Previsão de forex svm

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. Polegares para cima? Classificação de Sentimentos usando Técnicas de Aprendizado de Máquina, Em: EMNLP '02: Anais da Conferência ACL-02 sobre Métodos Empíricos no Processamento de Linguagem Natural - Volume 10, páginas 79--86. [Citado por 154] (36,66 / ano)

Resumo: "Consideramos o problema de classificar documentos não por tópico, mas pelo sentimento geral, por exemplo, determinar se uma revisão é positiva ou negativa. Usando as análises de filmes como dados, descobrimos que as técnicas padrão de aprendizado de máquina superam definitivamente as linhas de base produzidas pelo homem. No entanto, os três métodos de aprendizado de máquina que empregamos (Naive Bayes, classificação de entropia máxima e máquinas de vetores de suporte) não se baseiam tanto na classificação de sentimentos quanto na categorização tradicional baseada em tópicos, concluindo examinando fatores que tornam o problema de classificação de sentimentos mais desafiador."

Resumo: "O arcabouço de evidência bayesiana é aplicado neste artigo à regressão de máquina de vetores de apoio a quadrados mínimos (LS-SVM) para inferir modelos não-lineares para predizer uma série temporal financeira e a volatilidade relacionada. No primeiro nível de inferência, O modelo está relacionado com a formulação LS-SVM, que permite incluir a volatilidade variável no tempo do mercado por meio de 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, e a comparação de modelos é realizada no terceiro nível de inferência para sintonizar automaticamente os parâmetros da função kernel e selecionar os inputs relevantes. A formulação LS-SVM permite derivar expressões analíticas no espaço de recursos e expressões práticas são obtidas no espaço dual substituindo o produto interno por t ele relacionou a função do kernel usando o teorema de Mercer. Os desempenhos de previsão um passo à frente obtidos na previsão da taxa semanal de faturamento de 90 dias e os preços diários de fechamento do DAX30 mostram que previsões significativas de sinais fora da amostra podem ser feitas com relação à estatística do teste de Pesaran-Timmerman "

Resumo: "Este trabalho trata da aplicação de uma nova técnica de redes neurais, a máquina de vetores de suporte (SVM), na previsão de séries temporais financeiras. O objetivo deste trabalho é examinar a viabilidade da SVM na previsão de séries temporais financeiras comparando-a com uma rede neural multi-camada de retropropagação (BP) Cinco contratos de futuros reais que são obtidos do Chicago Mercantile Market são usados ​​como conjuntos de dados O experimento mostra que o SVM supera a rede neural BP com base nos critérios de média quadrática normalizada erro (NMSE), erro absoluto médio (MAE), simetria direcional (DS) e simetria direcional ponderada (WDS) Como não existe uma maneira estruturada de escolha dos parâmetros livres de SVMs, 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 temporais financeiras ".

Resumo: "Este artigo propõe uma versão modificada de máquinas de vetores de suporte, denominada máquina de vetores de suporte C - ascending, para modelar séries temporais financeiras não-estacionárias. As máquinas de vetores de suporte ascendentes C são obtidas por uma simples modificação da função de risco regularizada. suporte a máquinas de vetores, segundo o qual os erros mais recentes são penalizados mais do que os erros distantes sensíveis. Esse procedimento é baseado no conhecimento prévio de que, na série temporal financeira não estacionária, a dependência entre a entrada variáveis ​​e variável de saída mudam gradualmente ao longo do tempo, especificamente, os dados passados ​​recentes poderiam fornecer informações mais importantes do que os dados antigos distantes No experimento, as máquinas de vetores de suporte C - ascending são testadas usando três futuros reais coletados do Chicago Mercantile Market. É mostrado que as máquinas de vetores de suporte C - ascending com os dados de amostra realmente ordenados consistentemente previam melhor que o padrão su máquinas de vetor pport, com o pior desempenho quando os dados de amostra pedidos reversamente são usados. Além disso, as máquinas de vetores de suporte C - ascending usam menos vetores de suporte do que as máquinas de vetores de suporte padrão, resultando em uma representação mais esparsa da solução. "

Resumo: "A análise de rating de crédito corporativo tem atraído muitos interesses de pesquisa na literatura. Estudos recentes mostraram que os métodos de Inteligência Artificial (IA) alcançaram melhor desempenho que os métodos estatísticos tradicionais. Este artigo introduz uma técnica relativamente nova de aprendizado de máquina, SVM), para o problema na tentativa de fornecer um modelo com melhor poder explicativo. Nós usamos backpropagation neural network (BNN) como um benchmark e obtivemos precisão de previsão em torno de 80% para ambos os métodos BNN e SVM para os mercados dos Estados Unidos e Taiwan. , apenas uma ligeira melhoria da SVM foi observada. Outra direção da pesquisa é melhorar a interpretabilidade dos modelos baseados em IA. Nós aplicamos resultados de pesquisas recentes na interpretação de modelos de redes neurais e obtivemos importância relativa das variáveis ​​financeiras de entrada dos modelos de rede neural Com base nesses resultados, realizamos uma análise comparativa do mercado sobre as diferenças entre fatores de alerta nos mercados dos Estados Unidos e de Taiwan ".

Resumo: "Este trabalho propõe o uso de especialistas em máquinas de vetores de suporte (SVMs) para previsão de séries temporais. Os especialistas em SVMs generalizados têm uma arquitetura de rede neural de dois estágios. Na primeira etapa, o mapa de características auto-organizáveis ​​(SOM) é usado como algoritmo de clustering para particionar todo o espaço de entrada em várias regiões desconexas. Uma arquitetura estruturada em árvore é adotada na partição para evitar o problema de predeterminar o número de regiões particionadas. Em seguida, no segundo estágio, vários SVMs, também chamados de especialistas em SVM, As regiões particionadas de melhor ajuste são construídas encontrando a função de kernel mais apropriada e os parâmetros livres ideais de SVMs. Os dados de manchas solares, conjuntos de dados Santa Fe A, C e D, e os dois conjuntos de dados de construção são avaliados no experimento. mostra que os especialistas em SVMs alcançaram melhorias significativas no desempenho de generalização em comparação com os modelos de SVMs únicos. Além disso, os especialistas em SVMs também convergem mais rápido e se menos vetores de suporte ".

