Python Modeling in Finance - Intermediate

Fitch Learning
Training overview
Professional Course
2 days

Course description

*With many businesses now working from home, we have introduced virtual learning so we can continue to deliver high quality training to the financial community as we accommodate this new way of working. Since 2003, Fitch Learning has been delivering virtual learning programs to clients and learners. Building on this extensive experience, we are now able to offer a range of public courses in a live online environment, whilst ensuring you will get the same value as you would in our classroom courses.

Python Modeling in Finance - Intermediate

This course offers a continuation of the introductory course in Python by providing a more in-depth examination of key packages within the context of financial applications. In particular, it explores in further detail the Pandas data analysis package, using Python optimization tools to calibrate models to data, and gives an overview of the scikit-learn predictive modeling package.

This course gives 16 CPD hours.

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Who should attend?

This course is ideal for financial analysts, business analysts, portfolio analysts, quantitative analysts, risk managers, model validators, quantitative developers and information systems professionals. Taking either the beginner level course or having a basic understanding of the Python language is a prerequisite to attending this course. We expect participants to have a basic knowledge of finance and basic notions of programming as well.

Training content

Day One


  • Briefly review Python IDE options/setup and package management
  • Survey finance use cases of major Python packages including pandas, NumPy, stats models, matplotlib, and scikit-learn
  • Review Python plotting options including matplotlib and seaborn
  • Review file input/output options


  • Series, Data Frames, and Panels: The central pandas objects
  • How to create Data Frames from files and other built in Python containers
  • Filtering, interpolating, and wrangling data in Pandas Data Frames
  • Rolling Operations on pandas Data Frames, e.g. moving average, volatility, etc.
  • Combining and demonstrating SQL style operations on Data Frames

Pandas Applications to Finance

  • Calculating daily returns, volatility, Sharpe ratio and VaR values
  • Applying these calculations on a rolling basis
  • Visualizing output using the pandas graphing library

Asset Allocation

  • Storing equity price data in a pandas Data Frame
  • Computing daily returns, covariance matrix estimation
  • Defining the Markowitz portfolio objective function
  • Using Python based convex optimizers to determine the optimal Markowitz portfolio

Day Two

Option Pricing and Model Calibration

  • Review the implementation of a black scholes option pricer
  • Discuss how to calibrate the model volatility parameter from underlying stock price data
  • Next consider the Heston stochastic volatility model and discuss the associated calibration procedure
  • Develop plotting and reporting functions to analyze differences between these pricing models

Monte Carlo Applications

  • Simulated synthetic price time series of stocks using Ito processes, and time series models, e.g. ARCH, GARCH
  • Discuss examples of European and American option pricing using these time series
  • Develop a Monte Carlo yield curve simulation
  • Compute expected values of fixed income securities using these simulation results
  • Examine variance reduction methods and further MC refinements

Developing Hedging Strategies in Python

  • Review the main aims of hedging security selection and sizing
  • Implement a standard minimum variance hedge and apply to a long-short equity portfolio
  • Develop an optimization framework for finding optimal hedges that minimize overall portfolio return distribution variance and VaR
  • Compare/contrast these different hedging techniques

Developing Predictive Models in Python

  • Provide a high-level overview with classification, regression, clustering and cross validation examples of the scikit-learn machine learning package
  • Demonstrate how one can extend linear regression models to LASSO, ridge, SVM regression, etc. which can result in models with enhanced predictive performance
  • Review the scikit-learn framework for model and parameter selection and model validation
  • Discuss clustering techniques, e.g. k-means, hierarchical clustering, and provide an associated equity clustering application

About Fitch Learning

Fitch Learning

*With many businesses now working from home, we have introduced virtual learning so we can continue to deliver high quality training to the financial community as we accommodate this new way of working. Since 2003, Fitch Learning has been delivering...

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Fitch Learning

33 Whitehall Street
10004 New York

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