Quantitative Finance Algorithmic Trading in Python

Quantitative Finance & Algorithmic Trading in Python, available at $109.99, has an average rating of 4.54, with 182 lectures, 15 quizzes, based on 1846 reviews, and has 16996 subscribers.

You will learn about Understand stock market fundamentals Understand bonds and bond pricing Understand the Modern Portfolio Theory and Markowitz model Understand the Capital Asset Pricing Model (CAPM) Understand derivatives (futures and options) Understand credit derivatives (credit default swaps) Understand stochastic processes and the famous Black-Scholes model Understand Monte-Carlo simulations Understand Value-at-Risk (VaR) Understand CDOs and the financial crisis Understand interest rate models (Vasicek model) This course is ideal for individuals who are Anyone who wants to learn the basics of financial engineering! It is particularly useful for Anyone who wants to learn the basics of financial engineering!.

Enroll now: Quantitative Finance & Algorithmic Trading in Python

Summary

Title: Quantitative Finance & Algorithmic Trading in Python

Price: $109.99

Average Rating: 4.54

Number of Lectures: 182

Number of Quizzes: 15

Number of Published Lectures: 182

Number of Published Quizzes: 15

Number of Curriculum Items: 198

Number of Published Curriculum Objects: 197

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand stock market fundamentals

Understand bonds and bond pricing

Understand the Modern Portfolio Theory and Markowitz model

Understand the Capital Asset Pricing Model (CAPM)

Understand derivatives (futures and options)

Understand credit derivatives (credit default swaps)

Understand stochastic processes and the famous Black-Scholes model

Understand Monte-Carlo simulations

Understand Value-at-Risk (VaR)

Understand CDOs and the financial crisis

Understand interest rate models (Vasicek model)

Who Should Attend

Anyone who wants to learn the basics of financial engineering!

Target Audiences

Anyone who wants to learn the basics of financial engineering!

This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main.

First of all we have to consider bonds and bond pricing. Markowitz-model is the second step. Then Capital Asset Pricing Model (CAPM). One of the most elegant scientific discoveries in the 20th century is the Black-Scholes modeland how to eliminate risk with hedging.

IMPORTANT: only take this course, if you are interested in statistics and mathematics !!!

Section 1 – Introduction

installing Python

why to use Python programming language

the problem with financial models and historical data

Section 2 – Stock Market Basics

present value and future value of money

stocks and shares

commodities and the FOREX

what are short and long positions?

Section 3 – Bond Theory and Implementation

what are bonds

yields and yield to maturity

Macaulay duration

bond pricing theory and implementation

Section 4 – Modern Portfolio Theory (Markowitz Model)

what is diverzification in finance?

mean and variance

efficient frontier and the Sharpe ratio

capital allocation line (CAL)

Section 5 – Capital Asset Pricing Model (CAPM)

systematic and unsystematic risks

beta and alpha parameters

linear regression and market risk

why market risk is the only relevant risk?

Section 6 – Derivatives Basics

derivatives basics

options (put and call options)

forward and future contracts

credit default swaps (CDS)

interest rate swaps

Section 7 – Random Behavior in Finance

random behavior

Wiener processes

stochastic calculus and Ito’s lemma

brownian motion theory and implementation

Section 8 – Black-Scholes Model

Black-Scholes model theory and implementation

Monte-Carlo simulations for option pricing

the greeks

Section 9 – Value-at-Risk (VaR)

what is value at risk (VaR)

Monte-Carlo simulation to calculate risks

Section 10 – Collateralized Debt Obligation (CDO)

what are CDOs?

the financial crisis in 2008

Section 11 – Interest Rate Models

mean reverting stochastic processes

the Ornstein-Uhlenbeck process

the Vasicek model

using Monte-Carlo simulation to price bonds

Section 12 – Value Investing

long term investing

efficient market hypothesis

APPENDIX – PYTHON CRASH COURSE

basics – variables, strings, loops and logical operators

functions

data structures in Python (lists, arrays, tuples and dictionaries)

object oriented programming (OOP)

NumPy

Thanks for joining my course, let’s get started!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Why to use Python?