Resumo: "Máquinas de vetores de suporte (SVMs) são métodos promissores para a previsão de séries temporais financeiras, pois utilizam uma função de risco composta pelo erro empírico e um termo regularizado que é derivado do princípio de minimização do risco estrutural. Este estudo aplica SVM a Além disso, este estudo examina a viabilidade de aplicar SVM na previsão financeira, comparando-o com redes neurais de propagação reversa e raciocínio baseado em casos. Os resultados experimentais mostram que 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 propagação reversa (BPN) funciona bem em tarefas de reconhecimento de padrões, o método tem algum limitações na medida em que é uma arte encontrar uma estrutura de modelo apropriada e uma solução ótima. Além disso, é necessário carregar o maior número possível de conjuntos de treinamento na rede para pesquisar os pesos da rede. características do espaço de funcionalidades sem derivar pesos das redes a partir dos dados de formação, é capaz de extrair a solução óptima com o pequeno tamanho do conjunto de treino. Neste estudo, mostramos que o classificador proposto da abordagem SVM supera o BPN ao problema da falência das empresas predição.

Os resultados demonstram que o desempenho de precisão e generalização do SVM é melhor que o do BPN, à medida que o tamanho do conjunto de treinamento fica menor. Também examinamos o efeito da variabilidade no desempenho em relação a vários valores de parâmetros no 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 vetor de suporte (SVM) tem recebido crescente interesse em áreas que vão desde a sua aplicação original no reconhecimento de padrões a outras aplicações, como estimativa de regressão devido ao seu notável desempenho de generalização. aplicação da SVM na previsão de séries temporais financeiras A exeqüibilidade de aplicar a SVM na previsão financeira é examinada primeiramente comparando-a com a rede neural de propagação reversa multicamadas (BP) e a rede neural da função de base radial regularizada (RBF). SVM em relação aos parâmetros livres é investigado experimentalmente, sendo então propostos parâmetros adaptativos, incorporando a não estacionariedade de séries temporais financeiras em SVM, sendo utilizados cinco contratos futuros reais coligados do Chicago Mercantile Market como conjuntos de dados. os três métodos, SVM supera a rede neural BP em financi previsão de desempenho, e há um desempenho de generalização comparável entre o SVM e a rede neural de RBF regularizada. Além disso, os parâmetros livres de SVM têm um grande efeito no desempenho de generalização. O SVM com parâmetros adaptativos pode alcançar um desempenho de generalização mais alto e usar menos vetores de suporte do que o SVM padrão na previsão financeira. "

Resumo: "O uso de Support Vector Machines (SVMs) é estudado na previsão financeira comparando-o com um perceptron multicamada treinado pelo algoritmo Back Propagation (BP). As SVMs projetam melhor que a BP com base nos critérios de Erro Quadrático Médio Normalizado (NMSE), erro médio absoluto (MAE), simetria direcional (DS), tendência Correct Up (CP) e Correct Down (CD). O índice de preço diário S & amp; P 500 é usado como o conjunto de dados. maneira de escolher os parâmetros livres de SVMs, o erro de generalização com relação aos parâmetros livres de SVMs é investigado neste experimento. Como ilustrado no experimento, eles têm pouco impacto sobre a solução. A análise dos resultados experimentais demonstra que é vantajoso para aplicar SVMs para prever a série temporal financeira ".

Resumo: "A previsão de falências atraiu muitos interesses de pesquisa na literatura anterior, e estudos recentes mostraram que técnicas de aprendizado de máquina alcançaram melhor desempenho do que as estatísticas tradicionais. Este artigo aplica máquinas de vetores de suporte (SVMs) ao problema de previsão de falência. sugerir um novo modelo com melhor poder explicativo e estabilidade. Para servir a este propósito, usamos uma técnica de busca de grade usando validação cruzada de 5 vezes para descobrir os valores ótimos de parâmetro da função kernel do SVM. precisão de predição de SVM, comparamos seu desempenho com os de análise discriminante múltipla (MDA), análise de regressão logística (Logit), e redes neurais de propagação reversa totalmente conectadas de três camadas (BPNs) Os resultados do experimento mostram que SVM supera o outro métodos."

Resumo: "O uso de sistemas inteligentes para previsões de mercado de ações foi amplamente estabelecido. Neste artigo, investigamos como o comportamento aparentemente caótico dos mercados de ações pode 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 S & P CNX NIFTY Analisamos os valores do índice principal do Nasdaq 100 de 7 anos e os valores do índice NIFTY de 4 anos Este artigo investiga o desenvolvimento de um Uma técnica confiável e eficiente para modelar o comportamento aparentemente caótico dos mercados de ações. Nós consideramos uma rede neural artificial treinada usando o algoritmo Levenberg-Marquardt, Support Vector Machine (SVM), modelo neurofuzzy Takagi-Sugeno e uma Rede Neural de Diferença Boosting (DBNN). Este artigo explica brevemente como os diferentes paradigmas conexionistas podem ser formulados usando diferentes métodos de aprendizagem e, em seguida, investiga se eles podem fornecer o nível de desempenho exigido, que seja suficientemente bom e robusto para fornecer um modelo de previsão confiável para os índices do mercado de ações. Os resultados dos experimentos revelam que todos os paradigmas conexionistas considerados poderiam representar o comportamento dos índices de ações com muita precisão ".

