Mathematical Finance with Python : Theory and Practice

What you’ll learn
Essentials of Python Programming : Printing, Data Types, Branching, Loops, Advanced Data Structures, Functions, Recursion, Classes & Object oriented programming
Python for Finance : Statistics, Detailed modelling of Options using the Black-Scholes Model, Bond Valuation, Mathematical functions for finance(exp,log-normal)
Stochastic Finance : Random Walk Modelling, Monte-Carlo simulation for finance (Estimation of stock and options price), Brownian process , Histogram generation.
Machine Learning for Finance : Technical Analysis with Candlestick OHLC and other charts, Prediction of stock prices using advanced machine learning algorithms.
Detailed Mathematical Modelling of Black-Scholes Equation with Python. Comparison techniques for different BS Options pricing models with respect to impact
Special techniques for testing and debugging codes. Conceptual understanding of testing and debugging coupled with live debugging sessions in the virtual lab
Building mathematical models for financial instruments using custom functions & classes. Converting mathematical expressions into concise pieces of python code
Clear understanding of essential mathematical concepts required for financial modelling ( writing a mathematical expression in python language using functions)
Theoretical understanding of programming concepts and Hands-on python lab for each concept to apply the theoretical learning immediately in coding environment
Key model building techniques for Finance like Black-Scholes Options Pricing, Monte Carlo Simulation for Finance, Stock prediction using Machine Learning etc.,

Requirements
No programming experience needed.
No prior knowledge on mathematics / statistics needed.
All you need is a computer with internet connection and loads of enthusiasm to learn.

Description
This course combines the right mix of programming concepts with Python, Mathematical Modelling, Quantitative Finance and Machine Learning. This course is divided into four parts.

Part one covers the essentials of python programming. This includes basic printing, data types, branching and iteration. This part also covers key topics like tuples, mutability, functions, recursion, classes and concepts of object oriented programming. This part culminates with a capstone project, wherein we would build a financial model to calculate mortgage payments.

Part two covers python and mathematics for finance. This part entails two capstone projects which would cover detailed modelling of options using the Black-Scholes Model and detailed modelling of valuation of bond instruments. This part clearly explain all the basic math concepts required for finance in a theoretical (white board fashion) and each concept session is followed by a hands-on lab session in python. The topics covered are exponential functions, logarithmic functions, Log-normal functions. There is also a special bonus session on modelling of options Greeks

Part three covers stochastic modelling for finance. This part is key for quantitative finance and we give a solid foundational understanding of the key concepts of stochastic finance. There is an exclusive and exhaustive coverage of Monte Carlo simulation techniques for Finance and its application by means of estimation of stock price and options price. Other topics covered are Random Walk Modelling, Geometric Brownian process simulation, Calculation of Pi using Monte Carlo Simulation.

Part four covers machine learning for finance . In this part , students would learn technical analysis of stocks with candlestick and OHLC charts, prediction of stock prices using advanced machine learning algorithms and real time market research.

This course covers mathematics of finance in detail. The USP of the course is that each concept is explained in a theoretical fashion (using a digital white board) and is immediately followed by a hands-on lab session. This powerful combination would ensure that the students learn the concepts very well.

Important concepts of python programming , mathematics and finance are explained in a way with which the student would have absolute clarity. Hard core coding in Python is involved, wherein the mathematical models are designed using user defined custom functions and not consumed from standard libraries. The student gets to learn the nuts and bolts of model creation in python.

Who this course is for
Working Professionals who would want to upskill themselves, in order to meet the demands of the changing business landscape.
University students from any background, who would want to strengthen their profile with job ready skills
Academic professionals and Research scholars, who would want to add a new set of skills in their arsenal.