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Analyzing Big Financial Data with Python培訓

 
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課程大綱
 
  • Introduction
  • Understanding the Fundamentals of Python
  • Overview of Using Technology and Python in Finance
  • Overview of Tools and Infrastructure
  • Python Deployment Using Anaconda
    Using the Python Quant Platform
    Using IPython
    Using Spyder
    Getting Started with Simple Financial Examples with Python
  • Calculating Implied Volatilities
    Implementing the Monte Carlo Simulation
    Using Pure Python
    Using Vectorization with Numpy
    Using Full Vectoriization with Log Euler Scheme
    Using Graphical Analysis
    Using Technical Analysis
    Understanding Data Types and Structures in Python
  • Learning the Basic Data Types
    Learning the Basic Data Structures
    Using NumPy Data Structures
    Implementing Code Vectorization
    Implementing Data Visualization in Python
  • Implementing Two-Dimensional Plots
    Using Other Plot Styles
    Implementing Finance Plots
    Generating a 3D Plot
    Using Financial Time Series Data in Python
  • Exploring the Basics of pandas
    Implementing First and Second Steps with DataFrame Class
    Getting Financial Data from the Web
    Using Financial Data from CSV Files
    Implementing Regression Analysis
    Coping with High-Frequency Data
    Implementing Input/Output Operations
  • Understanding the Basics of I/O with Python
    Using I/O with pandas
    Implementing Fast I/O with PyTables
    Implementing Performance-Critical Applications with Python
  • Overview of Performance Libraries in Python
    Understanding Python Paradigms
    Understanding Memory Layout
    Implementing Parallel Computing
    Using the multiprocessing Module
    Using Numba for Dynamic Compiling
    Using Cython for Static Compiling
    Using GPUs for Random Number Generation
    Using Mathematical Tools and Techniques for Finance with Python
  • Learning Approximation Techniques
    Regression
    Interpolation
    Implementing Convex Optimization
    Implementing Integration Techniques
    Applying Symbolic Computation
    Stochastics with Python
  • Generation of Random Numbers
    Simulation of Random Variables and of Stochastic Processes
    Implementing Valuation Calculations
    Calculation of Risk Measures
    Statistics with Python
  • Implementing Normality Tests
    Implementing Portfolio Optimization
    Carrying Out Principal Component Analysis (PCA)
    Implementing Bayesian Regression using PyMC3
    Integrating Python with Excel
  • Implementing Basic Spreadsheet Interaction
    Using DataNitro for Full Integration of Python and Excel
    Object-Oriented Programming with Python
  • Building Graphical User Interfaces with Python
  • Integrating Python with Web Technologies and Protocols for Finance
  • Web Protocols
    Web Applications
    Web Services
    Understanding and Implementing the Valuation Framework with Python
  • Simulating Financial Models with Python
  • Random Number Generation
    Generic Simulation Class
    Geometric Brownian Motion
    The Simulation Class
    Implementing a Use Case for GBM
    Jump Diffusion
    Square-Root Diffusion
    Implementing Derivatives Valuation with Python
  • Implementing Portfolio Valuation with Python
  • Using Volatility Options in Python
  • Implementing Data Collection
    Implementing Model Calibration
    Implementing Portfolio Valuation
    Best Practices in Python Programming for Finance
  • Troubleshooting
  • Summary and Conclusion
  • Closing Remarks
 
  備案號:備案號:滬ICP備08026168號-1 .(2014年7月11)...................
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