Resumo: "Recentemente, a regressão vetorial de suporte (SVR) foi introduzida para resolver problemas de regressão e predição. Neste trabalho, aplicamos SVR às tarefas de previsão financeira. Em particular, os dados financeiros são geralmente ruidosos e o risco associado é variável no tempo. Portanto, o nosso modelo SVR é uma extensão do padrão SVR que incorpora a adaptação das margens, variando as margens do SVR, podemos refletir a mudança na volatilidade dos dados financeiros e analisamos o efeito de margens assimétricas para permitir a redução do risco de queda. Nossos resultados experimentais mostram que o uso do desvio padrão para calcular uma margem variável fornece um bom resultado preditivo na previsão do Índice Hang Seng. "

Resumo: "Support vector machine (SVM) é um tipo muito específico de algoritmos de aprendizado caracterizados pelo controle de capacidade da função de decisão, pelo uso das funções do kernel e pela dispersão da solução. Neste trabalho, investigamos a previsibilidade de recursos financeiros. direção do 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 Linear Discriminante, Análise Quadrática Discriminante e Redes Neurais de Retropropagação Elman. os outros métodos de classificação. Além disso, propomos um modelo de combinação, integrando SVM com os outros métodos de classificação. O modelo de combinação apresenta o melhor desempenho entre todos os métodos de previsão. "

Resumo: "O principal objetivo deste trabalho é comparar a máquina de vetores de suporte (SVM) desenvolvida pela Vapnik com outras técnicas, como Backpropagation e Radial Basis Function (RBF) para aplicações de previsão financeira. A teoria do algoritmo SVM é baseada em teoria de aprendizagem estatística O treinamento de SVMs leva a um problema de programação quadrática (QP). Resultados computacionais preliminares para predição de preços de ações também são apresentados. "

Resumo: "Este artigo propõe uma versão modificada de máquinas de vetores de suporte (SVMs), denominadas máquinas de vetores 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 SVMs 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 baseiam-se no conhecimento prévio de que, na série temporal não estacionária, os pontos de dados recentes 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 que os SVMs padrão na previsão de séries temporais não estacionárias. essa modificação é que os DSVMs usam menos vetores de suporte, resultando em uma representação esparsa da solução. "

Resumo: "Este artigo propõe uma versão modificada de máquinas de vetores de suporte (SVMs), chamadas de máquinas de vetores de suporte de descendente (& # 949; - DSVMs), para modelar séries temporais financeiras não estacionárias. O & # 949; Os - DSVMs são obtidos incorporando o conhecimento do domínio do problema - não-estacionariedade de séries temporais financeiras em SVMs. Ao contrário dos SVMs padrão que usam um tubo constante em todos os pontos de dados de treinamento, os SDMs usam um adaptador adaptativo. tube para lidar com as mudanças na estrutura dos dados. O experimento mostra que os SDMs se generalizam melhor do que os SVMs padrão na previsão de séries temporais financeiras não estacionárias. Outra vantagem dessa modificação é que o & # 949; Os DSVMs convergem para menos vetores de suporte, resultando em uma representação mais esparsa da solução. "

Resumo: "As informações de metadados desempenham um papel crucial no aumento da eficiência e arquivamento da organização de documentos. Os metadados de notícias incluem o DateLine, ByLine, HeadLine e muitos outros. Descobrimos que as informações do Headline são úteis para adivinhar o tema da notícia. Particularmente para artigos de notícias financeiras Descobrimos que o HeadLine pode ser especialmente útil para localizar sentenças explicativas para quaisquer eventos importantes, como mudanças significativas nos preços das ações. Neste trabalho, exploramos uma abordagem de aprendizagem baseada em vetor de suporte para extrair automaticamente os metadados do HeadLine. de encontrar o HeadLine s melhora se os DateLines são identificados primeiro. Então usamos os HeadLine s extraídos 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 alteração significativa de preço. "

Resumo: "Impulsionados pela necessidade de alocação de capital de forma rentável e pelos recém-sugeridos regulamentos de Basiléia II, as instituições financeiras estão cada vez mais obrigadas a construir modelos de credit scoring que avaliem o risco de inadimplência de seus clientes. Muitas técnicas têm sido sugeridas O Support Vector Machines (SVMs) é uma nova técnica promissora que recentemente emanou de diferentes domínios, como estatística aplicada, redes neurais e aprendizado de máquina. Neste artigo, nós experimentamos máquinas de vetor de suporte de mínimos quadrados (LS-SVMs ), uma versão recentemente modificada de SVMs, e relata resultados significativamente melhores quando contrastados com as técnicas clássicas ".

Resumo: "A abordagem da Rede Neural Convencional tem sido útil na previsão do estresse corporativo das demonstrações contábeis. Neste artigo, adotamos uma abordagem do problema da Máquina de Vetores de Suporte. Uma nova maneira de selecionar preditores de falência é mostrada usando a distância Euclidiana. critério calculado dentro do kernel SVM. Um estudo comparativo é fornecido usando três modelos clássicos de socorro corporativo e um modelo alternativo baseado na abordagem SVM. "

Abstract: "Uma arquitetura de rede neural de dois estágios construída pela combinação de Support Vector Machines (SVMs) com mapa de características auto-organizáveis ​​(SOM) é proposta para previsão de séries temporais. No primeiro estágio, SOM é usado como um algoritmo de clustering todo o espaço de entrada em várias regiões disjunta. Uma arquitetura estruturada em árvore é adotada na partição para evitar o problema de predeterminar o número de regiões particionadas. Então, no segundo estágio, vários SVMs, também chamados de especialistas em SVM, que melhor se ajustam a cada A região particionada é construída encontrando a função de kernel mais apropriada e os parâmetros de aprendizado ideais das SVMs. A taxa de câmbio de Santa Fé e cinco contratos de futuros reais são usados ​​no experimento. velocidade de convergência em comparação com um único modelo SVM. "

