{"id":31373,"date":"2017-06-09T19:24:07","date_gmt":"2017-06-09T16:24:07","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=31373"},"modified":"2020-02-20T00:14:14","modified_gmt":"2020-02-19T21:14:14","slug":"tensorflow-for-foreign-exchange-market-analyzing-time-series-data","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/","title":{"rendered":"TensorFlow for Foreign Exchange Market: Analyzing Time-Series Data"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/#Latent_variable_models\" >Latent variable models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/#Implementation_with_TensorFlow\" >Implementation with TensorFlow<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/#Analyzing_FX_market_data\" >Analyzing FX market data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/#Want_details_Watch_the_video\" >Want details? Watch the video!<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/#Related_slides\" >Related slides<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/#Further_reading\" >Further reading<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/#About_the_expert\" >About the expert<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Latent_variable_models\"><\/span>Latent variable models<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>During <a href=\"https:\/\/www.altoros.com\/blog\/tag\/tensorbeat\/\">TensorBeat 2017<\/a>, <a href=\"https:\/\/www.linkedin.com\/in\/daniel-egloff-37a34010\/\" target=\"_blank\" rel=\"noopener noreferrer\">Daniel Egloff<\/a> looked into the value brought by deep learning solutions to the financial sector. He shared his experience of building a generative model for <a href=\"https:\/\/www.altoros.com\/blog\/tensorflow-in-finance-discussing-predictive-analytics-and-budget-planning\/\">financial time-series data<\/a> and demonstrated how to implement it with TensorFlow. Daniel noted that this kind of models\u2014for example, <a href=\"https:\/\/www.altoros.com\/blog\/the-diversity-of-tensorflow-wrappers-gpus-generative-adversarial-networks-etc\/\">generative adversarial networks<\/a> (GANs) or <a href=\"https:\/\/en.wikipedia.org\/wiki\/Autoencoder#Variational_autoencoder_.28VAE.29\"  target=\"_blank\" rel=\"noopener noreferrer\">variational autoencoders<\/a> (VAEs)\u2014is quite a recent innovation in deep learning.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/daniel-egloff-tensorbeat-2017-tensorflow-for-finance-v12.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/daniel-egloff-tensorbeat-2017-tensorflow-for-finance-v12-1024x678.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31375\" \/><\/a><\/p>\n<blockquote><p><em>\u201cIn finance, deep learning is complementary to the existing models, not a replacement.\u201d<br \/>\n\u2014Daniel Egloff, QuantAlea<\/em><\/p><\/blockquote>\n<p>So, how does deep learning bring value to finance?<\/p>\n<ul>\n<li>Richer functional relationship between explanatory and response variables<\/li>\n<li>Model complicated interactions<\/li>\n<li>Automatic feature discovery<\/li>\n<li>Capable to handle large amounts of data<\/li>\n<li>Standard training procedures with backpropagation and stochastic gradient descent<\/li>\n<li>A variety of frameworks and tooling<\/li>\n<\/ul>\n<p>In this context, Daniel overviewed <a href=\"https:\/\/en.wikipedia.org\/wiki\/Latent_variable_model\" target=\"_blank\" rel=\"noopener noreferrer\">latent variable models<\/a>, which allow to:<\/p>\n<ul>\n<li>Better explain data distribution<\/li>\n<li>Postulate prior distribution<\/li>\n<li>Define the likelihood of data as a latent variable function<\/li>\n<li>Infer distribution of latent variable given data<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/deep-learning-in-finance-tensorflow-latent-variable-model-v12.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/deep-learning-in-finance-tensorflow-latent-variable-model-v12.png\" alt=\"\" width=\"640\" class=\"aligncenter size-full wp-image-31376\" \/><\/a><\/p>\n<p>A latent variable can be thought of as an encoded representation, where the likelihood serves as a decoder and a posterior as an encoder.<\/p>\n<p>Dealing with maximum likelihood, one may face that a marginal and a posterior are intractable and their calculation suffers from exponential complexity. This can be addressed with <a href=\"https:\/\/en.wikipedia.org\/wiki\/Markov_chain\" target=\"_blank\" rel=\"noopener noreferrer\">Markov chain<\/a>, <a href=\"https:\/\/en.wikipedia.org\/wiki\/Hybrid_Monte_Carlo\" target=\"_blank\" rel=\"noopener noreferrer\">Hamiltonian Monte Carlo<\/a> algorithm, and approximation\/variational inference.