, it is easily extended to access the library of R packages (over 13,000 at last count) and control other windows processes and leverage external DLLs. Zorro can download data from various sources and store them as efficient binary files in your own data library.

Do you have any examples of what Zorro can do?

Eu faço! Check out this video of me coding a simple, momentum-based trading strategy in Zorro. The video demonstrates Zorro’s optimization, walk-forward analysis, and portfolio trading tools – which are really just scratching the surface of what Zorro is capable of, but provides a nice introduction.

If Zorro is so awesome, why aren't more people using it?

At the time of writing, Zorro’s user base was relatively small, but growing at an accelerating rate.

I am a massive fan of what the developers of Zorro have built (and continue to build – they roll out new features every few months), but it is pretty clear to me that they are genius software engineers first and foremost. With all the respect in the world, I speculate that judging by their website, they aren’t the world’s best marketers or salespeople (says the guy with the electric green website). A lot of people would never get past that first page and download the software, although Johan’s (the lead developer) blog is certainly getting the word out that the software is the real deal.

The other obstacle is that the software isn’t particularly well documented. Sure, the manual describes all the functionality, but important things are hidden away in remarks or comments sections and are almost sure to be overlooked. This is not meant to be critical – I’m actually really glad that the documentation is what it is as I’d rather the developers spend their time working on new features than filling the manual with perfectly written technical prose.

This is not really an issue for verteran programmers looking to pick up Zorro, but the downside however is that there is a siginificant barrier to proficiency for non-coders. My belief is that it is not feasible for non-programmers to pick up the software, learn to write code, learn how to apply the advanced functionlaity of Zorro using that code, as simple as that code is, in a reasonable amount of time. I think many non-coders would walk away in frustration.

That’s where Robot Wealth can really help – our courses start off by approaching Zorro form the absolute beginner’s perspective, using it as a vehicle to teach programming rather than assuming that programming is already known. With a focus on the practical, we teach you firstly how to operate the software and then how to use it wisely through extensive code examples that are broken down and fully explained.

What data do you use in the course? Do you supply data?

The data that we use in the course is all available for free with the Zorro platform. We also show you how to use Zorro and other software to build your own library of data from freely available and paid sources. We will be rolling out a shared data platform for members, subject to licensing conditions and constraints imposed by the providers.

What programming languages do you teach?

The courses focus on Lite-C and R.

Lite-C is a powerful, simple, C-based language that enables rapid development, efficient research and fast backtesting.

R is a hugely popular and widely used language for statistical computing, data science and visualization. While the syntax of R certainly has it’s own unique nuances, its library of over 13,000 packages give it an enormous amount of power. Thanks to these packages, we can readily use advanced tools from the fields of statistics, econometrics, data science, artificial intelligence and many others without having to code them from scratch.

Lite-C and R make a formidable partnership: we can rapidly prototype and debug trading algorithms using the former, then call advanced R functions directly in our Lite-C script.

What have you got against Python?

Absolutely nothing! I love Python. I use it on an almost daily basis, especially for standalone machine learning or artificial intelligence work. Amongst general purpose programming languages, Python almost has to be the first choice.

The Lite-C + R combination we use in the courses is just a really simple and easy way to get almost a complete algo trading technology stack out of the box: an accurate and fast backtester, an efficient research environment, advanced statistical tools and a trade execution engine. We could get all of this and more with Python, but it would take a lot longer to set up and ongoing effort to maintain.

When it comes to programming languages, I am agnostic for the most part. I take a pragmatic approach by using the best tool for the given task. In this case, the task is to set up an algo trading technology stack that is accessible to beginners and experts alike and which allows the trader to focus on strategy research and development by taking care of as much of the painful detail as possible. The Lite-C and R combination accomplishes this with minimal fuss, but a similar stack could be set up using Python as well.

While Lite-C and R are the programming languages of our courses, the courses are really about an approach to algo trading rather than using a specific programming tool. Everything in the courses can be implemented in Python, and other programming languages as well for that matter.

What's the deal with this community of algo traders you refer to?

Robot Wealth provides individual DIY traders with the tools and resources that professionals have access to. One of those resources is a group of other traders, developers, and risk managers of diverse backgrounds and complementary skillsets. Professional traders have the luxury of being able to be a specialist in one or two of the skills needed for successful algo trading, since they can work in teams that together cover all the bases. Individual traders don’t really have that luxury – unless they are part of a wider community.