Resumo: "Neste artigo apresentamos uma análise dos resultados de um estudo sobre previsão de preços de eletricidade no atacado utilizando redes neurais (NNs) e Support Vector Machines (SVM). Freqüentes mudanças regulatórias nos mercados de eletricidade e no mercado As estratégias de precificação (licitação) fazem com que a reciclagem eficiente seja crucial para manter a precisão dos modelos de previsão de preço de energia elétrica. A eficiência do NN e SVM para avaliação de preços foi avaliada usando dados regionais do Mercado Nacional de Eletricidade da Austrália (NEM) durante o período. de setembro de 1998 a dezembro de 1998. A análise dos resultados mostrou que as SVMs com uma única solução produzem precisões de previsão mais consistentes e, portanto, exigem menos tempo para treinar de forma ideal do que os NNs, o que pode resultar em uma solução em um grande número de mínimos locais As precisões de previsão do SVM e do NN foram muito semelhantes. "

Resumo: "O uso de sistemas inteligentes para predições do mercado de ações tem sido amplamente estabelecido. Neste trabalho, investigamos como o comportamento aparentemente caótico dos mercados de ações poderia ser bem representado usando um conjunto de paradigmas inteligentes. Para demonstrar a técnica proposta, consideramos Índice Nasdaq-100 da Nasdaq Stock Market SM e índice de ações S & P CNX NIFTY Os paradigmas inteligentes considerados foram uma rede neural artificial treinada usando o algoritmo de Levenberg-Marquardt, máquina de vetores de suporte, modelo neuro-fuzzy de Takagi-Sugeno e uma diferença Os diferentes paradigmas foram combinados usando duas abordagens diferentes, de modo a otimizar o desempenho, reduzindo as diferentes medidas de erro. A primeira abordagem é baseada em uma medida de erro direto eo segundo método é baseado em um algoritmo evolutivo para procurar o melhor combinação linear dos diferentes paradigmas inteligentes. Os resultados experimentais revelam que o ensemble techniq O desempenho dos modelos foi melhor do que os métodos individuais e a abordagem direta do conjunto parece funcionar bem para o problema considerado. "

Resumo: "Recentemente, a SVR (Support Vector Regression) tem sido aplicada à previsão de séries temporais financeiras. A série histórica financeira geralmente contém as características de tamanho de amostra pequeno, alto ruído e não-estacionário. Especialmente a volatilidade da série temporal variando e incorporando algumas informações valiosas sobre a série. Anteriormente, tínhamos proposto usar a volatilidade nos dados para alterar de forma adaptativa a largura da margem em SVR. Nós notamos que a margem e a margem inferior não seriam necessariamente as mesmas, e Também observamos que a escolha deles afetaria o risco de subida, o risco de queda e o desempenho global de predição. Neste trabalho, introduzimos uma nova abordagem para adotar o momento no ajuste de margens assimétricas. o índice Hang Seng e a Dow Jones Industrial Average. "

Resumo: "O uso de sistemas inteligentes para previsões de mercado de ações foi amplamente estabelecido. Neste artigo, investigamos como o comportamento aparentemente caótico dos mercados de ações pode ser bem representado usando vários paradigmas conexionistas e técnicas de soft computing. Para demonstrar as diferentes técnicas, Consideramos o índice Nasdaq-100 do Nasdaq Stock MarketTM e o índice de ações S & P CNX NIFTY Analisamos os valores do índice principal do Nasdaq 100 de 7 anos e os valores do índice NIFTY de 4 anos, investigando o desenvolvimento de uma técnica confiável e eficiente para modelar o caótico Considerou-se uma rede neural artificial treinada utilizando algoritmo de Levenberg-Marquardt, SVM (Support Vector Machine), modelo neuro-fuzzy de Takagi-Sugeno e DBNN (Differential Boosting Neural Network). paradigmas podem ser formulados usando diferentes métodos de aprendizagem e, em seguida, investiga se eles podem nível de desempenho exigido, que são suficientemente bons e robustos para fornecer um modelo de previsão confiável para os índices do mercado de ações. Os resultados dos experimentos revelam que todos os paradigmas conexionistas considerados poderiam representar o comportamento dos índices de ações com muita precisão. "

Resumo: "Recentemente, a regressão vetorial de suporte (SVR) foi aplicada à predição de séries temporais financeiras. As características típicas de séries temporais financeiras são não-estacionárias e ruidosas. A volatilidade, geralmente variável no tempo, das séries temporais é Informações valiosas sobre a série Anteriormente, havíamos proposto usar a volatilidade para alterar de forma adaptativa a largura da margem de SVR. Verificamos que a margem de lucro e a margem de baixa não são necessariamente as mesmas, e observamos que a escolha delas seria afetam o risco ascendente, o risco negativo e também o resultado geral da previsão. Neste artigo, introduzimos uma nova abordagem para adaptar as margens assimétricas usando o momentum. Pedimos e comparamos esse método para prever o Índice Hang Seng e o Dow Jones Industrial Average "

Resumo: "Vantagens e limitações dos modelos de volatilidade existentes para previsão da volatilidade do mercado de ações e câmbio de dados multiescala e de alta dimensão foram identificadas. Máquinas de vetores de suporte (SVM) têm sido propostas como um modelo de volatilidade complementar capaz de efetivamente extração de informações de dados multiescala e de mercado de alta dimensionalidade Os modelos baseados em SVM podem lidar com efeitos de memória longa e multiescala de mercados não homogêneos, sem premissas restritivas e aproximações exigidas por outros modelos. resultados preliminares com dados de câmbio sugerem que SVM pode trabalhar com eficiência Entradas de alta dimensão para contabilizar a volatilidade de efeitos de memória longa e multiescala. Vantagens dos modelos baseados em SVM devem ser de suma importância no campo emergente de finanças de alta frequência e em modelos multivariados para gerenciamento de risco de portfólio. "

