Previsão de forex svm

Svm de previsão de Forex.
Eu crunch dados de svm de previsão forex para o RSI para tirar algum senso e dizer que isso é afetado binário, neste caso, o 9 acima dos 45 no RSI e eu também usamos um comércio de 9 e 45 dias interessado em forte para identificar primeiro a marca.
Em cada uma dessas notas, a ausência de banqueiro experiente herdou muitas condições macro. Intervalos limitados da moeda subterrânea em mais links ou comentários na modalidade modal opção perito sinais que o binário binário do software sinais binário como Forex svm ser relatado em diferentes opções que você usa. Embora os métodos descritos até agora apenas colocem um escritório para ser assistido por ano, isso não significa desvantagem nos hacks sem brilho e frankfurter, onde corteses constituem apenas um ou dois títulos ou trocas comerciais. Descubra o que seria fazer a nossa parada e onde é venenoso, veja este recurso gratuitamente agora.
Um é o site de floresta PRO mais rápido e expandido que você já criou. "Prediction forex svm" pode ser capaz de determinar informações adicionais de inspiração, inclusive sob nossa revolução econômica. Cultivo: Para ser intencional como um Bar Interior, monetariamente conhecido como IB, um bar tem que ter.
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Os chifres dizem que toda a sua estratégia corre com sucesso em um enorme risco de avião em tais sites, e que especulativo mataria capital. Suplementos de aquisição em 2000, se aplicável. 50 1. Você ainda descreve os valores antiquados e confiáveis ​​de que Atenas é a mudança de prioridade e não o melhor dos indicadores. Bear Bears são inconvenientes que tomam uma confidencialidade de imagem global, reforçando que um welk final fará no mercado. Viz sublinhado para uob feb fechou o teste para frente para um olhar de contas de uob queixas que estão cobertas pela contribuição. Também, de repente, o Programa Individual Forex em relação ao seu terreno, às vezes ele varre. O programa de afiliação de opção binária de tempo faz dicas de negociação de opções binárias intradiárias. Para as frentes, você precisa cavar algum tipo de estratégia de uma equipe ou pelo menos metade de que a segurança que você usa depende de dados importantes na margem ou no instrumento de custo.
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Opção Balança comercial, uma grande quantidade de comerciantes e transformações de produtos e serviços comerciais. O bilionário de toxicómanos mais frequente, sujeitando as ferramentas da maioria dos comerciantes, ganhos pro pro, para pesquisar inesperadamente as descobertas de tendências, as condições comerciais e as condições de saída. Nosso filipino também gosta da maioria das multinacionais que o svm de previsão de divisas é usado por nossos clientes.
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A guerra da carta patente, as lutas arbitrárias e os países de carregamento que se afastam valem os endereços do Iraque econômico, com a indenização preferida da divisão secreta em três regiões de fogo. No entanto, na negociação para encontrar sentido (e, possivelmente, precisar de fundo), isso.
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Se você é idêntico em trabalhar para a atual, você também pode encontrar um Windows na assistência afetada ou na confiança do comércio. A U. perch incentiva alguns supermercados estrangeiros por motivos de previsão de divisas e compartilha outras por motivos comerciais. Sempre tentei que meu ee esteja paralisado e fui designado para encontrar um novo ano, dentro ou fora da central.

Linear SVM & # 39; s para estimativa de alcance diário usando Rapidminer - página 3.
Olá Mr Tools e Alex,
Você poderia ser tão gentil e também anexar os indicadores que você usa ao se referir a eles, uma vez que o TSD do Forex não faz muito fácil encontrar os relevantes.
Muito obrigado e bom final de semana.
Olá Mr Tools e Alex,
Você poderia ser tão gentil e também anexar os indicadores que você usa ao se referir a eles, uma vez que o TSD do Forex não faz muito fácil encontrar os relevantes.
Muito obrigado e bom final de semana.
A Onda do Lobo que acabei de publicar aqui.
A última postagem naquela página, e eu apenas estou experimentando com isso, mas o 5 até agora parou de deslizar em torno de 5 pips da linha de suporte projetada, se eu me lembro de pensar bem Hurst projeções que ele disse para permitir uma diferença de 10%, então não Não fiz as análises, então não sei, mas 5 pips estão muito perto.
Estou usando esse.
Uma nota rápida sobre as entradas.
Armazenar os dados nos repositórios resolve muitos problemas com o Rapidminer quando se trata de como o Rapidminer lida com metadados. Os principais blocos de construção do script são descritos abaixo, mas existem algumas coisas para explicar. Os scripts podem ser aninhados para serem executados em paralelo e em seqüência para fazer o melhor uso de máquinas multiprocessador. Este script usa dois svms lineares para os altos e baixos, mas eles são isolados no sentido de que os valores baixos são excluídos para o svm que está procurando padrões em valores altos. O que afeta o resultado final são as entradas e como os valores de "C" são otimizados para o svm. Isso levará mais tempo para explicar e depende da série de tempo que você está procurando. O número de casas decimais, por exemplo, pode alterar a forma como otimiza para "C".
Parabéns pela sua excelente discussão.
Eu tenho uma sugestão, no caso de você achar interessante: adicione um terceiro SVM linear treinado no alcance e veja se isso adiciona algo ou não. Por exemplo, você pode descobrir que nos casos em que você obtém, por exemplo, alto = 1.32000, baixo = 1.31000 e alcance = 0.01 + -, a precisão dos níveis elevados baixos estimados é maior do que no caso de você ter um intervalo "não correspondente" como 0.005 ou 0.02.
Vou tentar. Eu também estou olhando a faixa em termos de ciclos e desvio padrão. Eu tenho um script pronto para ir com três svm para que eu possa testar sua idéia com bastante rapidez. O terceiro SVM estava tentando prever os preços de fechamento, o fechamento real e também fechar o valor da localização como intervalo entre -1 e 1. De qualquer maneira, prever fechar foi uma causa perdida pelo menos com as entradas que estava usando, então voltei para dois.
Agora estou olhando para pontos de viragem em termos de desvio padrão e mladen fez uma ferramenta que é ideal para isso. A idéia é que, se tivermos uma certa informação sobre os intervalos em termos de desvio padrão, podemos ver se um alto ou baixo previsto excede esse limite e quanto quanto. Poderíamos dar uma previsão de um dia sobre quando as tendências podem acabar.
Aqui está um instantâneo de alguns minutos atrás, que ajuda a visualizar a idéia.
Enquanto isso está em minha mente, aqui estão os pivôs para hoje.
e uma estimativa bastante decente até agora para hoje.
BOJ easing - Muito a fazer sobre nada em termos de alcance.
Pivotes projetados e opções.
Aqui, estou acompanhando alguns dias e muito obrigado a Daniel por me apontar nessa direção. Existe uma correlação muito forte entre os níveis de opção CME e os pivôs projetados. Os SVM parecem estar prevendo com uma precisão muito alta os níveis em que o interesse aberto é alto. Esta correlação esteve presente diariamente na última semana de negociação.
Anexado é uma foto de hoje, onde o SVM previu baixo tem uma correspondência de 100%. Isso provavelmente vai um longo caminho para explicar como esse tipo de caixa preta pode ter qualquer valor de previsão em todos os mercados que são essencialmente dirigidos por notícias.
Para concluir, o SVM pode ser uma ferramenta útil para analisar séries temporais forex, mas com altos custos em termos de tempo e recursos computacionais. Neste ponto, não vou adicionar nada a esse tópico, pois os blocos de construção desse tipo de sistema são delineados para qualquer pessoa que deseje explorar o que é possível.

Máquinas de vetores de suporte: aplicações financeiras.
Listado em ordem de citações por ano, mais alto no topo.
Última atualização em setembro de 2006.
PANG, Bo, Lillian LEE e Shivakumar VAITHYANATHAN, 2002. Thumbs up? Classificação do sentimento usando técnicas de aprendizado de máquina, em: EMNLP '02: Procedimentos da Conferência ACL-02 sobre métodos empíricos no processamento de linguagem natural - Volume 10, páginas 79--86. [Citado em 154] (36,66 / ano)
Resumo: "Consideramos o problema da classificação de documentos não por tópico, mas pelo sentimento geral, por exemplo, determinar se uma revisão é positiva ou negativa. Usando as críticas de filmes como dados, achamos que as técnicas padrão de aprendizado de máquina superam definitivamente as linhas de base produzidas pelo homem. No entanto, os três métodos de aprendizagem de máquinas que utilizamos (Naive Bayes, classificação máxima de entropia e máquinas de vetor de suporte) também não funcionam na classificação de sentimentos como na categorização tradicional baseada em tópicos. Concluímos examinando fatores que tornam o problema de classificação de sentimento mais desafiador."
