Algorithmic Trading Python 2023 - 3.8 Hypothesis Testing Code Explained #technology #python смотреть онлайн

Algorithmic Trading Python 2023 Docs download link:
https://github.com/Baoqizhigang/Stock-Trading-Models/tree/main/data
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Greetings everyone! Welcome to my channel, Quantum Unicorn. Please join me on a remarkable journey as we explore the world of stock trading and financial analysis using the powerful programming language, Python. I'm excited to share my knowledge and insights with you as we delve into the exciting and dynamic field of stock trading modeling and financial analysis. I look forward to embarking on this journey with you and creating a vibrant community of like-minded individuals. Let's make the most of this opportunity to learn and grow together. Welcome aboard!

We will delve into the exciting world of Python and its application in the analysis of financial data. Before we get started, let's explore how Python is utilized in the financial industry.

Quantitative analysts and engineers of investment banks use Python to build all kinds of models, predict returns, and evaluate risks. Engineers use Python to crawl financial news and to dig out users' reactions and sentiments. This new source of data from social media can greatly help quantitative analysts to improve the performance of the models.

Investment banks rely on the expertise of quantitative analysts and engineers who use Python. But the use of Python is not limited to investment banks, it has also become a popular tool among data scientists in consumer banks. They use Python to analyze credit risk models and customer behavior, reducing the risk of lending.

With the ability to predict customer behavior, they can also create recommendation models to improve the accuracy of recommendations for new customers across different markets, which is called "customer migrations".

So, what makes Python so well-suited for financial data analysis? There are two main reasons:
Easy To Learn: Python is a simple and straightforward language, as it doesn't have any complex language syntax or intricate guidelines. Moreover, Python’s syntax is so similar to English, many find it easier to learn than other programming languages. With some time and dedication, you can learn to write Python, even if you've never written a line of code before

Used In Machine Learning And Artificial Intelligence: Python is a popular language for machine learning and artificial intelligence due to its ability to perform complex calculations and handle diverse activities. The language is equipped with libraries for neural system experimentation, making it a valuable tool in this field.

In practice, it is always necessary to generate and transform original variables into other forms. For example, we need to get the stock returns from the stock prices. Therefore, in the next step, we will learn how to generate new variables from our original variables. We will learn an advanced workflow using DataFrame to implement a trend-following strategy for trading stocks. By the end of the first module, you will be able to visualize and apply stock data to bring your trading ideas to life.

Now, let’s take a quick look at the packages of Python that will be used for the tutorial:
Pandas is a python package, that provides fast, flexible, and expressive data structures. It aims to be the fundamental high-level building blocks, for doing practical real-world data analysis. For example, DataFrame and the series from Pandas, are excellent data structures to store table and time series data. With DataFrame, we can easily pre-process data such as handling missing values and computing pairwise correlation.
NumPy is a fundamental package for the numerical computing of arrays and matrix. It is also a convenient tool for generating random numbers, which could be helpful if we want to shuffle data or generate a dataset with normal distribution.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It can create publication-quality plots, make interactive figures, and customize visual style and layout.
Statsmodels is a powerful library for statisticians. It contains modules for regression and time series analysis. In this tutorial, we will use Statsmodels to obtain multiple linear regression models.

00:00 Introduction
00:45 Main
13:20 End