<\/p>\n<p>To enable approximation with variational autoencoders:<\/p>\n<ul>\n<li>Assume latent space with prior distribution<\/li>\n<li>Parameterize the likelihood with a deep neural network<\/li>\n<li>Approximate intractable posterior with a deep neural network<\/li>\n<li>Learn the parameters with <a href=\"https:\/\/en.wikipedia.org\/wiki\/Backpropagation\" target=\"_blank\" rel=\"noopener noreferrer\">backpropagation<\/a><\/li>\n<\/ul>\n<p>When applying it to time-series data, Daniel employed <a href=\"https:\/\/en.wikipedia.org\/wiki\/Gaussian_function\" target=\"_blank\" rel=\"noopener noreferrer\">the Gaussian function<\/a>, its distributions (the parameters are calculated from a <a href=\"https:\/\/www.altoros.com\/blog\/recurrent-neural-networks-classifying-diagnoses-with-long-short-term-memory\/\">recurrent neural network<\/a>), and variational inference (to train a model). When embedding time-series data, he used a lag of 20 to 60 historical observations at each time step. <\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/daniel-egloff-quant-alea-tensorbeat-2017-v12.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/daniel-egloff-quant-alea-tensorbeat-2017-v12-1024x678.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31377\" \/><\/a><\/p>\n<p>&nbsp; <\/p>\n<h3><span class=\"ez-toc-section\" id=\"Implementation_with_TensorFlow\"><\/span>Implementation with TensorFlow<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>To run the process of training a TensorFlow-based model, P100 GPUs were chosen. (However, long time-series and large batch sizes require substantial GPU memory. We\u2019ve previously written how <a href=\"https:\/\/www.altoros.com\/blog\/tensorflow-in-the-cloud-accelerating-resources-with-elastic-gpus\/\">to tackle<\/a> such a problem.)<\/p>\n<p>So, to unroll a recurrent neural network (RNN), <a href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/nn\/dynamic_rnn\" target=\"_blank\" rel=\"noopener noreferrer\">tf.nn.dynamic_rnn<\/a> may be used as it is simple to work with and handles variable sequence length. Though, it is not flexible enough for generative networks. To enjoy more flexibility, one may use <a href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/while_loop\" target=\"_blank\" rel=\"noopener noreferrer\">tf.while_loop<\/a>. Still, it comes in more coding and requires a comprehensive understanding of control structures.<\/p>\n<p>Daniel then demonstrated the implementation at the code level, which comprises the following steps:<\/p>\n<ol>\n<li>Variable and weight setup<\/li>\n<li>Allocation of TensorArray objects<\/li>\n<li>Filling input TensorArray objects with data<\/li>\n<li>While loop body inference (updating the inference RNN state and the generator RNN state)<\/li>\n<li>Calling a while loop<\/li>\n<li>Stacking TensorArray objects<\/li>\n<li>Loss calculation<\/li>\n<\/ol>\n<p>The code samples are available in the slide deck below.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Analyzing_FX_market_data\"><\/span>Analyzing FX market data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>How is it applicable to real-life needs of the financial sector? Daniel demonstrated how deep learning can help out  foreign exchange (FX) market.<\/p>\n<p>Operating 24 hours, five and a half days a week, the world\u2019s largest and most liquid market is a perpetual fierce competition between the participants. The FX market is decentralized, which means there is no need to go through a centralized exchange and a price for a particular currency can change any second. The market\u2019s <a href=\"https:\/\/www.bis.org\/publ\/rpfx16.htm\" target=\"_blank\" rel=\"noopener noreferrer\">daily turnover<\/a> is equal to $5.1 trillion in 2016, with the peak turnover registered in 2013\u2014$5.3 trillion, which is a bit more than $220 billion per hour.<\/p>\n<p>With a humongous data flow, a common practice Daniel shared is collecting tick data of the overlapping US and European session. The reason for that is simple as the trading activity is the highest, offering valuable data sources to drive insights from. So, the model is trained on this fresh data captured and is further applied to the rest of the US session.<\/p>\n<p>Daniel then showed a 10-minute data flow sampled and aggregated in an <a href=\"https:\/\/en.wikipedia.org\/wiki\/Open-high-low-close_chart\" target=\"_blank\" rel=\"noopener noreferrer\">open-high-low-close chart<\/a>.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/deep-learning-in-finance-tensorflow-foreign-exchange-market-data-sample-v12.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/deep-learning-in-finance-tensorflow-foreign-exchange-market-data-sample-v12.png\" alt=\"\" width=\"640\" class=\"aligncenter size-full wp-image-31378\" \/><\/a><\/p>\n<p>As can be seen, there are quite big jumps in price moves in the chart. To get value out of this info, one may normalize data with <a href=\"https:\/\/en.wikipedia.org\/wiki\/Standard_deviation\" target=\"_blank\" rel=\"noopener noreferrer\">the standard deviation<\/a> over a training interval. In 2016, there were 260 trading days, and the team trained a model per day.