Global macro trading strategy using PGM(pgmpy package)
usha rengaraju (~usha75) |
The python library used is pgmpy package . The workshop proposal falls under both scientific computing and Data Science Category
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::
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 practitioners , Financial Analyst, Data Scientists, financial data scientists, Probabilistic programmers, Statisticians, Machine Learning 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.
Colab Notebook for the workshop : https://colab.research.google.com/drive/1KU5d7BHo1W-ViNmbzpkKWjCJffZXxDEx
Github : https://github.com/abinashpanda
Usha Rengaraju :
I am a polymath and unicorn data scientist with strong foundations in Economics, Finance, Business Foundations, Business Analytics and Psychology. I specialize in Probabilistic Graphical Models, Machine Learning and Deep Learning. I have completed Financial Engineering and Risk Management program from Columbia University with top honors, micromasters in Marketing Analytics from UC Berkeley and statistical analysis in Life Sciences specialization from Harvard. I am chapter lead/Co-Organizer of Women in Machine Learning and Data Science Bengaluru Chapter and Core oganizing team member at WIDS Bengaluru .I have around 6 years of technical experience working in various companies like Infosys, Temenos, NeoEYED and Mysuru Consulting Group. I am part of dedicated group of experts and enthusiasts who explore Coursera courses before they open to the public, an ambassador at AIMed (an initiative which brings together physicians and AI experts), part time Data science instructor, mentor at GLAD (gladmentorship.com), mentor at JobsForHer and volunteer at Statistics without Borders. I developed the course curriculum for Probabilistic Graphical Models @ Upgrad which is taught by Professor Srinivasa Raghavan from IIIT Bangalore.
Abinash Panda : Founder of Pgmpy Package :
Abinash is the Co -Founder of a startup -Prodios and has been a data scientist for more than 4 years. He has worked in multiple early stage startups and helped them build their data analytics pipeline. He love to munge, plot and analyse data. He has been a speaker at several Python conferences.
Abinash Panda has written two books in Probabilistic Graphical Models and HMM
He is the founding member and significantly contributed to pgmpy package.
This proposal got accepted at DevConf 2019(RedHat Summit) and also is conducted as part of ODSC event.
Speaker at World Machine Learning Summit 2018: https://1point21gws.com/machinelearning/bangalore/schedule.html#day1
Speaker at Google Cloud Next 18:
Session on Neuroeconomics -Neuroscience of Decision Making
Workshop at ODSC -Introduction to Bayesian Networks :
Speaker at BRUG (Time series Analysis):
Speaker at BangPypers: (Introduction to Probabilistic Graphical Models")
Speaker at PyLadies : https://twitter.com/aartee_ty/status/1066228144003264512
Workshop in Stock Price Prediction using Probabilistic Graphical Models https://www.meetup.com/Byte-Academy-Bangalore/events/256804439/
Webinar in Stock Price Prediction using Probabilistic Graphical Models https://www.meetup.com/Byte-Academy-Bangalore/events/fznzmqyzcbfb/