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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-and-machine-learning-in-financial-analysis/
课程评论：没有评论
课程名称：金融分析中的完整Python与机器学习 课程概述：本课程将让您熟悉各种最新的金融分析内容，以及Python环境下机器学习算法的技术，从而能够执行高度专业化的金融分析。您将学习全面的Python环境，并掌握深度学习算法和人工神经网络，这些技术将极大增强您的金融分析技能和专业知识。 课程开始时，将探讨多种下载金融数据的方法，并为建模做好准备。我们将检查资产价格和回报的基本统计特性，并研究所谓的风格化事实的存在。随后，我们计算技术分析中常用的指标（如布林带、移动平均收敛发散（MACD）和相对强弱指数（RSI）），并对基于这些指标构建的自动交易策略进行回测。 接下来的部分介绍时间序列分析，探索流行模型如指数平滑、自回归整合移动平均（ARIMA）和广义自回归条件异方差（GARCH）（包括多元规范）。同时，我们还将介绍因子模型，包括著名的资本资产定价模型（CAPM）和法马-法兰奇三因子模型。我们将通过不同的方式演示资产配置优化，并使用蒙特卡洛模拟来执行例如计算美国期权价格或估算风险价值（VaR）等任务。 在课程的最后部分，我们将在金融领域实施一个完整的数据科学项目。我们将使用随机森林、XGBoost、LightGBM、堆叠模型等先进分类器来解决信用卡欺诈/违约问题。同时，我们还将调整模型的超参数（包括贝叶斯优化）并处理类别不平衡问题。最后，我们将演示如何使用深度学习（利用PyTorch）来解决众多金融问题。
In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise.This tutorial begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI)) and backtest automatic trading strategies built on their basis.The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the Value at Risk (VaR).In the last part of the course, we carry out an entire data science project in the financial domain. We approach credit card fraud/default problems using advanced classifiers such as random forest, XGBoost, LightGBM, stacked models, and many more. We also tune the hyperparameters of the models (including Bayesian optimization) and handle class imbalance. We conclude the book by demonstrating how deep learning (using PyTorch) can solve numerous financial problems.
