Quantitative Finance :Global macro trading strategy using Probabilistic Graphical Models

{ This is a handson workshop in pgmpy package. The creator of pgmpy package Abinash Panda will do the code demonstration }

Crude oil plays an important role in the macroeconomic stability and it heavily influences the performance of the global financial markets. Unexpected fluctuations in the real price of crude oil are detrimental to the welfare of both oil-importing and oil-exporting economies.Global macro hedge-funds view forecast the price of oil as one of the key variables in generating macroeconomic projections and it also plays an important role for policy makers in predicting recessions.

Probabilistic Graphical Models can help in improving the accuracy of existing quantitative models for crude oil price prediction as it takes in to account many different macroeconomic and geopolitical variables .

Hidden Markov Models are used to detect underlying regimes of the time-series data by discretising the continuous time-series data. In this workshop we use Baum-Welch algorithm for learning the HMMs, and Viterbi Algorithm to find the sequence of hidden states (i.e. the regimes) given the observed states (i.e. monthly differences) of the time-series.

Belief Networks are used to analyse the probability of a regime in the Crude Oil given the evidence as a set of different regimes in the macroeconomic factors . Greedy Hill Climbing algorithm is used to learn the Belief Network, and the parameters are then learned using Bayesian Estimation using a K2 prior. Inference is then performed on the Belief Networks to obtain a forecast of the crude oil markets, and the forecast is tested on real data.

 
 

Outline/Structure of the Workshop

Theory:(30 minute)

Brief Introduction to the Crude Oil Price Prediction Problem

Identification of Macro Economic Factors influencing the Energy Markets

Refresher : Hidden Markov Model and Bayesian Networks

Handson (1 hour) - pgmpy package

Data Retrieval from the EIA and FRED

Data Preprocessing

Regime detection model using Hidden Markov Models

Learning the macroeconomic structure of the oil markets using hill-climbing structural learning.

Testing the constructed model by simulating trades

Learning Outcome

The audience will learn how to construct a macro trading model for crude oil price forecasting by representing structural and macroeconomic changes in the oil market by using Bayesian Networks and HMM .

Target Audience

Quantitative Finance researchers, Algorithmic Trading practioners , Financial Analyst, Data Scientists, financial data scientists, Probabilistic programmers, Statisticians, Machine Learnign Engineers, Deep Learning Engineers,PGM experts.

Prerequisites for Attendees

Basic Understanding of Bayesian Networks is preferred ,not Mandatory though.

Prior programming experience in Python preferred.

Video


schedule Submitted 4 years ago

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  • Dat Tran
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    Dat Tran - Image ATM - Image Classification for Everyone

    Dat Tran
    Dat Tran
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    schedule 4 years ago
    Sold Out!
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  • Badri Narayanan Gopalakrishnan
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    Juan Manuel Contreras
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    schedule 4 years ago
    Sold Out!
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  • Ramanathan R
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    Ramanathan R / Gurram Poorna Prudhvi - Time Series analysis in Python

    240 Mins
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    “Time is precious so is Time Series Analysis”

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    Anuj Gupta - Natural Language Processing Bootcamp - Zero to Hero

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  • Akshay Bahadur
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    Akshay Bahadur - Minimizing CPU utilization for deep networks

    Akshay Bahadur
    Akshay Bahadur
    SDE-I
    Symantec Softwares
    schedule 4 years ago
    Sold Out!
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  • Dr. Atul Singh
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    45 Mins
    Talk
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    Favio Vázquez - Complete Data Science Workflows with Open Source Tools

    90 Mins
    Tutorial
    Beginner

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  • Suvro Shankar Ghosh
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    Suvro Shankar Ghosh - Real-Time Advertising Based On Web Browsing In Telecom Domain

    45 Mins
    Case Study
    Intermediate

    The following section describes Telco Domain Real-time advertising based on browsing use case in terms of :

    • Potential business benefits to earn.
    • Functional use case architecture depicted.
    • Data sources (attributes required).
    • Analytic to be performed,
    • Output to be provided and target systems to be integrated with.

    This use case is part of the monetization category. The goal of the use case is to provide a kind of DataMart to either Telecom business parties or external third parties sufficient, relevant and customized information to produce real-time advertising to Telecom end users. The customer targets are all Telecom network end-users.

    The customization information to be delivered to advertise are based on several dimensions:

    • Customer characteristics: demographic, telco profile.
    • Customer usage: Telco products or any other interests.
    • Customer time/space identification: location, zoning areas, usage time windows.

    Use case requirements are detailed in the description below as “ Targeting method”

    1. Search Engine Targeting:

    The telco will use users web history to track what users are looking at and to gather information about them. When a user goes onto a website, their web browsing history will show information of the user, what he or she searched, where they are from, found by the ip address, and then build a profile around them, allowing Telco to easily target ads to the user more specifically.

    1. Content and Contextual Targeting:

    This is when advertisers can put ads in a specific place, based on the relative content present. This targeting method can be used across different mediums, for example in an article online, about purchasing homes would have an advert associated with this context, like an insurance ad. This is achieved through an ad matching system which analyses the contents on a page or finds keywords and presents a relevant advert, sometimes through pop-ups.