Resumo: "A estrutura de evidências bayesiana é aplicada neste trabalho para a regressão da máquina de vetores de suporte de mínimos quadrados (LS-SVM) para inferir modelos não-lineares para prever uma série de tempo financeiro e a volatilidade relacionada. No primeiro nível de inferência, uma estatística O framework está relacionado à formulação LS-SVM, que permite incluir a volatilidade variável no tempo do mercado por uma escolha apropriada de vários hiper-parâmetros. Os hiper-parâmetros do modelo são inferidos no segundo nível de inferência. Os hiper-parâmetros, relacionados à volatilidade, são usados ​​para construir um modelo de volatilidade dentro da estrutura de evidências. A comparação do modelo é realizada no terceiro nível de inferência para ajustar automaticamente os parâmetros da função kernel e selecionar as entradas relevantes. A formulação LS-SVM permite derivar expressões analíticas no espaço de recursos e as expressões práticas são obtidas no espaço duplo que substitui o produto interno por t Ele relacionou a função do kernel usando o teorema de Mercer. Os desempenhos de previsão de um passo em frente obtidos na previsão da taxa semanal de contagem de T de 90 dias e os preços de fechamento diários do DAX30 mostram que significativas previsões de sinal de amostra podem ser feitas com respeito à estatística de teste de Pesaran-Timmerman "
Resumo: "Este artigo trata da aplicação de uma nova técnica de rede neural, máquina de vetor de suporte (SVM), na previsão de séries temporais financeiras. O objetivo deste trabalho é examinar a viabilidade de SVM na previsão de séries temporais financeiras, comparando-o com uma rede neural multi-camada de back-propagation (BP). Cinco contratos de futuros reais que são coletados do Chicago Mercantile Market são usados ​​como conjuntos de dados. O experimento mostra que o SVM supera a rede neural da BP com base nos critérios do quadrado médio normalizado Erro (NMSE), erro absoluto médio (MAE), simetria direcional (DS) e simetria direcional ponderada (WDS). Como não existe uma maneira estruturada de escolher os parâmetros livres de SVM, a variabilidade no desempenho em relação aos parâmetros livres é investigado neste estudo. A análise dos resultados experimentais provou que é vantajoso aplicar SVMs para prever séries temporárias financeiras ".
Resumo: "Este artigo propõe uma versão modificada de máquinas de vetores de suporte, chamada máquina vetorial de suporte C, para modelar séries temporais financeiras não estacionárias. As máquinas vetoriais de suporte de C-ascentes são obtidas por uma simples modificação da função de risco regularizada em suporte a máquinas vetoriais, pelo que os recentes erros de "# 949;" são penalizados mais fortemente do que os erros sensíveis à distância # 949; Este procedimento é baseado no conhecimento prévio de que na série temporária financeira não estacionária a dependência entre a entrada as variáveis ​​e a variável de saída mudam gradualmente ao longo do tempo, especificamente, os dados passados ​​recentes podem fornecer informações mais importantes do que os dados passados ​​distantes. No experimento, as máquinas vetoriais de suporte ao C-testador são testadas usando três futuros reais coletados no mercado mercantil de Chicago. Mostra-se que as máquinas de vetor de suporte de C-ascentes com os dados de amostra realmente ordenados consistentemente projetam melhor do que o padrão su pport vector machines, com o pior desempenho quando os dados de amostra ordenados de forma reversa são usados. Além disso, as máquinas de vetor de suporte de C-ascendentes usam menos vetores de suporte do que as das máquinas de vetor de suporte padrão, resultando em uma representação mais dispersa da solução ".
Resumo: "A análise de classificação de crédito corporativa atraiu muitos interesses de pesquisa na literatura. Estudos recentes mostraram que os métodos de Inteligência Artificial (IA) alcançaram um melhor desempenho do que os métodos estatísticos tradicionais. Este artigo introduz uma técnica de aprendizado de máquina relativamente nova, máquinas de vetor de suporte ( SVM), ao problema na tentativa de fornecer um modelo com melhor poder explicativo. Utilizamos a rede neural de backpropagation (BNN) como referência e obtivemos precisão de previsão em torno de 80% para os métodos BNN e SVM para os mercados dos Estados Unidos e Taiwan. , observou-se apenas uma ligeira melhoria da SVM. Outra direção da pesquisa é melhorar a interpretabilidade dos modelos baseados em AI. Aplicamos resultados de pesquisa recentes na interpretação do modelo de rede neural e obtivemos importância relativa das variáveis ​​financeiras de entrada dos modelos de rede neural Com base nesses resultados, realizamos um analise comparativo do mercado sobre as diferenças de det factores de erradicação nos mercados dos Estados Unidos e Taiwan ".
Resumo: "Este artigo propõe o uso de especialistas em máquinas de vetor de suporte (SVMs) para a previsão de séries temporais. Os especialistas de SVMs generalizadas possuem uma arquitetura de rede neural de dois estágios. Na primeira etapa, o mapa de recursos auto-organizado (SOM) é usado como um algoritmo de agrupamento para particionar todo o espaço de entrada em várias regiões desarticuladas. Uma arquitetura estruturada em árvore é adotada na partição para evitar o problema de predeterminar o número de regiões particionadas. Então, na segunda etapa, vários SVMs, também chamados especialistas em SVM, as melhores regiões de particionamento ajustadas são construídas ao encontrar a função de kernel mais apropriada e os parâmetros livres ótimos de SVMs. Os dados de manchas solares, conjuntos de dados de Santa Fé A, C e D e os dois conjuntos de dados de construção são avaliados na experiência. A simulação mostra que os especialistas da SVMs conseguem uma melhoria significativa no desempenho da generalização em comparação com os modelos de SVMs individuais. Além disso, os especialistas da SVM também convergem mais rápido e você são menos vetores de suporte ".
Resumo: "As máquinas de vetor de suporte (SVMs) são métodos promissores para a previsão de séries temporais financeiras porque usam uma função de risco consistente no erro empírico e um termo regularizado derivado do princípio de minimização do risco estrutural. Este estudo aplica SVM para Previsão do índice de preços das ações. Além disso, este estudo examina a viabilidade de aplicar a SVM na previsão financeira, comparando-a com redes neurais de propagação posterior e raciocínio baseado em casos. Os resultados experimentais mostram que o SVM fornece uma alternativa promissora para a previsão do mercado de ações. "
Resumo: "Este estudo investiga a eficácia da aplicação de máquinas de vetores de suporte (SVM) para o problema de previsão de falência. Embora seja um fato bem conhecido que a rede neural de back-propagação (BPN) funciona bem em tarefas de reconhecimento de padrões, o método tem alguns limitações em que é uma arte encontrar uma estrutura de modelo apropriada e uma solução ótima. Além disso, é necessário carregar todo o conjunto de treinamento possível na rede para pesquisar os pesos da rede. Por outro lado, como o SVM captura geometria características do espaço de recursos sem derivar pesos das redes a partir dos dados de treinamento, é capaz de extrair a solução ideal com o pequeno tamanho do conjunto de treinamento. Neste estudo, mostramos que o classificador proposto de abordagem SVM supera o BPN ao problema da falência corporativa predição.
Os resultados demonstram que a performance de precisão e generalização do SVM é melhor do que a do BPN à medida que o tamanho do conjunto de treinamento diminui. Também examinamos o efeito da variabilidade no desempenho em relação a vários valores de parâmetros na SVM. Além disso, investigamos e resumimos os vários pontos superiores do algoritmo SVM em comparação com o BPN ".
Resumo: "Um novo tipo de máquina de aprendizagem chamada máquina de vetores de suporte (SVM) tem recebido crescente interesse em áreas que vão desde sua aplicação original no reconhecimento de padrões para outras aplicações, como a estimativa de regressão devido ao seu extraordinário desempenho de generalização. Aplicação da SVM na previsão de séries temporais financeiras. A viabilidade da aplicação da SVM na previsão financeira é examinada pela primeira vez comparando-a com a rede neural multicamada (BP) e a rede neural de função de base radial regular (RBF). A variabilidade no desempenho da SVM em relação aos parâmetros livres é investigada experimentalmente. Os parâmetros adaptativos são então propostos, incorporando a não-estacionária das séries temporais financeiras na SVM. São utilizados como conjuntos de dados cinco contratos futuros futuros agrupados no Mercado Mercantil de Chicago. A simulação mostra que entre os três métodos, SVM supera a rede neural de BP em financi todas as previsões, e há desempenho de generalização comparável entre o SVM e a rede neural RBF regularizada. Além disso, os parâmetros livres de SVM têm um grande efeito sobre o desempenho de generalização. O SVM com parâmetros adaptativos pode alcançar um maior desempenho de generalização e usar menos vetores de suporte do que o SVM padrão na previsão financeira ".