The power of the Robot Wealth community is that through knowledge sharing and the combination of various skills and experience, we can achieve a lot more together than any of us could individually. We all come from different backgrounds, and at the time of writing, we counted everyone from complete beginners to ex bank and hedge fund traders as our members. Imagine the power of such a diverse community of motivated and driven individuals, all armed with a baseline level of knowledge from the Robot Wealth courses, working together to build trading systems.

Pretty exciting, isn’t it?

In practical terms, we communicate via an online forum and the messaging application Slack. The forums are repositories of knowledge that we wish to preserve for the benefit of the entire community. Slack is more used for informal discussion and trading system collaboration. We are always on the lookout for new members who are keen and willing to either simply be involved or to even lead the development of a particular trading strategy or idea.

You mentioned that Experienced and Professional members get access to a library of research projects. Tell me more about that.

As part of the Advanced Algorithmic Trading course, students have the opportunity to put what they’ve learned into practice. Specifically, they can choose any trading idea or approach to the markets and, following a sound methodology like we teach in the course, undertake a research project that explores the potential to turn that idea into a trading strategy.

We examine each research project and provide detailed, personalized feedback. This is a great way to really round out the Advanced course and to help the student put the skills they’ve learned into practice. I don’t know of any other service that provides personalized, tailored feedback to individuals that doesn’t charge thousands of dollars for the privilege.

Once a student’s submission has been deemed to be of a professional level (sometimes it can take one or two iterations based on the feedback we provide, but this is a great opportunity to learn), the submission is added to the repository of research projects.

Any student who has contributed a research project gets immediate access to the entire repository of research projects.

What if my research project doesn't turn into a viable trading strategy?

That’s totally fine! The point of the research project is not necessarily to create a profitable strategy; it’s to help you put the skills you learned into practice, and refine them even further.

Some research projects will turn into viable trading strategies, but many won’t. That’s a fact of life when it comes to systematic trading research.

But here’s the thing. Even the research projects that didn’t work out are still an incredibly powerful repository of knowledge , and really highlight the power of working with a community. That’s because normally independent traders need to investigate dozens of trading ideas themselves before they find success. Traders the world over are researching and discarding similar ideas. That’s a huge waste of resources. Thanks to the library of research projects, we leverage the power of community to reduce the amount of work each individual has to undertake.

Think of it like amplifying your research effort: submit one research project, get access to dozens.

The other nice by-product of the repository is that it provides a library of code that you can use to find solutions to coding problems that have you stumped. You also might take someone’s research and put a slightly different spin on it to generate a profitable strategy. It’s amazing how much inspiration you can find by looking at the work of others in a slightly different way.

What happens if all the Robot Wealth members start trading the same strategy?

We are unlikely to significantly degrade a strategy given the volumes that we would be trading as a group of individuals and the liquidity of the markets we would be focusing on. If this ever looked like becoming a problem, we would be able to finesse the execution of each individual’s copy of the algorithm, or target more liquid markets. I don’t see this becoming a problem for a while though.

Do you recommend any particular broker?

Na verdade não. I personally trade with Interactive Brokers and Oanda, both of whom I am happy with and maintain good relationships with.

Can you be my mentor?

Honestly, I would love to work one on one with you, show you the ropes and help you get to where you want to be. The reality is however, that I just don’t have the time. If my story resonates with you, the best way to work with me is as a part of the Robot Wealth community. That way, I can share what I’ve learned with as many people as care to learn about it.

Can you consult on my project?

I would love to work with you, and it pains me to turn down the opportunity to work with the driven and intelligent people that make up the DIY trading community, but I have had to stop consulting to individuals due to time constraints. If you’re an individual, the best input you can get into your project is via our community. If you post there, I’ll end up helping out out anyway, without charging you a hefty consulting fee. You will get a lot from the community in return for sharing your project, far beyond assistance with your project.

If you’re a trading firm or a financial institution, I can best help you out via my consulting firm Quantify Partners.

Can you help me out with my project?

Not on a one-on-one basis, but certainly via our community.

Can I collaborate with you?

Unfortunately I’ve had to start saying no to collaboration offers. For a long time I tried to say yes to everyone and ended up just spreading myself so thinly that I wasn’t helping anyone, including myself. If you’d like to collaborate, consider joining our community where apart from me, you’ll find a driven, intelligent and passionate group of likeminded individuals who would love to welcome you into their ranks.

What is your background?

I’ve got a couple of engineering degrees and I worked in that profession for over a decade. I spent most of my time in the resources industry where I worked on computer simulations of environmental processes, particularly hydrology. The highlight was working on a whole-of-life data project that involved constructing networks of environmental monitoring stations in the Australian outback, and then using the collected data to empirically model groundwater dynamics – which doesn’t sound all that interesting, but was actually quite a departure from the generally accepted contemporary methods. It was a lot of fun and I learned plenty.