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/deep-learning-in-finance-tensorflow-foreign-exchange-market-volatility-of-prediction-v12.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/deep-learning-in-finance-tensorflow-foreign-exchange-market-volatility-of-prediction-v12.png\" alt=\"\" width=\"640\" class=\"aligncenter size-full wp-image-31379\" \/><\/a><\/p>\n<p>Thus, the solution helps to track the pace the FX market moves at, analyze peak vs. recession times, observe the trends, and make predictions based on this information.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Want_details_Watch_the_video\"><\/span>Want details? Watch the video!<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><center><script src=\"https:\/\/fast.wistia.com\/embed\/medias\/za3nz8hyd3.jsonp\" async><\/script><script src=\"https:\/\/fast.wistia.com\/assets\/external\/E-v1.js\" async><\/script><\/p>\n<div class=\"wistia_embed wistia_async_za3nz8hyd3\" style=\"height:358px;width:640px\">&nbsp;<\/div>\n<p><\/center><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Related_slides\"><\/span>Related slides<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><center><iframe loading=\"lazy\" src=\"\/\/www.slideshare.net\/slideshow\/embed_code\/key\/2UAWAlbRxhP164\" width=\"595\" height=\"485\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" style=\"border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;\" allowfullscreen> <\/iframe><\/center><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/tensorflow-in-finance-discussing-predictive-analytics-and-budget-planning\/\">TensorFlow in Finance: Discussing Predictive Analytics and Budget Planning<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/learning-financial-data-and-recognizing-images-with-tensorflow-and-neural-networks\/\">Learning Financial Data and Recognizing Images with TensorFlow and Neural Networks<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/research-papers\/performance-of-distributed-tensorflow-a-multi-node-and-multi-gpu-configuration\/\">Performance of Distributed TensorFlow: A Multi-Node and Multi-GPU Configuration<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"About_the_expert\"><\/span>About the expert<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><small><a href=\"https:\/\/www.linkedin.com\/in\/daniel-egloff-37a34010\/\" target=\"_blank\" rel=\"noopener noreferrer\">Daniel Egloff<\/a> is a partner at InCube Group and Managing Director of QuantAlea, a Swiss software engineering company specialized in GPU software development. He studied mathematics, theoretical physics, and computer science. Daniel has 15+ years of experience as a quant in the financial service industry.<\/small><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Latent variable models<\/p>\n<p>During TensorBeat 2017, Daniel Egloff looked into the value brought by deep learning solutions to the financial sector. He shared his experience of building a generative model for financial time-series data and demonstrated how to implement it with TensorFlow. Daniel noted that this kind of models\u2014for example, generative [&#8230;]<\/p>\n","protected":false},"author":3,"featured_media":31384,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[214],"tags":[972,748,916,749],"class_list":["post-31373","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tutorials","tag-finance","tag-machine-learning","tag-tensorbeat","tag-tensorflow"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>TensorFlow for Foreign Exchange Market: Analyzing Time-Series Data | Altoros<\/title>\n<meta name=\"description\" content=\"This blog post features some insights on building a generative model for financial time-series data and implementing it with TensorFlow.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TensorFlow for Foreign Exchange Market: Analyzing Time-Series Data | Altoros\" \/>\n<meta property=\"og:description\" content=\"Latent variable models During TensorBeat 2017, Daniel Egloff looked into the value brought by deep learning solutions to the financial sector. He shared his experience of building a generative model for financial time-series data and demonstrated how to implement it with TensorFlow. Daniel noted that this kind of models\u2014for example, generative [...]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/\" \/>\n<meta property=\"og:site_name\" content=\"Altoros\" \/>\n<meta property=\"article:published_time\" content=\"2017-06-09T16:24:07+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-02-19T21:14:14+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data-tensorbeat-2017-daniel-egloff.gif\" \/>\n\t<meta property=\"og:image:width\" content=\"640\" \/>\n\t<meta property=\"og:image:height\" content=\"454\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/gif\" \/>\n<meta name=\"author\" content=\"Sophia Turol\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sophia 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