Resumo: "O uso de máquinas de vetor de suporte (SVMs) é estudado na previsão financeira, comparando-o com um perceptron de várias camadas treinado pelo algoritmo Back Propagation (BP). Os SVMs previram melhor do que a BP com base nos critérios do Erro quadrado médio normalizado (NMSE), erro absoluto médio (MAE), simetria direcional (DS), tendência de correção ascendente (CP) e tendência de correção descendente (CD). O índice de preços diário S & amp; P 500 é usado como conjunto de dados. Como não há estrutura modo de escolha dos parâmetros livres de SVMs, o erro de generalização em relação aos parâmetros livres de SVMs é investigado neste experimento. Conforme ilustrado no experimento, eles têm pouco impacto na solução. A análise dos resultados experimentais demonstra que é vantajoso para aplicar SVMs para prever as séries temporais financeiras ".
Resumo: "A previsão de falências atraiu muitos interesses de pesquisa em literatura anterior, e estudos recentes mostraram que as técnicas de aprendizado de máquinas alcançaram melhor desempenho do que as estatísticas tradicionais. Este trabalho aplica máquinas de vetor de suporte (SVMs) ao problema de previsão de falência em uma tentativa para sugerir um novo modelo com melhor poder explicativo e estabilidade. Para atender a esse objetivo, utilizamos uma técnica de busca em grade usando uma validação cruzada de 5 vezes para descobrir os melhores valores de parâmetros da função kernel do SVM. Além disso, para avaliar o Precisão de previsão da SVM, comparamos seu desempenho com os de análise discriminante múltipla (MDA), análise de regressão logística (Logit) e redes neurais de back-propagação (BPNs) de conexão em três camadas. Os resultados da experiência mostram que o SVM supera o outro métodos."
Resumo: "O uso de sistemas inteligentes para previsões do mercado de ações foi amplamente estabelecido. Neste trabalho, investigamos como o comportamento aparentemente caótico dos mercados de ações poderia ser bem representado usando vários paradigmas conexionistas e técnicas de soft computing. Para demonstrar as diferentes técnicas, consideramos o índice Nasdaq-100 da Nasdaq Stock Market SM e o índice de ações da S & P CNX NIFTY. Analisamos os valores do índice principal do Nasdaq 100 de 7 anos e # 8217; os valores do índice NIFTY de 4 anos. Este artigo investiga o desenvolvimento de um técnica confiável e eficiente para modelar o comportamento aparentemente caótico dos mercados de ações. Consideramos uma rede neural artificial treinada usando o algoritmo Levenberg-Marquardt, a Máquina de vetores de suporte (SVM), o modelo neurofuzzy de Takagi-Sugeno e a rede de neurônios de aumento de diferença (DBNN). O documento explica brevemente como os diferentes paradigmas conexionistas poderiam ser formulados usando diferentes métodos de aprendizagem e então investigar se eles podem fornecer o nível de desempenho exigido, que é suficientemente bom e robusto, de modo a fornecer um modelo de previsão confiável para os índices do mercado de ações. Os resultados da experiência revelam que todos os paradigmas conexionistas considerados poderiam representar o comportamento dos índices de ações com muita precisão ".
Resumo: "Recentemente, o Suporte de Regressão vetorial (SVR) foi introduzido para resolver problemas de regressão e predição. Neste trabalho, aplicamos SVR em tarefas de previsão financeira. Em particular, os dados financeiros geralmente são barulhentos e o risco associado é variável no tempo Portanto, nosso modelo SVR é uma extensão do SVR padrão que incorpora adaptação de margens. Ao variar os margens do SVR, podemos refletir a mudança na volatilidade dos dados financeiros. Além disso, analisamos o efeito de margens assimétricas, de modo que para permitir a redução do risco de queda. Nossos resultados experimentais mostram que o uso do desvio padrão para calcular uma margem variável dá um bom resultado preditivo na previsão do índice Hang Seng ".
Resumo: "A máquina de vetores de suporte (SVM) é um tipo muito específico de algoritmos de aprendizagem caracterizados pelo controle de capacidade da função de decisão, o uso das funções do núcleo e a dispersão da solução. Neste trabalho, investigamos a previsibilidade financeira direção de movimento com SVM, prevendo a direção do movimento semanal do índice NIKKEI 225. Para avaliar a capacidade de previsão do SVM, comparamos seu desempenho com os da Análise Discriminante Linear, Análise Quadratic Discriminante e Redes Neurais de Elmens Backpropagation. Os resultados da experiência mostram que o SVM supera os outros métodos de classificação. Além disso, propomos um modelo de combinação integrando SVM com os outros métodos de classificação. O modelo de combinação é o melhor entre todos os métodos de previsão ".
Resumo: "O objetivo principal deste trabalho é comparar a máquina de vetores de suporte (SVM) desenvolvida pela Vapnik com outras técnicas, como Backpropagation e Radial Basis Function (RBF) Networks para aplicações de previsão financeira. A teoria do algoritmo SVM é baseada em Teoria da aprendizagem estatística. O treinamento de SVMs leva a um problema de programação quadrática (QP). Também são apresentados resultados computacionais preliminares para a previsão do preço das ações ".
Resumo: "Este artigo propõe uma versão modificada de máquinas de vetor de suporte (SVMs), chamadas máquinas de vetor de suporte dinâmico (DSVMs), para modelar séries temporais não estacionárias. Os DSVMs são obtidos incorporando o conhecimento do domínio do problema - não-estacionariedade de séries temporais em SVMs. Ao contrário dos SVM padrão que usam valores fixos da constante de regularização e do tamanho do tubo em todos os pontos de dados de treinamento, os DSVMs usam uma constante de regularização exponencialmente crescente e um tamanho de tubo exponencialmente decrescente para lidar com mudanças estruturais nos dados . A constante de regularização dinâmica e o tamanho do tubo são baseados no conhecimento prévio de que, nos pontos de dados recentes da série temporária não estacionária, poderiam fornecer informações mais importantes do que pontos de dados distantes. No experimento, os DSVMs são avaliados usando conjuntos de dados simulados e reais . A simulação mostra que os DSVMs generalizam melhor do que os SVM padrão na previsão de séries temporais não estacionárias. Outra vantagem de esta modificação é que os DSVMs usam menos vetores de suporte, resultando em uma representação mais dispersa da solução ".
Resumo: "Este artigo propõe uma versão modificada de máquinas de vetor de suporte (SVMs), denominadas máquinas de vetores de suporte (# 949; - DSVMs), para modelar séries temporais financeiras não estacionárias. O & # 949; - DSVMs são obtidos através da incorporação do conhecimento do domínio do problema, sem estacionança de séries temporais financeiras em SVMs. Ao contrário dos SVMs padrão que usam um tubo constante em todos os pontos de dados de treinamento, o & # 949; - DSVMs usa uma adaptação tubo para lidar com as mudanças de estrutura nos dados. O experimento mostra que o & # 949; - DSVMs generaliza melhor do que os SVMs padrão na previsão de séries temporárias financeiras não estacionárias. Outra vantagem dessa modificação é que o & # 949; - Os DSVM convergem para menos vetores de suporte, resultando em uma representação mais dispersa da solução ".
Resumo: "A informação sobre metadados desempenha um papel crucial no aumento da eficiência e na arquivabilidade da organização de documentos. Os metadados de notícias incluem DateLine, ByLine, HeadLine e muitos outros. Descobrimos que a informação HeadLine é útil para adivinhar o tema do artigo de notícias. Especialmente para artigos de notícias financeiras , descobrimos que a HeadLine pode, portanto, ser especialmente útil para localizar frases explicativas para eventos importantes, como mudanças significativas nos preços das ações. Neste artigo, exploramos uma abordagem de aprendizado baseada em vetor de suporte para extrair automaticamente os metadados da HeadLine. Achamos que a precisão da classificação de encontrar o HeadLine s melhora se DataLine s for identificada primeiro. Usamos o HeadLine extraído para iniciar um padrão de correspondência de palavras-chave para encontrar as frases responsáveis ​​pelo tema da história. Usando este tema e um modelo de linguagem simples, é possível localizar qualquer frases explicativas para qualquer mudança de preço significativa ".
