|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/mathematical-finance-with-python/
课程评论：没有评论
课程名称：Python数学金融：理论与实践 课程概况：本课程结合了Python编程概念、数学建模、定量金融和机器学习的应用。课程分为四个部分： 第一部分涵盖了Python编程的基本知识，包括打印输出、数据类型、分支和迭代等基础内容。此外，还涉及元组、可变性、函数、递归、类和面向对象编程等重要主题。本部分的高潮是一个顶点项目，学生将建立一个金融模型，以计算抵押贷款付款。 第二部分专注于金融数学与Python的结合，包含两个顶点项目，分别是利用布莱克-舒尔斯模型进行期权的详细建模和债券工具的估值建模。此部分详细讲解金融所需的基础数学概念，通过理论讲解（白板风格），每个概念后都有实际的Python实验课。涵盖的主题包括指数函数、对数函数、对数正态分布函数，同时还包括关于期权希腊值建模的特别附加课程。 第三部分涉及金融的随机建模，这是定量金融的关键。本部分提供了随机金融主要概念的坚实基础，详细介绍了金融中的蒙特卡洛模拟技术及其应用，如股票价格和期权价格的估算。其他涵盖主题包括随机游走建模、几何布朗运动模拟、利用蒙特卡洛模拟计算圆周率等。 第四部分探讨金融领域的机器学习。学生将学习股票的技术分析，包括K线图和OHLC图，使用高级机器学习算法预测股票价格，以及实时市场研究。 本课程深入探讨金融数学，课程的独特之处在于每个概念都以理论方式讲解（使用数字白板），并随后立即进行实践实验。这种强大的组合将确保学生很好地掌握各个概念。Python编程、数学和金融的重要概念以清晰易懂的方式进行解释。课程涵盖严谨的Python编码，学员将通过自定义函数设计数学模型，而非使用标准库，深入了解Python中的模型创建过程。
This course combines the right mix of programming concepts with Python, Mathematical Modelling, Quantitative Finance and Machine Learning. This course is divided into four parts. Part one covers the essentials of python programming. This includes basic printing, data types, branching and iteration. This part also covers key topics like tuples, mutability, functions, recursion, classes and concepts of object oriented programming. This part culminates with a capstone project, wherein we would build a financial model to calculate mortgage payments.Part two covers python and mathematics for finance. This part entails two capstone projects which would cover detailed modelling of options using the Black-Scholes Model and detailed modelling of valuation of bond instruments. This part clearly explain all the basic math concepts required for finance in a theoretical (white board fashion) and each concept session is followed by a hands-on lab session in python. The topics covered are exponential functions, logarithmic functions, Log-normal functions. There is also a special bonus session on modelling of options GreeksPart three covers stochastic modelling for finance. This part is key for quantitative finance and we give a solid foundational understanding of the key concepts of stochastic finance. There is an exclusive and exhaustive coverage of Monte Carlo simulation techniques for Finance and its application by means of estimation of stock price and options price. Other topics covered are Random Walk Modelling, Geometric Brownian process simulation, Calculation of Pi using Monte Carlo Simulation. Part four covers machine learning for finance. In this part , students would learn technical analysis of stocks with candlestick and OHLC charts, prediction of stock prices using advanced machine learning algorithms and real time market research.This course covers mathematics of finance in detail. The USP of the course is that each concept is explained in a theoretical fashion (using a digital white board) and is immediately followed by a hands-on lab session. This powerful combination would ensure that the students learn the concepts very well. Important concepts of python programming , mathematics and finance are explained in a way with which the student would have absolute clarity. Hard core coding in Python is involved, wherein the mathematical models are designed using user defined custom functions and not consumed from standard libraries. The student gets to learn the nuts and bolts of model creation in python.