Resumo: "Support vector machines (SVMs) é uma nova ferramenta não paramétrica para estimação de regressão. Utilizaremos essa ferramenta para estimar os parâmetros de um modelo GARCH para prever a volatilidade condicional dos retornos do mercado de ações. Os modelos GARCH são usualmente estimados usando máxima verossimilhança ( ML), assumindo que os dados são normalmente distribuídos. Neste artigo, vamos mostrar que os modelos GARCH podem ser estimados usando SVMs e que essas estimativas têm uma maior capacidade de previsão do que as obtidas através de métodos comuns ML. "

Resumo: "Algoritmos de classificação como análise discriminante linear e regressão logística são técnicas lineares populares para modelagem e previsão de problemas corporativos. Essas técnicas visam encontrar uma combinação linear ótima de variáveis ​​de entrada explicativas, como, por exemplo, índices de solvência e liquidez, para Analisar, modelar e prever o risco de default corporativo Recentemente, foram desenvolvidas técnicas de classificação não linear baseadas em kernel, como máquinas de vetor de suporte, máquinas de vetor de quadrados mínimos e análise discriminante de fisher de núcleo. Basicamente, esses métodos mapeiam as entradas primeiro de maneira não linear. para um espaço de característica induzida pelo núcleo de alta dimensionalidade, no qual um classificador linear é construído na segunda etapa. Expressões práticas são obtidas no chamado espaço dual pela aplicação do teorema de Mercer. classificação baseada em kernel não-linear e ilustram seu desempenho na previsão g falência de empresas de média capitalização na Bélgica e nos Países Baixos. "

Resumo: "Recentemente, a máquina de vetores de suporte (SVM) se tornou uma ferramenta popular na previsão de séries temporais. Ao desenvolver um SVM de sucesso, a extração de características é o primeiro passo importante. Este trabalho propõe as aplicações da análise de componentes principais (PCA). análise de componentes principais (KPCA) e análise de componentes independentes (ICA) para SVM para extração de características PCA transforma linearmente as entradas originais em características não correlacionadas KPCA é um PCA não linear desenvolvido usando o método kernel. Examinando os dados das manchas solares e um contrato real de futuros, o experimento mostra que a SVM por extração de recurso usando PCA, KPCA ou ICA pode ter um desempenho melhor do que sem extração de recursos, além de melhor desempenho de generalização no KPCA e ICA. extração de recurso de extração de recurso PCA ".

Resumo: "Este trabalho trata da aplicação da análise de saliência em Support Vector Machines (SVMs) para seleção de atributos. A importância da característica é classificada através da avaliação da sensibilidade da saída da rede à entrada característica em termos da derivada parcial. É desenvolvida uma abordagem para remover os recursos irrelevantes com base na sensibilidade. Cinco contratos futuros são examinados no experimento. Com base nos resultados da Simulação, é mostrado que essa análise de saliência é eficaz em SVMs para identificar características importantes. "

Resumo: "O preço da energia é o indicador mais importante nos mercados de eletricidade e suas características estão relacionadas ao mecanismo de mercado e à mudança versus os comportamentos dos participantes do mercado. É necessário construir um modelo de previsão de preços em tempo real com capacidade adaptativa. papel, um método de regressão vetorial de suporte on-line preciso (AOSVR) é aplicado para atualizar o modelo de previsão de preço Os resultados dos testes numéricos mostram que o método é eficaz na previsão dos preços dos mercados de energia elétrica. "

Abstract: "Usamos as máquinas de vetores de suporte (SVM) em uma abordagem de classificação para" bater o mercado ". Dada a informação fundamental sobre contabilidade e preço das ações negociadas na Bolsa de Valores da Austrália, tentamos usar SVM para identificar ações que são prováveis para superar o mercado, tendo retornos excepcionais. O portfólio igualmente ponderado formado pelas ações selecionadas pela SVM tem um retorno total de 208% ao longo de um período de cinco anos, superou significativamente o benchmark de 71 %.Também damos uma nova perspectiva com uma classe tradeoff de sensibilidade, em que a saída do SVM é interpretada como uma medida de probabilidade e classificada, de tal forma que as ações selecionadas podem ser fixadas para os 25% mais altos "

Resumo: "Em Support Vector Machines (SVM), um modelo não linear é estimado com base na solução de um problema de programação quadrática (QP). A função de custo quadrático consiste em um termo de custo de máxima verossimilhança com variância constante e uma regularização. Ao especificar uma diferença de inclusão no modelo de variância de ruído, o termo de máxima verossimilhança é adotado para o caso de ruído heterocedástico, que surge em séries temporais financeiras, e os SVMs de Volatilidade Tubo resultantes são aplicados em um dia à frente. previsão do índice de ações DAX 30. A influência dos preços de fechamento de hoje da Bolsa de Valores de Nova York na previsão do preço de fechamento DAX30 de amanhã é analisada. "

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."

Linear SVM's for daily range estimation using Rapidminer.

First I would like thank mladen for his help with the csv importer and exporter. If you are running the estimation across many currency pairs, they are really a huge time saver. In this thread I will explain how to set the exporter, storing the data to the Rapidminer repository and lastly the svm scripts themselves.

To get things started, download Rapidminer 64bit from here Rapid - I - Download RapidMiner Community Windows 64 and create the rapidminer repository. Then run the exporter as indicator which will write a csv file to your expert/files directory. The exporter is setup to output five decimal places for EURUSD. To export EURJPY for instance you will have to set the OHLCfmt parameter to - T3.3 and for XAU or S&P500 futures - T4.2.

One important requirement is that Support Vector Machine learners need training data that has a wider range of values than the out of sample data in order to work. Looking at EURUSD this is not an issue as we do not have to go too far back to find a range of 1.1870 - 1.5140. If you chose this range as your training data, your results would be valid until your out of sample values exceeded that range. One way around the problem is normalization of values but first you have to decide what you are trying to estimate and decide if normalization will give you an output that makes sense.