I enjoyed the resources industry because it really epitomizes the concepts of practical design: you end up engineering things on the run to be ‘fit for purpose’, making do with potentially less equipment and materials than you’d prefer in some fairly remote and challenging environments. A lot of problems are solved through gathering data, experimentation and testing – not unlike algo trading.

Towards the end of my engineering career, I became really interested in artificial intelligence. I was lucky enough to work on problems like building machine learning algorithms capable of identifying endangered species habitat from high-resolution LiDAR data.

In parallel with my engineering career, I was also fascinated with the markets, particularly algo trading. For several years, I spent most of my spare time learning all I could about algo trading, conducting research and writing trading algorithms. This was a long process and even now I look back and shudder at the amount of time I spent pursuing dead ends and false leads (helping you avoid this is one reason for Robot Wealth’s existence, by the way).

Through sheer perseverance, I eventually started getting somewhere with algo trading. I wrote a couple of algos that did really well and started quietly sharing the results with friends of friends and other tenuous connections in the finance industry. This attracted the attention of some high net worth investors, who backed my approach and funded me. This was my first real break, and as things progressed, I wound up being offered a fairly senior role within a hedge fund. The fund was fairly traditional in its approach, but recognized the benefit of automation and more advanced analytics. I was brought in to automate the fund’s existing strategies and to find new sources of alpha through machine learning and big data.

In the beginning, working at the fund was something of a dream come true. I couldn’t believe that I was really here in this world of high finance, being paid to research and trade – the very things I’d freely given all my spare time to for years! Upon entering that world, I assumed that everyone who inhabits it would know the things I know and would be able to do the things that I could do. But it quickly became apparent that this is simply not the case. Sure, there are some firms out there that are literally on the bleeding edge of the latest technologies, like artificial intelligence and even quantum computing. But there are also a lot of firms out there who don’t have a handle on the brave new world of big data and machine learning, and who are at grave risk of becoming irrelevant as a result. It seemed to me that the best informed and brightest fund managers were actively seeking to learn about this new world.

The entrepreneur in me recognized a huge opportunity to provide consulting services to these firms, and working long hours and commuting to the office every day was starting to wear thin (I’ve never been very good at spending time in offices) so I set up a consulting company, Quantify Partners. We helped out trading firms and fund managers who either have a unique and complex data set they want to better understand or even monetize, or who simply want help building capacity in artificial intelligence, quantitative analytics and big data.

I was lucky enough to consult to some of the bigger fund managers in the Asia-Pacific region, but I was even more fortunate to be engaged by a small, tech-focused proprietary trading firm to get them up and running with AI. We hit it off so well that I accepted an offer to join them on a full-time basis as a partner and shareholder to drive the adoption of machine learning and AI across the business. The rest, as they say, is history.

You can read more about my journey from engineer to hedge fund quant to industry consultant and back to professional trader on Robot Wealth’s About page. People tell me its an interesting story, so check it out if you’re curious.

Why are you doing this? If you know so much about algo trading, why not just sit back and get obscenely rich off the profits?

I love this question.

There are so many reasons why algo trading doesn’t work this way that it is hard to know where to start. Ignoring the fact that you don’t just ‘sit back’ and let the algos do their thing (proper oversight can be like a full time job, depending on your trade frequency, and there will always be bugs and infrastructure issues to stay on top of), I don’t want trading to be the sum total of what I do. I love teaching, which provides an enormous amount of personal satisfaction that I simply don’t get from trading alone. Even more than that, my early experiences of learning algo trading in a vacuum really instilled in me a deep belief in the power of community and knowledge sharing.

Community and knowledge sharing is ultimately about empowerment, and financial empowerment is a big deal in my home country, Australia, and most places around the world. We have a superannuation (retirement fund) industry that is worth billions in annual management fees, yet many funds consistently return less than the local benchmark. We pay billions in fees for this sort of performance and retirees depend on the returns for their livelihoods. A huge part of Australia’s wealth is tied up in this industry and it really disturbs me to see the retirement funds of people who worked hard all their lives needlessly dwindle away. Middle class wage growth has been stagnant for decades while the cost of living continues to go up, so in the future our standard of living as a society is going to be tied more than ever to the performance of retirement funds.

I would love to empower people to take responsibility for their own participation in the markets. After all, no one is going to look after your money as diligently as you!

