im电竞游戏入口官网(im电竞游戏选手2.5.7): Live Online

im电竞游戏入口官网(im电竞游戏选手2.5.7) Objectives  CPD Certified

This course offers a short but intensive hands-on introduction to the use of Python in finance. It explores the key characteristics of this powerful and modern programming language to solve quantitative problems in finance and risk management.

Key Learning Outcomes:

  • Explore the benefits of using Python in practical day-to-day business activities
  • Explore in detail how Python is used in modern Finance, Portfolio Management, Financial Derivatives and Risk Management
  • Have a hands-on experience of programming in Python to solve financial problems

Who Should Attend

This course is ideal for financial analysts, business analysts, portfolio analysts, quantitative analysts, risk managers, model validators, quantitative developers and information systems professionals. There are no pre-requisites to attend this course. We expect participants to have a basic knowledge of finance and basic notions of programming.

Day One
Introduction to Python
  • Why Python is suited to data science, scientific computing and quantitative modeling
  • Python versions
  • The Anaconda Python distribution
  • The Spyder integrated-development environment
  • Jupyter notebooks
The Python programming language
  • Variables and types
  • Conditional statements
  • Collections
  • Loops
  • Functions
  • Comprehensions
Numerical computing with numpy
  • Floating-point representation and finite precision
  • Fundamentals of numerical methods
  • Writing scalable high-performance code using vectorization
  • Arrays
  • Plotting graphs using matplotlib
  • Matrices and vectors
  • Generating random variates
Day Two
Analyzing financial data using pandas
  • Importing data from Excel and CSV files
  • Analyzing time-series
    • Re-sampling data at different frequencies
    • Plotting time-series data
  • Converting prices to returns
  • Descriptive statistics of the return distribution
  • Representing portfolios
Statistics and optimization with SciPy
  • Loading portfolio data using pandas
  • Exploratory data analysis and visualization
    • Scatter plots
    • Kernel-density estimation
    • Using boxplots to compare return distributions
  • Statistical tests for Guassinity
  • Computing covariance and correlation matrices
  • Linear regression in Python
  • CAPM and the single-index model
  • Using scipy to estimate alpha and beta
  • Mean-variance portfolio optimization in Python
    • Plotting the efficient frontier
Monte-Carlo methods in Python
  • Standard error
  • Bootstrapping
  • Simulating stochastic processes using vectorization
  • Simulating geometric Brownian motion in Python
  • Monte-Carlo option pricing
  • Modeling Value-at-Risk