    1. Technical Targeting

    This form of targeting is associated with the user’s own software or hardware status. The advertisement is altered depending on the user’s available network bandwidth, for example if a user is on their mobile phone that has a limited connection, the ad delivery system will display a version of the ad that is smaller for a faster data transfer rate.

    1. Time Targeting:

    This type of targeting is centered around time and focuses on the idea of fitting in around people’s everyday lifestyles. For example, scheduling specific ads at a timeframe from 5-7pm, when the

    1. Sociodemographic Targeting:

    This form of targeting focuses on the characteristics of consumers, including their age, gender, and nationality. The idea is to target users specifically, using this data about them collected, for example, targeting a male in the age bracket of 18-24. The telco will use this form of targeting by showing advertisements relevant to the user’s individual demographic profile. this can show up in forms of banner ads, or commercial videos.

    1. Geographical and Location-Based Targeting:

    This type of advertising involves targeting different users based on their geographic location. IP addresses can signal the location of a user and can usually transfer the location through different cells.

    1. Behavioral Targeting:

    This form of targeted advertising is centered around the activity/actions of users and is more easily achieved on web pages. Information from browsing websites can be collected, which finds patterns in users search history.

    1. Retargeting:

    Is where advertising uses behavioral targeting to produce ads that follow you after you have looked or purchased are a particular item. Retargeting is where advertisers use this information to ‘follow you’ and try and grab your attention so you do not forget.

    1. Opinions, attitudes, interests, and hobbies:

    Psychographic segmentation also includes opinions on gender and politics, sporting and recreational activities, views on the environment and arts and cultural issues.

  • 45 Mins
    Talk
    Intermediate

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    One of the biggest challenges we faced was to generate predictions for more than 300 million images within a short time while keeping the costs low. Moreover, a resolution for the scaling problem became critical since we intended to apply other Deep Learning models on the same big dataset. We ended up formulating a batch-prediction solution by employing an Apache Spark setup that ran on an AWS EMR cluster.

    Spark is notorious for being difficult to configure and tune. As a result, we had to carry on several optimisation steps in order to meet the scale requirements that adhered to our time and financial constraints. In this talk, I would present our Spark setup and focus on the journey of optimising the Spark tagging solution. Additionally, I would also talk briefly about the underlying deep learning model which was used to predict the image tags.

  • 45 Mins
    Case Study
    Beginner

    With the rise of cloud, distributed architectures, containers, and microservices, a rise in data overload is visible. With growing amounts of DevOps processes; alerts, repeated mundane jobs etc. have put new demands to both synthesize meaning from this influx of information and connect it to broader business objectives.

    AIOps is the application of artificial intelligence for IT operations. AIOps uses machine learning and data science to give IT operations teams a real-time understanding of any issues affecting the availability or performance of the systems under their care. Rather than reacting to issues as they arise in the application environment, AIOps platforms allow IT operations teams to proactively manage performance challenges faster, and in real-time

    This case study focuses on solving the following business needs:

    1. With an ever-increasing rise in alerts, a large number of incidents were getting generated. There was a need to develop a framework that can generate correlations and identify correlated events, thereby reduce overall incidents volume.

    2. For many incidents a reactive strategy does not work and can lead to a loss of reputation; there was a need to develop predictive capabilities that can detect anomalous events and predict critical events well in advance.

    3. Given the pressures of reducing the Resolution time and short window of opportunity available to the analysts, there was a need to provide search capabilities so that the analysts can have a head start as to how similar incidents were solved in past.

    Data from multiple systems sending alerts, including traditional IT monitoring, log events in text format, application and network performance data etc were made available for the PoC.

    The solution framework developed had a discovery phase where the base data was visualized and explored, a NLP driven text mining layer where log data in text format was pre-processed, clustered and correlations were developed to identify related events using Machine Learning algorithms. Topic Mining was used to get a quick overview of a large number of event data. Next, a temporal mining layer explored the temporal relationship between nodes and cluster groups, necessary features were developed on top of the associations generated from temporal layers. Advanced Machine learning algorithms were then developed on these features to predict critical events almost 12 hours in advance. Last but not the least a search layer that computed the similarity of any incident with those in Service Now database was developed that provided analysts insights readily available information on similar incidents and how they were solved in past so that the analysts do not have to reinvent the wheel.

  • Shalini Sinha
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    Shalini Sinha / Ashok J / Yogesh Padmanaban - Hybrid Classification Model with Topic Modelling and LSTM Text Classifier to identify key drivers behind Incident Volume

    45 Mins
    Case Study
    Intermediate

    Incident volume reduction is one of the top priorities for any large-scale service organization along with timely resolution of incidents within the specified SLA parameters. AI and Machine learning solutions can help IT service desk manage the Incident influx as well as resolution cost by

    • Identifying major topics from incident description and planning resource allocation and skill-sets accordingly
    • Producing knowledge articles and resolution summary of similar incidents raised earlier
    • Analyzing Root Causes of incidents and introducing processes and automation framework to predict and resolve them proactively

    We will look at different approaches to combine standard document clustering algorithms such as Latent Dirichlet Allocation (LDA) and K-mean clustering on doc2vec along-with Text classification to produce easily interpret-able document clusters with semantically coherent/ text representation that helped IT operations of a large FMCG client identify key drivers/topics contributing towards incident volume and take necessary action on it.