Setting the training range.

The exporter has two parameters, FromDateTime and ThruDateTime. There was a time when I trained only on weekends, saved the model and ran that model over the week. After some testing it became evident that the best results from forex time series came from training everyday especially when there is a lot of volatility so that is what I do now. I usually estimate the Dollar index, S&P500 futures, Gold and Brent along with EURUSD, EURJPY and GBPJPY which are the currency pairs I like to trade. Thankfully, mladen added an automatic yesterday function to the exporter which means you can set it and forget it.

When ThruDateTime is set to some date in the future, it will automatically output the current bar which is ideal for outputting out of sample data. Now why not do this in Rapidminer? Well you can using a split data function which uses a ratio to split your training data and out of sample data but this method gives you more granular approach to dealing with dates.

To summarise, you will need two export history indicators on your chart, one for training data and one for out of sample data. Setting the Thrudate to the future will output the current bar. Keep in mind that training everyday has its draw backs in time. There is also nothing to say that the model generated will be any better than yesterdays but a lot depends on the characteristics of your time series.

Storing to the repository.

Once you have your csv files in order, it is time to store them to the rapidminer repository. This you can do in one shot using multiple load/store operations. I have attached a sample script. Ignore the header names and assign your own. As long you you remember which column of data corresponds to which indicator.

Executing the script.

Storing the data to the repositories solves many issues with Rapidminer when it comes to how Rapidminer deals with metadata. The main building blocks of the script are outlined below but there are quite a few things to explain. Scripts can be nested to run in parallel and in sequence to make best use of multiprocessor machines. This script uses two linear svms for the highs and lows but they are isolated in the sense that the low values are excluded for the the svm that is looking for patterns in high values.

What affects the end result are the inputs and how the values of "C" are optimised for the svm. This will take more time to explain and it dependent on the time series you are looking at. The number of decimal places for example can change the way one optimises for "C".

Once the SVMs have finished calculating the High, Low and pivot values, the data is written to a csv file in the experts/files directory. At that point mladen's new importer will bring in the data to metatrader.

Here is an snap shot from a few minutes ago of S&P 500 futures data calculated early this morning using a value of C=100. Processing time was about 12 minutes. This is an an example of results that can be achieved using linear svms for intraday trading.

Thank you so much for starting this thread. I will work on getting this running and understanding it over the next 2-3 days. It certainly doesnt look easy and I already have a few questions on how to get it running but will try first more myself. Hope you will be fine for us to ask a few questions here.

Thanks from me too for starting this thread, there are some videos here may help for newbies in this like me, hopefully it won't change the direction your going Alex, if so please let me know and will remove the link.

The Neuralmarket Trends videos are good for setting up the basic structure. I would highly recommend the video on genetic optimization for setting parameters. The idea is to use a grid of values and have rapidminer select the best values. I suggest doing this for windowing and c values. I will dig out the scripts to do this.

My only disappointment with the time series tutorial there is that the prediction trend accuracy is close to random. I would also argue with idea that neural nets and svms can't predict actual numbers. That is in fact exactly what we are doing here. Now I would take prediction trend accuracy with huge grain of salt. It is useful to point to see if your model is not spitting out random numbers but for real world optimisation it is not enough. The above model produces a prediction trend accuracy of between 72%-77% but you still need to have an idea of your average true range. I know right away that if for example GPBJPY is giving me a range of 100 pips then there is a problem with the model even though the trend accuracy is high.

The key here is a bit of planning. It is very important to determine what you are trying to predict and how are you going to test the validity of your results afterwards. The goal here was to have the svm predict take profit targets, not necessarily turning points although that does happen often. In the end, the output is only useful in the context of the trading strategy you are using.

There are a lot of ideas to look at here and I hope that this basic idea could evolve in many different directions with a little bit of creativity.

For fun here is the data for EURUSD for today calculated at the Asian session open. If someone would like to test this on something other than EURUSD, let me know.

Looking at the big picture.

Not every currency pair responds well to this type of analysis all the time and there will be days like NFP where you can hit both the upside and downside targets in the same hour. Right now the S&P is conforming to the pattern prediction better than the forex pairs so that may be a better guide in some cases where futures are leading. Other things to look at are how many pairs and indexes are trading above their predicted pivot. Risk on!

1) I put Export History indicator in experts/indicators.

2) I put the EURUSD Projected Pivot txt file you posted today in expert/files (just doing this today before learning to use rapidminer)

3) I open a EURUSD chart and put Export History indicator on it. Do I have to put in EURUSD under Currency Pairs in input?

4) Don't we also have to put pivots hf indicator (attached here) onto chart?

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms.

Mustafa Onur Özorhan Email author İsmail Hakkı Toroslu Onur Tolga Şehitoğlu.

This paper addresses problem of predicting direction and magnitude of movement of currency pairs in the foreign exchange market. The study uses Support Vector Machine with a novel approach for input data and trading strategy. The input data contain technical indicators generated from currency price data (i. e., open, high, low and close prices) and representation of these technical indicators as trend deterministic signals. The input data are also dynamically adapted to each trading day with genetic algorithm. The study incorporates a currency strength-biased trading strategy which selects the best pair to trade from the available set of currencies and is an improvement over the previous work. The accuracy of the prediction models are tested across several different sets of technical indicators and currency pair sets, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using trend deterministic technical indicator signals mixed with raw data improves overall performance and dynamically adapting the input data to each trading period results in increased profits. Results also show that using a strength-biased trading strategy among a set of currency pair increases the overall prediction accuracy and profits of the models.

Communicated by V. Loia.

Compliance with ethical standards.

Conflict of interest.

The authors declare that they have no conflict of interest.

Ethical approval.

This article does not contain any studies with human participants or animals performed by any of the authors.