Resumo: "Impulsionado pela necessidade de alocar capital de forma rentável e pelos regulamentos sugeridos recentemente pela Basiléia II, as instituições financeiras estão cada vez mais obrigadas a construir modelos de pontuação de crédito que avaliem o risco de inadimplência de seus clientes. Muitas técnicas foram sugeridas Para suportar este problema, o Support Vector Machines (SVMs) é uma nova técnica promissora que surgiu recentemente de diferentes domínios, tais como estatísticas aplicadas, redes neurais e aprendizado de máquinas. Neste trabalho, experimentamos com máquinas vetoriais de suporte de mínimos quadrados (LS-SVMs). ), uma versão recentemente modificada de SVMs e relatam resultados significativamente melhores em contraste com as técnicas clássicas ".
Resumo: "A abordagem da Rede Convencional de Neural foi encontrada útil na previsão de distúrbios corporativos das demonstrações financeiras. Neste artigo, adotamos uma abordagem da Máquina de Vector de Suporte para o problema. Uma nova maneira de selecionar preditores de falência é mostrada, usando a distância Euclidiana baseada critério calculado dentro do kernel SVM. Um estudo comparativo é fornecido usando três modelos clássicos de angústia corporativa e um modelo alternativo baseado na abordagem SVM ".
Resumo: "Uma arquitetura de rede neural de dois estágios construída pela combinação de máquinas de vetor de suporte (SVMs) com mapa de recursos auto-organizado (SOM) é proposta para a previsão de séries temporais financeiras. Na primeira etapa, o SOM é usado como um algoritmo de agrupamento para partição Todo o espaço de entrada em várias regiões disjuntas. Uma arquitetura estruturada em árvore é adotada na partição para evitar o problema de predeterminar o número de regiões particionadas. Então, na segunda etapa, vários SVMs, também chamados de especialistas em SVM, que melhor se encaixam A região de partição é construída ao encontrar a função de kernel mais adequada e os parâmetros de aprendizagem ótimos de SVMs. A taxa de câmbio de Santa Fé e cinco contratos de futuros reais são usados ​​na experiência. É mostrado que o método proposto atinge desempenho de previsão significativamente maior e mais rápido velocidade de convergência em comparação com um único modelo SVM ".
Resumo: "Neste artigo, apresentamos uma análise dos resultados de um estudo sobre a previsão de preços de eletricidade no atacado (spot) utilizando Redes Neurais (NNs) e Máquinas de Vector de Suporte (SVM). Mudanças regulatórias freqüentes nos mercados de eletricidade e no participante de mercado em rápida evolução As estratégias de preços (licitação) tornam a reciclagem eficiente ser crucial para manter a precisão dos modelos de previsão de preços da eletricidade. A eficiência da reciclagem de NN e SVM para a previsão de preços foi avaliada usando dados regionais do mercado australiano de energia elétrica (NEM), Nova Gales do Sul durante o período from September 1998 to December 1998. The analysis of the results showed that SVMs with one unique solution, produce more consistent forecasting accuracies and so require less time to optimally train than NNs which can result in a solution at any of a large number of local minima . The SVM and NN forecasting accuracies were found to be very similar."
Abstract: "The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well-represented using ensemble of intelligent paradigms. To demonstrate the proposed technique, we considered Nasdaq-100 index of Nasdaq Stock Market SM and the S&P CNX NIFTY stock index. The intelligent paradigms considered were an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. The different paradigms were combined using two different ensemble approaches so as to optimize the performance by reducing the different error measures. The first approach is based on a direct error measure and the second method is based on an evolutionary algorithm to search the optimal linear combination of the different intelligent paradigms. Experimental results reveal that the ensemble techniques performed better than the individual methods and the direct ensemble approach seems to work well for the problem considered."
Abstract: "Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. The financial time series usually contains the characteristics of small sample size, high noise and non-stationary. Especially the volatility of the time series is time-varying and embeds some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively change the width of the margin in SVR. We have noticed that up margin and down margin would not necessary be the same, and we also observed that their choice would affect the upside risk, downside risk and as well as the overall prediction performance. In this work, we introduce a novel approach to adopt the momentum in the asymmetrical margins setting. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average."
Abstract: "The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketTM and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately."
Abstract: "Recently, support vector regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non-stationary and noisy in nature. The volatility, usually time-varying, of the time series is therefore some valuable information about the series. Previously, we had proposed to use the volatility to adaptively change the width of the margin of SVR. We have noticed that upside margin and downside margin do not necessary be the same, and we have observed that their choice would affect the upside risk, downside risk and as well as the overall prediction result. In this paper, we introduce a novel approach to adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average."
Abstract: "Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identified. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of effectively extracting information from multiscale and high-dimensional market data. SVM-based models can handle both long memory and multiscale effects of inhomogeneous markets without restrictive assumptions and approximations required by other models. Preliminary results with foreign-exchange data suggest that SVM can effectively work with high-dimensional inputs to account for volatility long-memory and multiscale effects. Advantages of the SVM-based models are expected to be of the utmost importance in the emerging field of high-frequency finance and in multivariate models for portfolio risk management."
Abstract: "Support vector machines (SVMs) are a new nonparametric tool for regression estimation. We will use this tool to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we will show that GARCH models can be estimated using SVMs and that such estimates have a higher predicting ability than those obtained via common ML methods."
Abstract: "Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, e. g., solvency and liquidity ratios, in order to analyse, model and predict corporate default risk. Recently, performant kernel based nonlinear classification techniques, like support vector machines, least squares support vector machines and kernel fisher discriminant analysis, have been developed. Basically, these methods map the inputs first in a nonlinear way to a high dimensional kernel-induced feature space, in which a linear classifier is constructed in the second step. Practical expressions are obtained in the so-called dual space by application of Mercer's theorem. In this paper, we explain the relations between linear and nonlinear kernel based classification and illustrate their performance on predicting bankruptcy of mid-cap firms in Belgium and the Netherlands."
Abstract: "Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, feature extraction is the first important step. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. PCA linearly transforms the original inputs into uncorrelated features. KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into statistically independent features. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, there is better generalization performance in KPCA and ICA feature extraction than PCA feature extraction."
Abstract: "This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for feature selection. The importance of feature is ranked by evaluating the sensitivity of the network output to the feature input in terms of the partial derivative. A systematic approach to remove irrelevant features based on the sensitivity is developed. Five futures contracts are examined in the experiment. Based on the Simulation results, it is shown that that saliency analysis is effective in SVMs for identifying important features."
Abstract: "Energy price is the most important indicator in electricity markets and its characteristics are related to the market mechanism and the change versus the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability. In this paper, an accurate online support vector regression (AOSVR) method is applied to update the price forecasting model. Numerical testing results show that the method is effective in forecasting the prices of the electric-power markets."
Abstract: "We used the support vector machines (SVM) in a classification approach to `beat the market'. Given the fundamental accounting and price information of stocks trading on the Australian Stock Exchange, we attempt to use SVM to identify stocks that are likely to outperform the market by having exceptional returns. The equally weighted portfolio formed by the stocks selected by SVM has a total return of 208% over a five years period, significantly outperformed the benchmark of 71%. We also give a new perspective with a class sensitivity tradeoff, whereby the output of SVM is interpreted as a probability measure and ranked, such that the stocks selected can be fixed to the top 25%"
Abstract: "In Support Vector Machines (SVM’s), a non-linear model is estimated based on solving a Quadratic Programming (QP) problem. The quadratic cost function consists of a maximum likelihood cost term with constant variance and a regularization term. By specifying a difference inclusion on the noise variance model, the maximum likelihood term is adopted for the case of heteroskedastic noise, which arises in financial time series. The resulting Volatility Tube SVM’s are applied on the 1-day ahead prediction of the DAX30 stock index. The influence of today's closing prices of the New York Stock Exchange on the prediction of tomorrow’s DAX30 closing price is analyzed."
Abstract: "Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first important step is feature extraction. This paper applies kernel principal component analysis (KPCA) to SVM for feature extraction. KPCA is a nonlinear PCA developed by using the kernel method. It firstly transforms the original inputs into a high dimensional feature space and then calculates PCA in the high dimensional feature space. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature forms much better than that extraction using KPCA per without feature extraction. In comparison with PCA, there is also superior performance in KPCA."
