Python financial application programming for big Data projects (data analysis, pricing and quantification investments)

Python financial application programming for big Data projects (data analysis, pricing and quantification investments)

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Content Introduction
This tutorial introduces the basics of using Python for data analysis and financial application development.

Starting with introducing simple financial applications, the course leads students to review the basics of Python and to learn how to apply Python to financial analysis programming.

The course covers Python's basic data structure, input and output, efficiency analysis, math library, random analysis library, and statistical analysis library. The course then introduces the combination of Python and excel in the form of a topic, learning how to use Python's related libraries to generate Excel-callable functions, and Python's combination of Hadoop and MongoDB for big data analysis.

The final course introduces Python's object-oriented programming and introduces two cases: the use of Python to implement the financial Derivatives Analysis library and the use of Python to implement the event-driven quantitative investment system, so that students in the actual situation of the application of Python in the development of financial applications, the specific use of the way, Ability to train learners to independently develop Python modules.

Course Outline--

Introduction to the first, Python and financial applications
This lecture mainly introduces the basic features of Python, installs the Python environment required for this course, and outlines Python's application in financial data analysis. We will use a simple example of trend investing to explain why it is very convenient to use Python for financial data analysis and quantitative investment.

Second, Python basic data types and data structures
This presentation introduces Python's basic data types and data structures, including the underlying Python and numpy libraries.
1. Basic data type (integer, floating point, character type)
2. Basic data structure (tuple, control structure, function programming, list, dictionary, collection)
3. NumPy data Structures (arrays implemented using Python lists, regular numpy arrays, arrays of structures, memory allocations)

Third speaking, Python data visualization
This presentation introduces the data visualization techniques provided by Python's matplotlib library, although Python has many other ways of visualizing data, but Matplotlib provides a baseline implementation approach.
1. Two-dimensional drawing (one-dimensional data sets, two-dimensional datasets, other drawing modes, financial plotting)
2.3D Drawing

Analysis of financial time series in the four-lecture
One of the most common data types in financial analysis is the financial time series data, this chapter mainly introduces the pandas library of Python to the realization of the Financial time series type data structure--dataframe and series, and how to use these tools for basic financial time series analysis
1, Pandas Foundation (Dataframe class, basic analysis technology, series class, GroupBy operation)
2. Financial data
3. Data regression analysis
4. High-frequency financial data

V, input and output operation
This presentation describes the basic input and output operations provided by Python and how to use them effectively in financial data analysis and investment.
1, Python basic I/O operation (write object to hard disk, read and write text file, SQL database, read/write NumPy array)
2. I/O operation using Pandas (basic operation, SQL database, CSV file, Excel file)
3. Use Pytables for fast I/O (using table, using a compressed table, array operation, internal memory)

Six, improve Python efficiency
This presentation describes some of the tools available in Python to improve computational efficiency and their basic applications in financial data analysis and investment.
1. Python Running efficiency analysis
memory allocation and operational efficiency
2, Parallel Computing (Monte Carlo algorithm, serial computing, parallel computing)
3. Dynamic compilation (introduction example, Binary tree option pricing)
4. Static compilation using Cython
5. Generate random numbers based on GPU

Seventh lecture, Mathematical tools
This presentation introduces the mathematical methods and tools provided by Python for financial data analysis and their background knowledge and application methods.
1, approximate (regression, interpolation)
2. Convex Optimization (global optimization, local optimality, constrained optimization)
3. Integral (numerical integral, Analog integral)
4. Symbolic calculation (base, equation, integral, differential)

Eighth lecture, Random analysis
The characterization and research of uncertainty is an important aspect of financial research and analysis, and this paper introduces some knowledge of stochastic analysis in the application of financial data analysis and investment and Python implementation.
1. Random number
2. Simulation (random variable, stochastic process)
3. Variance Reduction Technology
4. Valuation (European Options, American options)
5. Risk measure index (in risk value, credit risk)

Nineth Lecture, statistical analysis
Statistical analysis is the core of financial data analysis, this talk about the common statistical analysis methods, financial applications and Python implementation.
1. Normality test
2. Asset Portfolio Optimization
3. Principal Component Analysis Application
4. Bayesian regression analysis

Tenth, numerical analysis technology
For some non-linear, non-explicit solutions to financial and data analysis problems, the need to use numerical analysis techniques, this talk about the basis of these technologies and applications, as well as the implementation of Python.
1. Solving linear equations (LU decomposition, Cholesky decomposition, QR Decomposition, Jacobi method, Gauss-seidel method)
2. Non-linear model in finance (implied volatility, Markov regime-switching model, Threshold autoregressive model, stationary transition model)
3. Root-Finding method

11th, using Python to manipulate Excel
Microsoft Excel is a common Office software, is the data analysis and application of the important support. Python provides a rich interface for interacting with Excel, which describes these interfaces as an example.
1. Basic spreadsheet interaction
2. Excel script in Python

12th, Python object-oriented programming and graphical user interface
This presentation introduces the Python object-oriented programming technology, which is the basis of the subsequent chapters, especially the quantitative investment chapter, and also introduces the basic methods of programming Python graphical user interface.
1. Object-oriented
2. Graphical user interface

13th, Big Data Technology overview in Finance
This lecture introduces the application of Big data technology in finance and the basic implementation of Python.
1. Overview of Hadoop
2. Using Hadoop for Character statistics
3. Examples of Hadoop financial applications
4. NoSQL Introduction

14th, Case 1: Using Python to build an option analysis system
This case uses the Python financial application knowledge previously described, constructs a relatively complete option analysis system to help students grasp the key points of financial system development and the way Python integrates applications, compared with the previous introduction, in case analysis more use of object-oriented approach.
1. Valuation Framework (Capital asset pricing principle, risk neutral pricing, market environment, etc.)
2. Simulation of financial model (random number generation module, generic simulation class, geometric Brownian motion, diffusion process simulation module with jump, simulation module of square root diffusion process)
3. Derivative valuation Module (Generic Valuation class, European-style execution class, American execution Class)
4. Application of Derivative Analysis Library--Volatility option pricing

15th, Case 2: Using Python to build a simple algorithmic trading system
Algorithmic and programmatic trading is one of the most important aspects of the application of computer technology in the financial field in the Big Data era. This paper introduces the implementation of Python in this area, including basic trading, trading strategy and backtesting.
1. Algorithmic Trading Overview and framework
2, the implementation of event-driven trading engine (event-driven software, event classes, data processing classes, policy classes, portfolio classes, the implementation of processing classes and the basic compilation of backtesting classes, event-driven execution)
3, the implementation of the trading strategy (moving average crossing strategy, s&p500 forecast trading strategy, mean reversion stock matching trading strategy)
4, strategy optimization (parameter optimization, model selection, optimization strategy)

Python financial application programming for big Data projects (data analysis, pricing and quantification investments)