  • Saikat Sarkar
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    Saikat Sarkar / Dhanya Parameshwaran / Dr Sweta Choudhary / Srikanth Ramaswamy / Usha Rengaraju - AI meets Neuroscience

    480 Mins
    Workshop
    Advanced

    This is a mixer workshop with lot of clinicians , medical experts , Neuroimaging experts ,Neuroscientists, data scientists and statisticians will come under one roof to bring together this revolutionary workshop.

    The theme will be updated soon .

    Our celebrity and distinguished presenter Srikanth Ramaswamy who is an advisor at Mysuru Consulting Group and also works Blue Brain Project at the EPFL will be delivering an expert talk in the workshop.

    https://www.linkedin.com/in/ramaswamysrikanth/

    { This workshop will be a combination of panel discussions , expert talk and neuroimaging data science workshop ( applying machine learning and deep learning algorithms to Neuroimaging data sets}

    { We are currently onboarding several experts from Neuroscience domain --Neurosurgeons , Neuroscientists and Computational Neuroscientists .Details of the speakers will be released soon }

    Abstract for the Neuroimaging Data Science Part of the workshop:

    The study of the human brain with neuroimaging technologies is at the cusp of an exciting era of Big Data. Many data collection projects, such as the NIH-funded Human Connectome Project, have made large, high- quality datasets of human neuroimaging data freely available to researchers. These large data sets promise to provide important new insights about human brain structure and function, and to provide us the clues needed to address a variety of neurological and psychiatric disorders. However, neuroscience researchers still face substantial challenges in capitalizing on these data, because these Big Data require a different set of technical and theoretical tools than those that are required for analyzing traditional experimental data. These skills and ideas, collectively referred to as Data Science, include knowledge in computer science and software engineering, databases, machine learning and statistics, and data visualization.

    The workshop covers Data analysis, statistics and data visualization and applying cutting-edge analytics to complex and multimodal neuroimaging datasets . Topics which will be covered in this workshop are statistics, associative techniques, graph theoretical analysis, causal models, nonparametric inference, and meta-analytical synthesis.

  • 90 Mins
    Workshop
    Beginner

    This will be a hands-on workshop how to build a custom interactive dashboard application on your local machine or on any cloud service provider. You will also learn how to deploy this application with both security and scalability in mind.

    Powerful Data visualization software solutions are extremely useful when building interactive data visualization dashboards. However, these types of solutions might not provide sufficient customization options. For those scenarios, you can use open source libraries like D3.js, Chart.js, or Bokeh to create custom dashboards. While these libraries offer a lot of flexibility for building dashboards with tailored features and visualizations.

  • 45 Mins
    Demonstration
    Intermediate

    Recent advancements in AI are proving beneficial in development of applications in various spheres of healthcare sector such as microbiological analysis, discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating a large-scale data into improved human healthcare. Automation in healthcare using machine learning/deep learning assists physicians to make faster, cheaper and more accurate diagnoses.

    Due to increasing availability of electronic healthcare data (structured as well as unstructured data) and rapid progress of analytics techniques, a lot of research is being carried out in this area. Popular AI techniques include machine learning/deep learning for structured data and natural language processing for unstructured data. Guided by relevant clinical questions, powerful deep learning techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.

    We have successfully developed three deep learning based healthcare applications using TensorFlow and are currently working on three more healthcare related projects. In this demonstration session, first we shall briefly discuss the significance of deep learning for healthcare solutions. Next, we will demonstrate two deep learning based healthcare applications developed by us. The discussion of each application will include precise problem statement, proposed solution, data collected & used, experimental analysis and challenges encountered & overcame to achieve this success. Finally, we will briefly discuss the other applications on which we are currently working and the future scope of research in this area.

  • Shrutika Poyrekar
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    Shrutika Poyrekar / kiran karkera / Usha Rengaraju - Introduction to Bayesian Networks

    90 Mins
    Workshop
    Advanced

    { This is a handson workshop . The use case is Traffic analysis . }

    Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein edges represent the conditional probability factor in the factorized probability distribution. Thus Bayesian Networks provide a compact representation for dealing with uncertainty using an underlying graphical structure and the probability theory. These models have a variety of applications such as medical diagnosis, biomonitoring, image processing, turbo codes, information retrieval, document classification, gene regulatory networks, etc. amongst many others. These models are interpretable as they are able to capture the causal relationships between different features .They can work efficiently with small data and also deal with missing data which gives it more power than conventional machine learning and deep learning models.

    In this session, we will discuss concepts of conditional independence, d- separation , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.

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