Quantitative Finance & Algorithmic Trading In Python
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.97 GB
| Duration: 18h 19m
Stock Market, Bonds, Markowitz-Portfolio Theory, CAPM, Black-Scholes Model, Value at Risk and Monte-Carlo Simulations
What you'll 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)
Requirements
You should have an interest in quantitative finance as well as in mathematics and programming!
Description
Master Quantitative Finance with Python Turn mathematics, statistics, and programming into powerful tools for understanding-and modeling-the financial world.If you've ever wondered how professionals price options, manage portfolio risk, or build financial models used on Wall Street, this course will guide you step by step through the foundations of financial engineering using Python.In this course, you won't just learn theory-you'll implement real financial models in Python, gaining practical skills that quantitative analysts, traders, and financial engineers use every day.You'll start with the building blocks of financial markets, including stocks, bonds, and derivatives. From there, we move into the mathematical models that revolutionized modern finance-from portfolio optimization to option pricing.Along the way, you'll discover some of the most influential ideas in financial science, including:Bond pricing and interest rate conceptsModern Portfolio Theory and the Markowitz modelThe Capital Asset Pricing Model (CAPM)The Black-Scholes model, one of the most elegant breakthroughs in 20th-century financeRisk management techniques such as Value-at-RiskMonte Carlo simulations for pricing and risk analysisYou'll also explore how randomness shapes financial markets through stochastic processes, Brownian motion, and Ito's calculus, and learn how these ideas are used to model asset prices.By the end of the course, you'll understand how quantitative finance works both theoretically and computationally, and you'll be able to build and implement these models yourself in Python.Important: This course is designed for learners who are genuinely interested in mathematics, statistics, and analytical thinking. If you enjoy working with numbers, models, and coding, you will find this journey incredibly rewarding.What You'll LearnSection 1 - IntroductionInstalling PythonWhy Python is one of the most powerful tools in financeThe challenges of financial modeling and historical dataSection 2 - Stock Market BasicsPresent value and future value of moneyStocks and equity marketsCommodities and the FOREX marketLong and short positions explainedSection 3 - Bond Theory and ImplementationWhat bonds are and how they workYield and yield to maturityMacaulay durationBond pricing theory and Python implementationSection 4 - Modern Portfolio Theory (Markowitz Model)Diversification in financeMean-variance optimizationEfficient frontier and Sharpe ratioCapital Allocation Line (CAL)Section 5 - Capital Asset Pricing Model (CAPM)Systematic vs. unsystematic riskBeta and alphaLinear regression and market riskWhy market risk is the most relevant riskSection 6 - Derivatives BasicsIntroduction to derivativesOptions: calls and putsForward and futures contractsMark-to-market mechanismCredit Default Swaps (CDS)Interest rate swapsSection 7 - Random Behavior in FinanceRandomness in financial marketsWiener processesStochastic calculus and Ito's LemmaBrownian motion theory and implementationSection 8 - Black-Scholes ModelBlack-Scholes theory and implementationMonte Carlo simulations for option pricingThe Greeks and risk sensitivitiesSection 9 - Value-at-Risk (VaR)Understanding Value-at-RiskMonte Carlo simulation for risk estimationSection 10 - Collateralized Debt Obligations (CDO)What CDOs areLessons from the 2008 financial crisisSection 11 - Interest Rate ModelsMean-reverting stochastic processesThe Ornstein-Uhlenbeck processThe Vasicek interest rate modelBond pricing with Monte Carlo simulationSection 12 - Value InvestingLong-term investing strategiesThe Efficient Market HypothesisWhether you want to become a quantitative analyst, improve your financial modeling skills, or simply understand the mathematics behind modern finance, this course will give you the tools to do it.Join now and start building real financial models with Python today.
Anyone who wants to learn the basics of financial engineering!
Quantitative Finance & Algorithmic Trading In Python