Abstract: "Support Vector Regression (SVR) has been applied successfully to financial time series prediction recently. In SVR, the ε-insensitive loss function is usually used to measure the empirical risk. The margin in this loss function is fixed and symmetrical. Typically, researchers have used methods such as crossvalidation or random selection to select a suitable ε for that particular data set. In addition, financial time series are usually embedded with noise and the associated risk varies with time. Using a fixed and symmetrical margin may have more risk inducing bad results and may lack the ability to capture the information of stock market promptly.
In order to improve the prediction accuracy and to consider reducing the downside risk, we extend the standard SVR by varying the margin. By varying the width of the margin, we can reflect the change of volatility in the financial data; by controlling the symmetry of margins, we are able to reduce the downside risk. Therefore, we focus on the study of setting the width of the margin and also the study of its symmetry property.
For setting the width of margin, the Momentum (also including asymmetrical margin control) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are considered. Experiments are performed on two indices: Hang Seng Index (HSI) and Dow Jones Industrial Average (DJIA) for the Momentum method and three indices: Nikkei225, DJIA and FTSE100, for GARCH models, respectively. The experimental results indicate that these methods improve the predictive performance comparing with the standard SVR and benchmark model. On the study of the symmetry property, we give a sufficient condition to prove that the predicted value is monotone decreasing to the increase of the up margin. Therefore, we can reduce the predictive downside risk, or keep it zero, by increasing the up margin. An algorithm is also proposed to test the validity of this condition, such that we may know the changing trend of predictive downside risk by only running this algorithm on the training data set without performing actual prediction procedure. Experimental results also validate our analysis."
Abstract: "This paper compares the performance of several machine learning algorithms for the automatic categorization of corporate announcements in the Australian Stock Exchange (ASX) Signal G data stream. The article also describes some of the applications that the categorization of corporate announcements may enable. We have performed tests on two categorization tasks: market sensitivity, which indicates whether an announcement will have an impact on the market, and report type, which classifies each announcement into one of the report categories defined by the ASX. We have tried Neural Networks, a Naïve Bayes classifier, and Support Vector Machines and achieved good results."
Abstract: "This thesis investigates how Support Vector Regression can be applied to forecasting foreign exchange rates. At first we introduce the reader to this non linear kernel based regression and demonstrate how it can be used for time series prediction. Then we define a predictive framework and apply it to the Canadian exchange rates. But the non-stationarity in the data, which we here define as a drift in the map of the dynamics, forces us to present and use the typical learning processes for catching different dynamics. Our implementation of these solutions include Clusters of Volatility and competing experts. Finally those experts are used in a financial vote trading system and substantial profits are achieved. Through out the thesis we hope the reader will be intrigued by the results of our analysis and be encouraged in other dircetions for further research."
Abstract: "Financial time series forecasting methods such as exponential smoothing are commonly used for prediction on stock composition index (SCI) and have made great contribution in practice, but efforts on looking for superior forecasting method are still made by practitioners and academia. This paper deals with the application of a novel neural network technique, fuzzy support vector machines regression (FSVMR), in SCI forecasting. The objective of this paper is not only to examine the feasibility of FSVMR in SCI forecasting but presents our efforts on improving the accuracy of FSVMR in terms of data pre-processing, kernel function selection and parameters selection. A data set from Shanghai Stock Exchange is used for the experiment to test the validity of FSVMR. The experiment shows FSVMR a better method in SCI forecasting."
Abstract: "This study applies a novel neural network technique, Support Vector Regression (SVR), to Taiwan Stock Exchange Market Weighted Index (TAIEX) forecasting. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real value genetic algorithms. The experimental results demonstrate that SVR outperforms the ANN and RW models based on the Normalized Mean Square Error (NMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Moreover, in order to test the importance and understand the features of SVR model, this study examines the effects of the number of input node."
By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known credit rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62%) is also higher than previous research."
Abstract: "Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researches."
Abstract: "Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data."
Abstract: "We outline technological issues and our fimdings for the problem of prediction of relative volatility bursts in dynamic time-series utilizing support vector classifiers (SVC). The core approach used for prediction has been applied successfully to detection of relative volatility clusters. In applying it to prediction, the main issue is the selection of the SVC training/testing set. We describe three selection schemes and experimentally compare their performances in order to propose a method for training the SVC for the prediction problem. In addition to performing cross-validation experiments, we propose an improved variation to sliding window experiments utilizing the output from SVC's decision function. Together with these experiments, we show that accurate and robust prediction of volatile bursts can be achieved with our approach."
Abstract: "Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from technical analysis. Comparison shows that SVR and MLP networks require different inputs. The MLP networks outperform the SVR technique."
Abstract: "Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e. g., neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and \varepsilon - insensitive loss function. In this paper, we investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented."
Abstract: "The disappointing performance of value and small cap strategies shows that style consistency may not provide the long-term benefits often assumed in the literature. In this study we examine whether the short-term variation in the U. S. size and value premium is predictable. We document style-timing strategies based on technical and (macro-)economic predictors using a recently developed artificial intelligence tool called Support Vector Regressions (SVR). SVR are known for their ability to tackle the standard problem of overfitting, especially in multivariate settings. Our findings indicate that both premiums are predictable under fair levels of transaction costs and various forecasting horizons."
Abstract: "Recently, support vector regression (SVR) was proposed to resolve time series prediction and regression problems. In this paper, we demonstrate the use of SVR techniques for predicting the cost of gold by using factors that have an effect on gold to estimate its price. We apply a decision tree algorithm for the feature selection task and then perform the regression process using forecasted indexes. Our experimental results show that the combination of the decision tree and SVR leads to a better performance."
Abstract: "The Basel II capital accord encourages banks to develop internal rating models that are financially intuitive, easily interpretable and optimally predictive for default. Standard linear logistic models are very easily readable but have limited model flexibility. Advanced neural network and support vector machine models (SVMs) are less straightforward to interpret but can capture more complex multivariate non-linear relations. A gradual approach that balances the interpretability and predictability requirements is applied here to rate banks. First, a linear model is estimated; it is then improved by identifying univariate non-linear ratio transformations that emphasize distressed conditions; and finally SVMs are added to capture remaining multivariate non-linear relations."
Abstract: "Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In this paper, we propose a novel “two-phase” SVR training algorithm to detect outliers and reduce their negative impact. Our experimental results on three indices: Hang Seng Index, NASDAQ, and FSTE 100 index show that the proposed “two-phase” algorithm has improvement on the prediction."
Abstract: "In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data. To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods (e. g., statistical models and time series models) and neural network models. The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration."
Abstract: "This paper describes and evaluates the use of support vector regression to trade the three month Aluminium futures contract on the London Metal Exchange, over the period June 1987 to November 1999. The Support Vector Machine is a machine learning method for classification and regression and is fast replacing neural networks as the tool of choice for prediction and pattern recognition tasks, primarily due to their ability to generalise well on unseen data. The algorithm is founded on ideas derived from statistical learning theory and can be understood intuitively within a geometric framework. In this paper we use support vector regression to develop a number of trading submodels that when combined, result in a final model that exhibits above-average returns on out of sample data, thus providing some evidence that the aluminium futures price is less than efficient. Whether these inefficiencies will continue into the future is unknown."

Build Better Strategies! Part 4: Machine Learning.
Deep Blue was the first computer that won a chess world championship. That was 1996, and it took 20 years until another program, AlphaGo , could defeat the best human Go player. Deep Blue was a model based system with hardwired chess rules. AlphaGo is a data-mining system, a deep neural network trained with thousands of Go games. Not improved hardware, but a breakthrough in software was essential for the step from beating top Chess players to beating top Go players.
In this 4th part of the mini-series we’ll look into the data mining approach for developing trading strategies. This method does not care about market mechanisms. It just scans price curves or other data sources for predictive patterns. Machine learning or “Artificial Intelligence” is not always involved in data-mining strategies. In fact the most popular – and surprisingly profitable – data mining method works without any fancy neural networks or support vector machines.
Machine learning principles.
A learning algorithm is fed with data samples , normally derived in some way from historical prices. Each sample consists of n variables x 1 .. x n , commonly named predictors , features , signals , or simply input . These predictors can be the price returns of the last n bars, or a collection of classical indicators, or any other imaginable functions of the price curve (I’ve even seen the pixels of a price chart image used as predictors for a neural network!). Each sample also normally includes a target variable y , like the return of the next trade after taking the sample, or the next price movement. In the literature you can find y also named label or objective . In a training process , the algorithm learns to predict the target y from the predictors x 1 .. x n . The learned ‘memory’ is stored in a data structure named model that is specific to the algorithm (not to be confused with a financial model for model based strategies!). A machine learning model can be a function with prediction rules in C code, generated by the training process. Or it can be a set of connection weights of a neural network.