Formulae regarding computation of precision, recall, accuracy and F-measure.

Referências.

Informações sobre direitos autorais.

Autores e Afiliações.

Mustafa Onur Özorhan 1 Email author İsmail Hakkı Toroslu 1 Onur Tolga Şehitoğlu 1 1. Computer Engineering Department Middle East Technical University Ankara Turkey.

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Mineração de texto de novas manchetes para previsão de mercado de FOREX: Um Algoritmo de Redução de Dimensão de Múltiplas Camadas com semântica e sentimento.

Destaques.

A previsão de FOREX através da mineração de texto de notícias é viável e eficaz.

A seleção de recursos por abstração de hipermantes de palavras aumenta a precisão da previsão.

A ponderação de recursos com base na soma das pontuações de sentimento pos e neg é eficaz.

A redução de recursos com base na otimização máxima para predição-meta é crucial.

Neste artigo uma nova abordagem é proposta para prever movimentos direcionais intraday de um par de moedas no mercado de câmbio baseado no texto de quebra de notícias financeiras. A motivação por trás deste trabalho é dupla: primeiro, embora a previsão de mercado através de mineração de texto seja uma área de trabalho promissora na literatura, as abordagens de mineração de texto utilizadas nesta fase não são muito além das básicas, já que ainda é um campo emergente. Este trabalho é um esforço para colocar mais ênfase nos métodos de mineração de texto e abordar alguns aspectos específicos que são fracos em trabalhos anteriores, a saber: o problema da alta dimensionalidade, bem como o problema de ignorar sentimento e semântica ao lidar com a linguagem textual. . Esta pesquisa pressupõe que abordar esses aspectos da mineração de textos tem um impacto na qualidade dos resultados alcançados. O sistema proposto prova que esta suposição está correta. A segunda parte da motivação é pesquisar um mercado específico, a saber, o mercado de câmbio, que parece não ter sido pesquisado nos trabalhos anteriores com base na previsão de mineração de texto. Portanto, os resultados deste trabalho também demonstram com sucesso uma relação preditiva entre esse tipo de mercado específico e os dados textuais das notícias. Além dos dois componentes principais da motivação, existem outros aspectos específicos que tornam única a configuração do sistema proposto e o experimento conduzido, por exemplo, o uso apenas de manchetes de artigos de notícias e não de corpos de artigos de notícias, o que permite o uso de pequenos pedaços de texto, em vez de longos; ou o uso de notícias financeiras gerais sem qualquer filtragem adicional.

Para realizar o acima, este trabalho produz um algoritmo multicamadas que aborda cada um dos aspectos mencionados do problema de mineração de texto em uma camada designada. A primeira camada é denominada Camada de Abstração Semântica e aborda o problema da co-referência na mineração de texto que está contribuindo para a dispersão. Co-referência ocorre quando duas ou mais palavras em um corpus de texto se referem ao mesmo conceito. Este trabalho produz uma abordagem personalizada pelo nome de Heuristic-Hypernyms Feature-Selection, que cria uma maneira de reconhecer palavras com a mesma palavra-mãe para ser considerada como uma entidade. Como resultado, a precisão da previsão aumenta significativamente nessa camada, que é atribuída à redução de ruído apropriada do espaço de recurso.

A segunda camada é denominada Camada de Integração de Sentimentos, que integra a capacidade de análise de sentimento ao algoritmo, propondo um peso de sentimento pelo nome da SumScore que reflete o sentimento dos investidores. Além disso, essa camada reduz as dimensões eliminando aquelas que são de valor zero em termos de sentimento e, portanto, melhora a precisão da previsão.

A terceira camada engloba um algoritmo de criação de modelo dinâmico, denominado STFR (Synchronous Targeted Feature Reduction). É adequado para o desafio em questão, através do qual a mineração de um fluxo de texto está em causa. Atualiza os modelos com as informações mais recentes disponíveis e, mais importante, garante que as dimensões sejam reduzidas ao mínimo absoluto.

O algoritmo e cada uma de suas camadas são extensivamente avaliados usando dados reais de mercado e conteúdo de notícias ao longo de vários anos e provaram ser sólidos e superiores a qualquer outra solução comparável. As técnicas propostas implementadas no sistema resultam em precisão direcional significativamente alta de até 83,33%.

Em cima de um algoritmo multifacetado bem-arredondado, este trabalho contribui com uma estrutura de pesquisa muito necessária para esse contexto, com um banco de ensaios de dados que deve tornar os esforços futuros de pesquisa mais convenientes. O algoritmo produzido é escalável e seu design modular permite melhorias em cada uma de suas camadas em pesquisas futuras. Este artigo fornece amplos detalhes para reproduzir todo o sistema e os experimentos realizados.

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Melhores estratégias 4: Machine Learning.

Deep Blue foi o primeiro computador que ganhou um campeonato mundial de xadrez. Isso foi em 1996, e levou 20 anos até que outro programa, AlphaGo, pudesse derrotar o melhor jogador de Go humano. O Deep Blue era um sistema baseado em modelos com regras de xadrez hardwired. O AlphaGo é um sistema de mineração de dados, uma rede neural profunda treinada com milhares de jogos Go. Não melhorou hardware, mas um avanço no software foi essencial para a etapa de bater os melhores jogadores de xadrez para bater os melhores jogadores de Go.

Nesta quarta parte da minissérie, analisaremos a abordagem de mineração de dados para o desenvolvimento de estratégias de negociação. Este método não se preocupa com os mecanismos de mercado. Ele apenas verifica curvas de preços ou outras fontes de dados para padrões preditivos. Aprendizado de máquina ou & # 8220; Inteligência Artificial & # 8221; nem sempre está envolvido em estratégias de mineração de dados. Na verdade, o mais popular # 8211; e surpreendentemente rentável & # 8211; O método de mineração de dados funciona sem nenhuma rede neural sofisticada ou máquina de vetores de suporte.

