Algorithmic Trading Python 2023 - 1.0 - Intro #technology #stockmarket #onlineearning

Algorithmic Trading Python 2023 Docs download link:

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Hi everyone, welcome to my channel, this is Quantum Unicorn, please join me in the journey to learn python programming, build stock trading models, and conduct real-world financial analysis in python.

Most people traded stocks through online brokers nowadays, and online brokers take the place of human brokers. We make our own trading decisions, based on the financial data and market information available from the Internet, using data science language. I combine both Python and Statistics concepts and apply them to analyzing financial data, such as stock data.

But why do investment banks and consumer banks use Python to build quantitative models to predict returns and evaluate risks? What makes Python one of the most popular tools for financial analysis?

• Python’s simplicity leads to lower error rates and less bug-hunting.
• Python may not be the fastest-performing language, but it’s a top choice for the optimal time to market.
• The straightforward syntax of Python will facilitate collaboration between developers, technical experts, and the C-suite.
• Finally, Python’s wealth of open-source libraries provides ready-to-go solutions for many common problems in fintech.
So If anyone ever asks you about the fintech viability of Python, now you’ll know what to tell them。

You are going to learn basic python to import, manipulate and visualize stock data in this module. As Python is highly readable and simple enough, you can build one of the most popular trading models - Trend following strategy by the end of this module!

You are going to learn how to calculate moving averages (MA), buy signals based on MA, strategy profits, stock return frequency distributions, Value at Risk (VaR), confidence intervals on average returns, hypothesis tests, p-values, linear regression, diagnostics like Durbin Watson test, normality validation, residuals. You will apply a multiple linear regression model to predict the daily return of SPY based on other stock prices, and you will evaluate the model using RMSE, R2, Sharpe Ratio, and Maximum Drawdown.

We are going to use a Jupyter notebook environment for this tutorial. !!!Jupyter Notebook is an application that allows you to create documents with Python code. !!! Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. !!! If you haven’t installed Jupyter Notebook locally on your computer, I have found some tutorials for installation instructions for your convenience.
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You can practice the financial analysis and other examples I explained in the videos. Simply follow the Jupyter notebook files I prepared, anytime and anywhere. !!!

The tutorial is suitable for anyone who is interested in analyzing financial data using Python. You will get the most out of the tutorial if you have basic knowledge of probabilities.
By the end of the tutorial, you can achieve the following using python:

- Import, pre-process, save, and visualize financial data into pandas Dataframe

- Manipulate the existing financial data by generating new variables using multiple columns

- Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts

- Build a trading model using a multiple linear regression model

- Evaluate the performance of the trading model using different investment indicators

I hope this tutorial will bring you one step closer to the financial abundance you have always wanted! Please subscribe to my channel and click the bell icon to get the new updates. If you have any questions, feel free to leave a comment. I will see you in the next episode.