Decoding Data Science & Stochastic Finance

🚀 Enhance your financial modeling skills with “𝓢𝓽𝓸𝓬𝓱𝓪𝓼𝓽𝓲𝓬 𝓕𝓲𝓷𝓪𝓷𝓬𝓮 𝔀𝓲𝓽𝓱 𝓟𝔶𝓽𝓱𝓸𝓷” - my new course

In today's financial modeling landscape, researchers & scientists urgently require a course that merges theory and practical methods into one volume.

While there are other SMOOCs on this subject, few offer a comprehensive perspective. This course represents my small effort to address this gap.

Here are several reasons this course stands out:

🔹 Offers a moderate theoretical foundation on Differential Equations (
)
🔹 Supplies templates in Python for designing financial models using SDE
🔹 Provides a complete numerical solution for process models like Merton model with Python code
🔹 Covers parameter estimation of the model through innovative Python component designs
🔹 Presents a Python framework for path generation of processes, covering various theories to build a moderate foundation

As a bonus, this course is also valuable for researchers in applied probability, covering:
🔹 Recovery of through the method when closed-form densities are difficult to compute - a detailed approach using characteristic functions (inspired by methods from & )
🔹 Carlo simulation with a Python framework
🔹 Various random variable generation methods using Python code
🔹 Motion & process for financial modelling

Whether you are a researcher, data scientist, applied mathematician, or even a software architect eager to learn about financial modeling - this course is for you.

Python code presented in this course is not just a collection of unorganized snippets but, IMO, well-designed components with reusability. With minimal effort, these can be implemented on a small scale.


So, what are you waiting for? Pursue & enjoy this course!

Stochastic Finance with Python Learn Financial Modelling from probabilistic & simulation perspective

🚀 Enhance your financial modeling skills with “𝓢𝓽𝓸𝓬𝓱𝓪𝓼𝓽𝓲𝓬 𝓕𝓲𝓷𝓪𝓷𝓬𝓮 𝔀𝓲𝓽𝓱 𝓟𝔶𝓽𝓱𝓸𝓷” - my new book. 📖

In today's financial modeling landscape, hashtag researchers & hashtag scientists urgently require a book that merges theory and practical methods into one volume.

While there are texts on this subject, few offer a comprehensive perspective. This book represents my small effort to address this gap.

From the back cover -

"Embark on a journey through hashtag finance, learning theory, underlying models, and derivations of financial models (hashtag , hashtag , portfolios) alongside nearly production-ready Python components related to stochastic finance...."

Here are several reasons this book stands out:

🔹 Offers a solid theoretical foundation on hashtag Differential Equations (hashtag )
🔹 Supplies templates in Python for designing financial models using SDE
🔹 Provides a complete numerical solution for hashtag process models like hashtag & hashtag along with Python code
🔹 Covers parameter estimation of the hashtag & hashtag model through innovative Python component designs
🔹 Discusses the theoretical background of option price modeling with the hashtag -Scholes model and includes Python code
🔹 Explores a hashtag (Partial Differential Equation) approach to solving the hashtag -Scholes model, detailing three methods for numerical solutions for PDE.
🔹 Examines hashtag construction and hashtag using innovative Python components
🔹 Presents a Python framework for path generation of hashtag processes, covering various theories to build a solid foundation
🔹 Discusses a hashtag method for hashtag process modeling

As an added bonus, this book is also valuable for researchers in applied probability, covering:
🔹 Recovery of hashtag hashtag through the hashtag method when closed-form densities are difficult to compute - a detailed approach using characteristic functions (inspired by methods from hashtag & hashtag )
🔹 hashtag Carlo simulation with a Python framework
🔹 Various random variable generation methods using Python code
🔹 hashtag reduction (hashtag ) techniques with Python code.
🔹 hashtag Motion & hashtag process for financial modelling

Whether you are a hashtag researcher, data scientist, applied mathematician, or even a software architect eager to learn about financial modeling - this book is for you.

Python code presented in this book is not just a collection of unorganized snippets but, IMO, well-designed components with reusability. With minimal effort, these can be implemented on a small scale.

So, what are you waiting for? Order and enjoy your reading! The link is in the comments.

Learn how to deduce the distribution of a complex random variable.

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https://youtu.be/GsvH7vuUjC0?si=BMW7Dlp_dnUkmz95

Probability Density of Random Variables of complex numbers This video discusses how to find the expression of Gaussian PDF with a complex random variable.

Fairness in ML - Introduction

This is the channel's and page's first video. Welcome to all data science and quant finance practitioners.

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