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.2016 Predicting price direction using ANN & SVM: Kara. et. al.2011 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 “Better Strategies 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. Por que é que? 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.

Obrigado pelo bom 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 Mecânico

Negociação no mercado de câmbio usando estratégias de negociação mecânicas.

Using R in Algorithmic Trading: Back-testing a machine learning strategy that retrains every day.

On my last postВ we went into the world of machine learning with the building of a simple machine learning modelВ using a support vector machine to attempt to predict the daily returns of the UUP in R. В However failing to or being able to predict the return of a series is no indicative of actual trading success or failure since trading returns are clearly not homogeneous (not all bad days are the same and not all good days are the same). This means that you can have a machine learning technique that has quite low accuracy (below random chance) but it can still be successful if it tends to predict positively the days that have the highest returns. When using machine learning strategiesВ for trading it is not only a matter of what percentage of the time you’re right/wrong but how much money you make or lose when you trade. Due to this fact it becomes very important to test our models within an actual back-test, so that we can actually find out whether our machine learning algorithmic works, in practice, when trading (at least from a historical perspective). On today’s tutorial we’re going to learn exactly how to do that.

To do the above we are going to be needing the following R packages: e1071, quantmod and PerformanceAnalytics. Note that the packages need to be loaded in the exact order above, because we’re going to be using some functions that are defined on e1071 and PerformanceAnalytics but we want the PerformanceAnalytics functions (so you’ll get errors if you load PerformanceAnalytics first and the links to the function names get overwritten internally). Today we’re going to run a back-test on a gold ETF (GLD), attempting to predict it using an SVM. Our SVM model will retrain on every bar using the past X bars. First of all, we load the libraries and we then obtain the data for the GLD ETF using the quantmod getSymbols function. Once this is done we will be using a simple function created by this guyВ that allows us to create a data frame containing all the predictors in a very organized manner (I fully recommend the post linked before, which shows you how to create/test a classification model using an SVM on the SPY). I also changed this function to also return the plain return of the series so that I can assign it to another array and later use it to get the profit/loss for the strategy. Define the function first in R as detailed below:

Note that the function defines 17 different predictors that we use as inputs to predict a binary classifier (1 bullish, -1 bearish) which we will then use to train our SVM. Also notice how the function takes advantage of the ROC function and other nifty vector based functions that are an order of magnitude more efficient than the loop I shared with you on my last post. Once the function is defined we can then simply call it toВ populateВ a data frame called “data” . We are then going to create a data frame called “daily” and assign it the daily returns, after which we will be deleting them from the main data array (because having the return in the predictor array would lead to snooping).

After this we can now run the back-test for our system. What we will do first is choose a learning period (the algorithm will be trained with the past learningPeriod number of bars) and we then create an empty data frame called “results” where we will be saving the daily returns of our trading strategy. In the case below I have chosen a learning period of 200. After this we loop through all the points of our data frame from lerningPeriod+1 to the length of the array and on each point we’re going to create a training subset using a learningPeriod number of bars prior to the current data pointВ and we are then going to use it to train our SVM. After this we’re then going find the prediction of the newly built SVM model for the next return and we’re going to add a positive or negative return to our results array depending on whether our result matches or mismatches the actual real output class. Note that in any case (whether we’re right or wrong) I have subtracted 0.0001 from the return, which is the commission burden I have chosen to put on all trades (0.01% of trade volume). В I then use the chart functionality of the performance analytics library to display a graph of trading progress every 200 bars.

. , data = efTrain , cost = 100 , gamma = 0.1 )

After this is finished we will have a graph showing the results of the system in a format given by the PerformanceAnalytics package. Since you have an array of returns in xts format you will also be able to invoke any of the functions within this package to further analyze the statistics of your trading system. The image below shows you the return we have obtained for this strategy on the GLD ETF, we can see that the machine learning technique used was not able to properly predict directionality in a large variety of cases. This shows, as we also saw on our previous post, that achieving profitable returns using machine learning procedures is not very easy (even when using as many inputs as we have used here).

However by choosing inputs a bit better, doing some data pre-processing and modifying the gamma and c characteristics of the SVM we can indeed obtain some profitable results for the daily retrained SVM on GLD as showed below(note that the y axis is logarithmic). By using ensemble techniques, including several different machine learning algorithms and choosing even better inputs we can improve the results below even further, however we’ll leave this discussion for future posts.

If you would like to learn more about machine learning techniques and how you too can create your own systems that re-train on every bar and give historically profitable resultsВ please consider joiningВ Asirikuy, a website filled with educational videos, trading systems, development and a sound, honest and transparent approach towards automated trading in general . Espero que tenha gostado deste artigo ! : o)

4 Responses to “Using R in Algorithmic Trading: Back-testing a machine learning strategy that retrains every day”

i am a bit puzzled. From your previous posts you seemed to be few steps further than this post. What happened to your own framework for machine learning strategy creation and how are you guys with the GPU approach on the lower time frames. I recall you concluded that there is more potential in the intra day strategies (which much lower drawdowns). I would be curious to see an update there :-) So to me your post on R strategy building on the DTF seems like a result from some drawbacks on your former track? I cross fingers that is not the case..

Greetings and still thanks for this “howto”

Btw, in case u never stumbled upon this article:

sure, I read it quite some time ago. Muito interessante.

Thanks for your post :o) Don’t worry, we continue to research machine learning on the lower time frames and the creation of systems using data-mining and GPUs. We’re making some good advances in both of these fields and our cloud data-mining is already generating many promising systems (many of them already under live testing). These articles are meant as tutorials for those who don’t have access to our tools but wish to carry out some machine learning experiments using R with freely available data and software. Please don’t take all article I post as the “state of the art” of what we are doing, many are meant as educational tools for those who want to start their journey into our research paths. Obrigado novamente por postar