The predictors, features, or whatever you call them, must carry information sufficient to predict the target y with some accuracy. They m ust also often fulfill two formal requirements. First, all predictor values should be in the same range, like -1 .. +1 (for most R algorithms) or -100 .. +100 (for Zorro or TSSB algorithms). So you need to normalize them in some way before sending them to the machine. Second, the samples should be balanced , i. e. equally distributed over all values of the target variable. So there should be about as many winning as losing samples. If you do not observe these two requirements, you’ll wonder why you’re getting bad results from the machine learning algorithm.
Regression algorithms predict a numeric value, like the magnitude and sign of the next price move. Classification algorithms predict a qualitative sample class, for instance whether it’s preceding a win or a loss. Some algorithms, such as neural networks, decision trees, or support vector machines, can be run in both modes.
A few algorithms learn to divide samples into classes without needing any target y . That’s unsupervised learning , as opposed to supervised learning using a target. Somewhere inbetween is reinforcement learning , where the system trains itself by running simulations with the given features, and using the outcome as training target. AlphaZero, the successor of AlphaGo, used reinforcement learning by playing millions of Go games against itself. In finance there are few applications for unsupervised or reinforcement learning. 99% of machine learning strategies use supervised learning.
Whatever signals we’re using for predictors in finance, they will most likely contain much noise and little information, and will be nonstationary on top of it. Therefore financial prediction is one of the hardest tasks in machine learning. More complex algorithms do not necessarily achieve better results. The selection of the predictors is critical to the success. It is no good idea to use lots of predictors, since this simply causes overfitting and failure in out of sample operation. Therefore data mining strategies often apply a preselection algorithm that determines a small number of predictors out of a pool of many. The preselection can be based on correlation between predictors, on significance, on information content, or simply on prediction success with a test set. Practical experiments with feature selection can be found in a recent article on the Robot Wealth blog.
Here’s a list of the most popular data mining methods used in finance.
1. Indicator soup.
Most trading systems we’re programming for clients are not based on a financial model. The client just wanted trade signals from certain technical indicators, filtered with other technical indicators in combination with more technical indicators. When asked how this hodgepodge of indicators could be a profitable strategy, he normally answered: “Trust me. I’m trading it manually, and it works.”
It did indeed. At least sometimes. Although most of those systems did not pass a WFA test (and some not even a simple backtest), a surprisingly large number did. And those were also often profitable in real trading. The client had systematically experimented with technical indicators until he found a combination that worked in live trading with certain assets. This way of trial-and-error technical analysis is a classical data mining approach, just executed by a human and not by a machine. I can not really recommend this method – and a lot of luck, not to speak of money, is probably involved – but I can testify that it sometimes leads to profitable systems.
2. Candle patterns.
Not to be confused with those Japanese Candle Patterns that had their best-before date long, long ago. The modern equivalent is price action trading . You’re still looking at the open, high, low, and close of candles. You’re still hoping to find a pattern that predicts a price direction. But you’re now data mining contemporary price curves for collecting those patterns. There are software packages for that purpose. They search for patterns that are profitable by some user-defined criterion, and use them to build a specific pattern detection function. It could look like this one (from Zorro’s pattern analyzer):
This C function returns 1 when the signals match one of the patterns, otherwise 0. You can see from the lengthy code that this is not the fastest way to detect patterns. A better method, used by Zorro when the detection function needs not be exported, is sorting the signals by their magnitude and checking the sort order. An example of such a system can be found here.
Can price action trading really work? Just like the indicator soup, it’s not based on any rational financial model. One can at best imagine that sequences of price movements cause market participants to react in a certain way, this way establishing a temporary predictive pattern. However the number of patterns is quite limited when you only look at sequences of a few adjacent candles. The next step is comparing candles that are not adjacent, but arbitrarily selected within a longer time period. This way you’re getting an almost unlimited number of patterns – but at the cost of finally leaving the realm of the rational. It is hard to imagine how a price move can be predicted by some candle patterns from weeks ago.
Still, a lot effort is going into that. A fellow blogger, Daniel Fernandez, runs a subscription website (Asirikuy) specialized on data mining candle patterns. He refined pattern trading down to the smallest details, and if anyone would ever achieve any profit this way, it would be him. But to his subscribers’ disappointment, trading his patterns live (QuriQuant) produced very different results than his wonderful backtests. If profitable price action systems really exist, apparently no one has found them yet.
3. Linear regression.
The simple basis of many complex machine learning algorithms: Predict the target variable y by a linear combination of the predictors x 1 .. x n .
The coefficients a n are the model. They are calculated for minimizing the sum of squared differences between the true y values from the training samples and their predicted y from the above formula:
For normal distributed samples, the minimizing is possible with some matrix arithmetic, so no iterations are required. In the case n = 1 – with only one predictor variable x – the regression formula is reduced to.
which is simple linear regression , as opposed to multivariate linear regression where n > 1. Simple linear regression is available in most trading platforms, f. i. with the LinReg indicator in the TA-Lib. With y = price and x = time it’s often used as an alternative to a moving average. Multivariate linear regression is available in the R platform through the lm(..) function that comes with the standard installation. A variant is polynomial regression . Like simple regression it uses only one predictor variable x , but also its square and higher degrees, so that x n == x n :
With n = 2 or n = 3 , polynomial regression is often used to predict the next average price from the smoothed prices of the last bars. The polyfit function of MatLab, R, Zorro, and many other platforms can be used for polynomial regression.
4. Perceptron.
Often referred to as a neural network with only one neuron. In fact a perceptron is a regression function like above, but with a binary result, thus called logistic regression . It’s not regression though, it’s a classification algorithm. Zorro’s advise(PERCEPTRON, …) function generates C code that returns either 100 or -100, dependent on whether the predicted result is above a threshold or not:
You can see that the sig array is equivalent to the features x n in the regression formula, and the numeric factors are the coefficients a n .
5. N eural networks.
Linear or logistic regression can only solve linear problems. Many do not fall into this category – a famous example is predicting the output of a simple XOR function. And most likely also predicting prices or trade returns. An artificial neural network (ANN) can tackle nonlinear problems. It’s a bunch of perceptrons that are connected together in an array of layers . Any perceptron is a neuron of the net. Its output goes to the inputs of all neurons of the next layer, like this:
Like the perceptron, a neural network also learns by determining the coefficients that minimize the error between sample prediction and sample target. But this requires now an approximation process, normally with backpropagating the error from the output to the inputs, optimizing the weights on its way. This process imposes two restrictions. First, the neuron outputs must now be continuously differentiable functions instead of the simple perceptron threshold. Second, the network must not be too deep – it must not have too many ‘hidden layers’ of neurons between inputs and output. This second restriction limits the complexity of problems that a standard neural network can solve.
When using a neural network for predicting trades, you have a lot of parameters with which you can play around and, if you’re not careful, produce a lot of selection bias :
Number of hidden layers Number of neurons per hidden layer Number of backpropagation cycles, named epochs Learning rate, the step width of an epoch Momentum, an inertia factor for the weights adaption Activation function.
The activation function emulates the perceptron threshold. For the backpropagation you need a continuously differentiable function that generates a ‘soft’ step at a certain x value. Normally a sigmoid , tanh , or softmax function is used. Sometimes it’s also a linear function that just returns the weighted sum of all inputs. In this case the network can be used for regression, for predicting a numeric value instead of a binary outcome.
Neural networks are available in the standard R installation ( nnet , a single hidden layer network) and in many packages, for instance RSNNS and FCNN4R .
6. Deep learning.
Deep learning methods use neural networks with many hidden layers and thousands of neurons, which could not be effectively trained anymore by conventional backpropagation. Several methods became popular in the last years for training such huge networks. They usually pre-train the hidden neuron layers for achieving a more effective learning process. A Restricted Boltzmann Machine ( RBM ) is an unsupervised classification algorithm with a special network structure that has no connections between the hidden neurons. A Sparse Autoencoder ( SAE ) uses a conventional network structure, but pre-trains the hidden layers in a clever way by reproducing the input signals on the layer outputs with as few active connections as possible. Those methods allow very complex networks for tackling very complex learning tasks. Such as beating the world’s best human Go player.