Lecture 3: Financial models

Chapter 2: Environment Setup

Lecture 1: Installing Python

Lecture 2: Installing PyCharm

Chapter 3: Stock Market Basics

Lecture 1: Present value and future value of money

Lecture 2: Time value of money implementation

Lecture 3: Stocks and shares

Lecture 4: Commodities

Lecture 5: Currencies and the FOREX

Lecture 6: Short and long positions

Chapter 4: Bonds Theory

Lecture 1: What are bonds?

Lecture 2: Yields and yield to maturity

Lecture 3: Yields and yield to maturity

Lecture 4: Interest rates and bonds

Lecture 5: Macaulay duration

Lecture 6: Risks with bonds

Lecture 7: Stocks and bonds

Chapter 5: Bonds Implementation

Lecture 1: Bonds pricing implementation I

Lecture 2: Bonds pricing implementation II

Lecture 3: Exercise – continuous model for discounting

Lecture 4: Solution – continuous model for discounting

Chapter 6: Modern Portfolio Theory (Markowitz-Model)

Lecture 1: What are mean, variance and correlation?

Lecture 2: The main idea – diverzification

Lecture 3: Mathematical formulation

Lecture 4: Expected return of the portfolio

Lecture 5: Expected variance (risk) of the portfolio

Lecture 6: Efficient frontier

Lecture 7: Sharpe ratio

Lecture 8: Capital allocation line

Chapter 7: Markowitz-Model Implementation

Lecture 1: Markowitz model implementation I

Lecture 2: Markowitz model implementation II

Lecture 3: Markowitz model implementation III

Lecture 4: Markowitz model implementation IV

Lecture 5: Markowitz model implementation V

Chapter 8: Capital Asset Pricing Model (CAPM) Theory

Lecture 1: Systematic and unsystematic risk

Lecture 2: Capital asset pricing model formula

Lecture 3: The beta value

Lecture 4: What is linear regression?

Lecture 5: Capital asset pricing model and linear regression

Chapter 9: Capital Asset Pricing Model (CAPM) Implementation

Lecture 1: Capital asset pricing model implementation I

Lecture 2: Capital asset pricing model implementation II

Lecture 3: Capital asset pricing model implementation III

Lecture 4: Exercise – normal distribution of returns

Lecture 5: Solution – normal distribution of returns

Chapter 10: Derivatives Basics

Lecture 1: Introduction to derivatives

Lecture 2: Forward and future contracts

Lecture 3: Swaps and interest rate swaps

Lecture 4: Credit default swap (CDS)

Lecture 5: Options basics

Lecture 6: Call option

Lecture 7: Put option

Lecture 8: American and european options

Chapter 11: Random Behavior in Finance

Lecture 1: Types of analysis

Lecture 2: Random behavior of returns

Lecture 3: Wiener-processes and random walks

Lecture 4: Wiener-process implementation

Lecture 5: Stochastic calculus introduction

Lecture 6: Itos lemma in higher dimensions

Lecture 7: Solving the geometric random walk equation

Lecture 8: Geometric brownian motion implementation

Chapter 12: Black-Scholes Model

Lecture 1: Black-Scholes model introduction – the portfolio

Lecture 2: Black-Scholes model introduction – dynamic delta hedge

Lecture 3: Black-Scholes model introduction – no arbitrage principle

Lecture 4: Solution to Black-Scholes equation

Lecture 5: The greeks

Lecture 6: How to make money with Black-Scholes model?

Lecture 7: Long Term Capital Management (LTCM)

Chapter 13: Black-Scholes Model Implementation

Lecture 1: Black-Scholes model implementation

Lecture 2: What is Monte-Carlo simulation?

Lecture 3: Predicting stock prices with Monte-Carlo simulation

Lecture 4: Black-Scholes model implementation with Monte-Carlo simulation I

Lecture 5: Black-Scholes model implementation with Monte-Carlo simulation II

Lecture 6: Black-Scholes model implementation with Monte-Carlo simulation III

Chapter 14: Value at Risk (VaR)

Lecture 1: What is Value-at-Risk?

Lecture 2: Value-at-Risk introduction

Lecture 3: Value at risk implementation

Lecture 4: Value at risk implementation with Monte-Carlo simulation I

Lecture 5: Value at risk implementation with Monte-Carlo simulation II

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 32 votes

2 stars: 41 votes

3 stars: 206 votes

4 stars: 646 votes

5 stars: 921 votes

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