Further, whenever I share some part of my approach, I invariably get something valuable in return. Sometimes it is as simple as understanding on a deeper level through articulating the concepts. Sometimes it is refinement through feedback, suggestions and ideas that would never have occurred to me. Other times it is simply a new connection with a like-minded person. Very rarely have I regretted sharing what I’ve learned with another person.

I don’t want to give the impression that I want to be the Robin Hood of the finance world. I don’t. I absolutely enjoy making money as much as the next person. I also enjoy the challenges of working in the institutional space, which are different to the DIY space. But the money is only one part of the whole story and would be kind of boring and unfulfilling on its own. I’d also love to see more DIY traders meeting their objectives, as well as a more efficient superannuation industry that returned more money to investors’ pockets, leading to a little more freedom, a little more comfort and a higher standard of living. Those are things that make for a nicer society. And of course I’d like to have some fun and meet some interesting people along the way.

Negociação de reversão à média nos mercados futuros de energia.

Destaques.

Estudamos se a negociação técnica simples pode ser empregada de forma lucrativa para futuros de energia.

Estratégias com spreads de calendário com reversão à média com taxas de hedge dinâmicas são testadas.

Vinte e dois anos de dados históricos são testados com custos de transação e bootstrap.

Sinais de entrada e saída são gerados por Bollinger Bands.

Os melhores resultados são obtidos para Petróleo Bruto e Gás Natural.

Estudamos se estratégias comerciais simples que gozam de grande popularidade entre os profissionais podem ser empregadas de forma lucrativa no contexto de carteiras de hedge para futuros de petróleo bruto, gás natural, gasolina e óleo de aquecimento. As estratégias testadas baseiam-se em carteiras de spread de calendário com reversão da média estabelecidas com taxas de hedge dinâmicas. Os sinais de entrada e saída são gerados pelos chamados Bollinger Bands. O sistema de negociação é aplicado a vinte e dois anos de dados históricos de 1992 a 2013 para várias especificações, levando em conta os custos de transação. A significância dos resultados é avaliada com um teste de bootstrap no qual os pedidos gerados aleatoriamente são comparados com pedidos feitos pelo sistema de negociação. Considerando que encontramos a maioria das combinações envolvendo os futuros frente mês e segundo mês para ser significativamente lucrativa para todas as commodities testadas, os melhores resultados para o Índice de Sharpe ajustado ao risco são obtidos para WTI Crude Oil and Natural Gas, com Sharpe Ratios acima de 2 para a maioria das combinações e um desempenho bastante suave para todos os spreads do calendário. Com base em nossos resultados, há uma séria dúvida se os mercados futuros de energia podem ser considerados fracamente eficientes no curto prazo.

Classificação JEL.

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Inteligência Computacional e Mercados Financeiros: Uma Pesquisa e Direções Futuras.

Destaques.

Propomos um levantamento das técnicas de soft computing aplicadas ao mercado financeiro.

Pesquisamos vários estudos primários propostos na literatura.

Uma estrutura para a construção de um sistema de negociação inteligente foi proposta.

Direções futuras deste campo de pesquisa são discutidas.

Os mercados financeiros desempenham um papel importante na organização econômica e social da sociedade moderna. Nesse tipo de mercado, a informação é um bem inestimável. No entanto, com a modernização das transações financeiras e dos sistemas de informação, a grande quantidade de informações disponíveis para um profissional pode tornar proibitiva a análise de um ativo financeiro. Nas últimas décadas, muitos pesquisadores tentaram desenvolver métodos e algoritmos inteligentes computacionais para apoiar a tomada de decisões em diferentes segmentos do mercado financeiro. Na literatura, há um grande número de artigos científicos que investigam o uso de técnicas de inteligência computacional para resolver problemas do mercado financeiro. No entanto, poucos estudos se concentraram em revisar a literatura sobre esse assunto. A maioria dos artigos de revisão existentes tem um escopo limitado, seja concentrando-se em uma aplicação específica do mercado financeiro ou concentrando-se em uma família de algoritmos de aprendizado de máquina. Este artigo apresenta uma revisão da aplicação de vários métodos inteligentes computacionais em diversas aplicações financeiras. Este artigo fornece uma visão geral dos estudos primários mais importantes publicados de 2009 a 2015, que abrangem técnicas de pré-processamento e agrupamento de dados financeiros, para previsão de movimentos futuros do mercado, para mineração de informações financeiras textuais, entre outros. As principais contribuições deste artigo são: (i) uma revisão abrangente da literatura deste campo, (ii) a definição de um procedimento sistemático para orientar a tarefa de construir um sistema de negociação inteligente e (iii) uma discussão sobre os principais desafios e problemas abertos neste campo científico.

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