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Course Outline
Introduction
Understanding the Fundamentals of Python
Overview of Using Technology and Python in Finance
Overview of Tools and Infrastructure
- Python Deployment Using Anaconda
- Using the Python Quant Platform
- Using IPython
- Using Spyder
Getting Started with Simple Financial Examples with Python
- Calculating Implied Volatilities
- Implementing the Monte Carlo Simulation
- Using Pure Python
- Using Vectorization with Numpy
- Using Full Vectoriization with Log Euler Scheme
- Using Graphical Analysis
- Using Technical Analysis
Understanding Data Types and Structures in Python
- Learning the Basic Data Types
- Learning the Basic Data Structures
- Using NumPy Data Structures
- Implementing Code Vectorization
Implementing Data Visualization in Python
- Implementing Two-Dimensional Plots
- Using Other Plot Styles
- Implementing Finance Plots
- Generating a 3D Plot
Using Financial Time Series Data in Python
- Exploring the Basics of pandas
- Implementing First and Second Steps with DataFrame Class
- Getting Financial Data from the Web
- Using Financial Data from CSV Files
- Implementing Regression Analysis
- Coping with High-Frequency Data
Implementing Input/Output Operations
- Understanding the Basics of I/O with Python
- Using I/O with pandas
- Implementing Fast I/O with PyTables
Implementing Performance-Critical Applications with Python
- Overview of Performance Libraries in Python
- Understanding Python Paradigms
- Understanding Memory Layout
- Implementing Parallel Computing
- Using the multiprocessing Module
- Using Numba for Dynamic Compiling
- Using Cython for Static Compiling
- Using GPUs for Random Number Generation
Using Mathematical Tools and Techniques for Finance with Python
- Learning Approximation Techniques
- Regression
- Interpolation
- Implementing Convex Optimization
- Implementing Integration Techniques
- Applying Symbolic Computation
Stochastics with Python
- Generation of Random Numbers
- Simulation of Random Variables and of Stochastic Processes
- Implementing Valuation Calculations
- Calculation of Risk Measures
Statistics with Python
- Implementing Normality Tests
- Implementing Portfolio Optimization
- Carrying Out Principal Component Analysis (PCA)
- Implementing Bayesian Regression using PyMC3
Integrating Python with Excel
- Implementing Basic Spreadsheet Interaction
- Using DataNitro for Full Integration of Python and Excel
Object-Oriented Programming with Python
Building Graphical User Interfaces with Python
Integrating Python with Web Technologies and Protocols for Finance
- Web Protocols
- Web Applications
- Web Services
Understanding and Implementing the Valuation Framework with Python
Simulating Financial Models with Python
- Random Number Generation
- Generic Simulation Class
- Geometric Brownian Motion
- The Simulation Class
- Implementing a Use Case for GBM
- Jump Diffusion
- Square-Root Diffusion
Implementing Derivatives Valuation with Python
Implementing Portfolio Valuation with Python
Using Volatility Options in Python
- Implementing Data Collection
- Implementing Model Calibration
- Implementing Portfolio Valuation
Best Practices in Python Programming for Finance
Troubleshooting
Summary and Conclusion
Closing Remarks
Requirements
- Basic programming experience
- A solid grasp of mathematics for finance
35 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €11400 online delivery, based on a group of 2 delegates, €3600 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses
Testimonials (5)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
I mostly enjoyed everything.