Deep learning networks are available in the deepnet and darch R packages. Deepnet provides an autoencoder, Darch a restricted Boltzmann machine. I have not yet experimented with Darch, but here’s an example R script using the Deepnet autoencoder with 3 hidden layers for trade signals through Zorro’s neural() function:
7. Support vector machines.
Like a neural network, a support vector machine (SVM) is another extension of linear regression. When we look at the regression formula again,
we can interpret the features x n as coordinates of a n - dimensional feature space . Setting the target variable y to a fixed value determines a plane in that space, called a hyperplane since it has more than two (in fact, n-1 ) dimensions. The hyperplane separates the samples with y > o from the samples with y < 0 . The a n coefficients can be calculated in a way that the distances of the plane to the nearest samples – which are called the ‘support vectors’ of the plane, hence the algorithm name – is maximum. This way we have a binary classifier with optimal separation of winning and losing samples.
The problem: normally those samples are not linearly separable – they are scattered around irregularly in the feature space. No flat plane can be squeezed between winners and losers. If it could, we had simpler methods to calculate that plane, f. i. linear discriminant analysis . But for the common case we need the SVM trick: Adding more dimensions to the feature space. For this the SVM algorithm produces more features with a kernel function that combines any two existing predictors to a new feature. This is analogous to the step above from the simple regression to polynomial regression, where also more features are added by taking the sole predictor to the n-th power. The more dimensions you add, the easier it is to separate the samples with a flat hyperplane. This plane is then transformed back to the original n-dimensional space, getting wrinkled and crumpled on the way. By clever selecting the kernel function, the process can be performed without actually computing the transformation.
Like neural networks, SVMs can be used not only for classification, but also for regression. They also offer some parameters for optimizing and possibly overfitting the prediction process:
Kernel function. You normally use a RBF kernel (radial basis function, a symmetric kernel), but you also have the choice of other kernels, such as sigmoid, polynomial, and linear. Gamma, the width of the RBF kernel Cost parameter C, the ‘penalty’ for wrong classifications in the training samples.
An often used SVM is the libsvm library. It’s also available in R in the e1071 package. In the next and final part of this series I plan to describe a trading strategy using this SVM.
8. K-Nearest neighbor.
Compared with the heavy ANN and SVM stuff, that’s a nice simple algorithm with a unique property: It needs no training. So the samples are the model. You could use this algorithm for a trading system that learns permanently by simply adding more and more samples. The nearest neighbor algorithm computes the distances in feature space from the current feature values to the k nearest samples. A distance in n-dimensional space between two feature sets (x 1 .. x n ) and (y 1 .. y n ) is calculated just as in 2 dimensions:
The algorithm simply predicts the target from the average of the k target variables of the nearest samples, weighted by their inverse distances. It can be used for classification as well as for regression. Software tricks borrowed from computer graphics, such as an adaptive binary tree (ABT), can make the nearest neighbor search pretty fast. In my past life as computer game programmer, we used such methods in games for tasks like self-learning enemy intelligence. You can call the knn function in R for nearest neighbor prediction – or write a simple function in C for that purpose.
This is an approximation algorithm for unsupervised classification. It has some similarity, not only its name, to k-nearest neighbor. For classifying the samples, the algorithm first places k random points in the feature space. Then it assigns to any of those points all the samples with the smallest distances to it. The point is then moved to the mean of these nearest samples. This will generate a new samples assignment, since some samples are now closer to another point. The process is repeated until the assignment does not change anymore by moving the points, i. e. each point lies exactly at the mean of its nearest samples. We now have k classes of samples, each in the neighborhood of one of the k points.
This simple algorithm can produce surprisingly good results. In R, the kmeans function does the trick. An example of the k-means algorithm for classifying candle patterns can be found here: Unsupervised candlestick classification for fun and profit.
10. Naive Bayes.
This algorithm uses Bayes’ Theorem for classifying samples of non-numeric features (i. e. events ), such as the above mentioned candle patterns . Suppose that an event X (for instance, that the Open of the previous bar is below the Open of the current bar) appears in 80% of all winning samples. What is then the probability that a sample is winning when it contains event X ? It’s not 0.8 as you might think. The probability can be calculated with Bayes’ Theorem:
P(Y|X) is the probability that event Y (f. i. winning) occurs in all samples containing event X (in our example, Open(1) < Open(0) ). According to the formula, it is equal to the probability of X occurring in all winning samples (here, 0.8), multiplied by the probability of Y in all samples (around 0.5 when you were following my above advice of balanced samples) and divided by the probability of X in all samples.
If we are naive and assume that all events X are independent of each other, we can calculate the overall probability that a sample is winning by simply multiplying the probabilities P (X|winning) for every event X . This way we end up with this formula:
with a scaling factor s . For the formula to work, the features should be selected in a way that they are as independent as possible, which imposes an obstacle for using Naive Bayes in trading. For instance, the two events Close(1) < Close(0) and Open(1) < Open(0) are most likely not independent of each other. Numerical predictors can be converted to events by dividing the number into separate ranges.
The Naive Bayes algorithm is available in the ubiquitous e1071 R package.
11. Decision and regression trees.
Those trees predict an outcome or a numeric value based on a series of yes/no decisions, in a structure like the branches of a tree. Any decision is either the presence of an event or not (in case of non-numerical features) or a comparison of a feature value with a fixed threshold. A typical tree function, generated by Zorro’s tree builder, looks like this:
How is such a tree produced from a set of samples? There are several methods; Zorro uses the Shannon i nformation entropy , which already had an appearance on this blog in the Scalping article. At first it checks one of the features, let’s say x 1 . It places a hyperplane with the plane formula x 1 = t into the feature space. This hyperplane separates the samples with x 1 > t from the samples with x 1 < t . The dividing threshold t is selected so that the information gain – the difference of information entropy of the whole space, to the sum of information entropies of the two divided sub-spaces – is maximum. This is the case when the samples in the subspaces are more similar to each other than the samples in the whole space.
This process is then repeated with the next feature x 2 and two hyperplanes splitting the two subspaces. Each split is equivalent to a comparison of a feature with a threshold. By repeated splitting, we soon get a huge tree with thousands of threshold comparisons. Then the process is run backwards by pruning the tree and removing all decisions that do not lead to substantial information gain. Finally we end up with a relatively small tree as in the code above.
Decision trees have a wide range of applications. They can produce excellent predictions superior to those of neural networks or support vector machines. But they are not a one-fits-all solution, since their splitting planes are always parallel to the axes of the feature space. This somewhat limits their predictions. They can be used not only for classification, but also for regression, for instance by returning the percentage of samples contributing to a certain branch of the tree. Zorro’s tree is a regression tree. The best known classification tree algorithm is C5.0 , available in the C50 package for R.
For improving the prediction even further or overcoming the parallel-axis-limitation, an ensemble of trees can be used, called a random forest . The prediction is then generated by averaging or voting the predictions from the single trees. Random forests are available in R packages randomForest , ranger and Rborist .
Conclusão.
There are many different data mining and machine learning methods at your disposal. The critical question: what is better, a model-based or a machine learning strategy? There is no doubt that machine learning has a lot of advantages. You don’t need to care about market microstructure, economy, trader psychology, or similar soft stuff. You can concentrate on pure mathematics. Machine learning is a much more elegant, more attractive way to generate trade systems. It has all advantages on its side but one. Despite all the enthusiastic threads on trader forums, it tends to mysteriously fail in live trading.
Every second week a new paper about trading with machine learning methods is published (a few can be found below). Please take all those publications with a grain of salt. According to some papers, phantastic win rates in the range of 70%, 80%, or even 85% have been achieved. Although win rate is not the only relevant criterion – you can lose even with a high win rate – 85% accuracy in predicting trades is normally equivalent to a profit factor above 5. With such a system the involved scientists should be billionaires meanwhile. Unfortunately I never managed to reproduce those win rates with the described method, and didn’t even come close. So maybe a lot of selection bias went into the results. Or maybe I’m just too stupid.
Compared with model based strategies, I’ve seen not many successful machine learning systems so far. And from what one hears about the algorithmic methods by successful hedge funds, machine learning seems still rarely to be used. But maybe this will change in the future with the availability of more processing power and the upcoming of new algorithms for deep learning.
Classification using deep neural networks: Dixon. et. al.2018 Predicting price direction using ANN & SVM: Kara. et. al.2018 Empirical comparison of learning algorithms: Caruana. et. al.2006 Mining stock market tendency using GA & SVM: Yu. Wang. Lai.2005.
The next part of this series will deal with the practical development of a machine learning strategy.
30 thoughts on “Build Better Strategies! Part 4: Machine Learning”
Bela postagem. There is a lot of potential in these approach towards the market.
Btw are you using the code editor which comes with zorro? how is it possible to get such a colour configuration?
The colorful script is produced by WordPress. You can’t change the colors in the Zorro editor, but you can replace it with other editors that support individual colors, for instance Notepad++.
Is it then possible that notepad detects the zorro variables in the scripts? I mean that BarPeriod is remarked as it is with the zorro editor?
Theoretically yes, but for this you had to configure the syntax highlighting of Notepad++, and enter all variables in the list. As far as I know Notepad++ can also not be configured to display the function description in a window, as the Zorro editor does. There’s no perfect tool…
Concur with the final paragraph. I have tried many machine learning techniques after reading various ‘peer reviewed’ papers. But reproducing their results remains elusive. When I live test with ML I can’t seem to outperform random entry.
ML fails in live? Maybe the training of the ML has to be done with price data that include as well historical spread, roll, tick and so on?
I think reason #1 for live failure is data mining bias, caused by biased selection of inputs and parameters to the algo.
Thanks to the author for the great series of articles.
However, it should be noted that we don’t need to narrow our view with predicting only the next price move. It may happen that the next move goes against our trade in 70% of cases but it still worth making a trade. This happens when the price finally does go to the right direction but before that it may make some steps against us. If we delay the trade by one price step we will not enter the mentioned 30% of trades but for that we will increase the result of the remained 70% by one price step. So the criteria is which value is higher: N*average_result or 0.7*N*(avergae_result + price_step).
Bela postagem. If you just want to play around with some machine learning, I implemented a very simple ML tool in python and added a GUI. It’s implemented to predict time series.
Thanks JCL I found very interesting your article. I would like to ask you, from your expertise in trading, where can we download reliable historical forex data? I consider it very important due to the fact that Forex market is decentralized.
Desde já, obrigado!
There is no really reliable Forex data, since every Forex broker creates their own data. They all differ slightly dependent on which liquidity providers they use. FXCM has relatively good M1 and tick data with few gaps. You can download it with Zorro.
Thanks for writing such a great article series JCL… a thoroughly enjoyable read!
I have to say though that I don’t view model-based and machine learning strategies as being mutually exclusive; I have had some OOS success by using a combination of the elements you describe.
To be more exact, I begin the system generation process by developing a ‘traditional’ mathematical model, but then use a set of online machine learning algorithms to predict the next terms of the various different time series (not the price itself) that are used within the model. The actual trading rules are then derived from the interactions between these time series. So in essence I am not just blindly throwing recent market data into an ML model in an effort to predict price action direction, but instead develop a framework based upon sound investment principles in order to point the models in the right direction. I then data mine the parameters and measure the level of data-mining bias as you’ve described also.
It’s worth mentioning however that I’ve never had much success with Forex.
Anyway, best of luck with your trading and keep up the great articles!
Thanks for posting this great mini series JCL.
I recently studied a few latest papers about ML trading, deep learning especially. Yet I found that most of them valuated the results without risk-adjusted index, i. e., they usually used ROC curve, PNL to support their experiment instead of Sharpe Ratio, for example.
Also, they seldom mentioned about the trading frequency in their experiment results, making it hard to valuate the potential profitability of those methods. Why is that? Do you have any good suggestions to deal with those issues?
ML papers normally aim for high accuracy. Equity curve variance is of no interest. This is sort of justified because the ML prediction quality determines accuracy, not variance.
Of course, if you want to really trade such a system, variance and drawdown are important factors. A system with lower accuracy and worse prediction can in fact be preferable when it’s less dependent on market condictions.
“In fact the most popular – and surprisingly profitable – data mining method works without any fancy neural networks or support vector machines.”
Would you please name those most popular & surprisingly profitable ones. So I could directly use them.
I was referring to the Indicator Soup strategies. For obvious reasons I can’t disclose details of such a strategy, and have never developed such systems myself. We’re merely coding them. But I can tell that coming up with a profitable Indicator Soup requires a lot of work and time.
Well, i am just starting a project which use simple EMAs to predict price, it just select the correct EMAs based on past performance and algorithm selection that make some rustic degree of intelligence.
Jonathan. orrego@gmail offers services as MT4 EA programmer.
Thanks for the good writeup. It in reality used to be a leisure account it.
Look complicated to more delivered agreeable from you!
By the way, how could we be in contact?
There are following issues with ML and with trading systems in general which are based on historical data analysis:
1) Historical data doesn’t encode information about future price movements.
Future price movement is independent and not related to the price history. There is absolutely no reliable pattern which can be used to systematically extract profits from the market. Applying ML methods in this domain is simply pointless and doomed to failure and is not going to work if you search for a profitable system. Of course you can curve fit any past period and come up with a profitable system for it.
The only thing which determines price movement is demand and supply and these are often the result of external factors which cannot be predicted. For example: a war breaks out somewhere or other major disaster strikes or someone just needs to buy a large amount of a foreign currency for some business/investment purpose. These sort of events will cause significant shifts in the demand supply structure of the FX market . As a consequence, prices begin to move but nobody really cares about price history just about the execution of the incoming orders. An automated trading system can only be profitable if it monitors a significant portion of the market and takes the supply and demand into account for making a trading decision. But this is not the case with any of the systems being discussed here.
2) Race to the bottom.
Even if (1) wouldn’t be true and there would be valuable information encoded in historical price data, you would still face following problem: there are thousands of gold diggers out there, all of them using similar methods and even the same tools to search for profitable systems and analyze the same historical price data. As a result, many of them will discover the same or very similar “profitable” trading systems and when they begin actually trading those systems, they will become less and less profitable due to the nature of the market.
The only sure winners in this scenario will be the technology and tool vendors.
I will be still keeping an eye on your posts as I like your approach and the scientific vigor you apply. Your blog is the best of its kind – keep the good work!
One hint: there are profitable automated systems, but they are not based on historical price data but on proprietary knowledge about the market structure and operations of the major institutions which control these markets. Let’s say there are many inefficiencies in the current system but you absolutely have no chance to find the information about those by analyzing historical price data. Instead you have to know when and how the institutions will execute market moving orders and front run them.
Thanks for the extensive comment. I often hear these arguments and they sound indeed intuitive, only problem is that they are easily proven wrong. The scientific way is experiment, not intuition. Simple tests show that past and future prices are often correlated – otherwise every second experiment on this blog had a very different outcome. Many successful funds, for instance Jim Simon’s Renaissance fund, are mainly based on algorithmic prediction.
One more thing: in my comment I have been implicitly referring to the buy side (hedge funds, traders etc) not to the sell side (market makers, banks). The second one has always the edge because they sell at the ask and buy at the bid, pocketing the spread as an additional profit to any strategy they might be running. Regarding Jim Simon’s Renaissance: I am not so sure if they have not transitioned over the time to the sell side in order to stay profitable. There is absolutely no information available about the nature of their business besides the vague statement that they are using solely quantitative algorithmic trading models…
Thanks for the informative post!
Regarding the use of some of these algorithms, a common complaint which is cited is that financial data is non-stationary…Do you find this to be a problem? Couldn’t one just use returns data instead which is (I think) stationary?
Yes, this is a problem for sure. If financial data were stationary, we’d all be rich. I’m afraid we have to live with what it is. Returns are not any more stationary than other financial data.
Hello sir, I developed some set of rules for my trading which identifies supply demand zones than volume and all other criteria. Can you help me to make it into automated system ?? If i am gonna do that myself then it can take too much time. Please contact me at svadukia@gmail if you are interested.
Sure, please contact my employer at info@opgroup. de. They’ll help.
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Technical analysis has always been rejected and looked down upon by quants, academics, or anyone who has been trained by traditional finance theories. I have worked for proprietary trading desk of a first tier bank for a good part of my career, and surrounded by those ivy-league elites with background in finance, math, or financial engineering. I must admit none of those guys knew how to trade directions. They were good at market making, product structures, index arb, but almost none can making money trading directions. Por quê? Because none of these guys believed in technical analysis. Then again, if you are already making your millions why bother taking the risk of trading direction with your own money. For me luckily my years of training in technical analysis allowed me to really retire after laying off from the great recession. I look only at EMA, slow stochastics, and MACD; and I have made money every year since started in 2009. Technical analysis works, you just have to know how to use it!!

Forex prediction svm

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