We have a created an Algorithmic Trading Course in python for pure beginners wherein we discuss multiple concepts from a basic zero to hero framework. The video is a full tutorial which starts from basic installation of python and anaconda all the way to backtesting strategies and creating trading API. There are more quality quantitative strategy video son our channel that can be found below:
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The full code can be downloaded from the link below:
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Feel free to check out our Quantitative Trading course:
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Chapters
00:00 Intro
00:55 Installation of Anaconda
01:50 Installing Yfinance
02:40 Working with Jupyter Notebook
06:58 Working with numpy and pandas and other libraries
07:50 Downloading stock data
12:00 Working with data
13:00 Read and writing Data
21:35 Separating and Segregating Data
23:00 Data visualization and graphs
26:00 Normalization
30:00 Making changes and creating new data
42:00 Deleting Data
44:40 Resampling Data
48:50 Histogram Graph
50:30 Mean, Variance and Standard Deviation
01:00:00 Scatter Plot
01:00:40 Stock Comparison with risk metric
01:01:50 For loops
01:09:00 Correlation and Covariance
01:10:20 Heat map
01:15:00 Challenge 1
01:16:00 Simple and Log returns
01:23:00 Creating Moving averages data
01:32:15 Challenge 2
01:35:00 Reindexing
01:37:45 Forward fill and Backfill
01:41:00 Cumulative returns and drawdowns
01:57:00 Creating and Backtesting Strategies
02:09:00 Comparison to buy and hold
02:11:40 Long bias Strategy
02:16:00 Challenge 3
02:16:30 Creating a function
02:26:30 Creating a class
02:43:30 Importing and Using a Class
02:48:45 Challenge 4
02:51:00 API
02:52:00 Working with API

Disclaimer: The contents provided in the channel are purely educational. We do not provide any financial or investment advice. There is a very high degree of risk involved in trading. Past results are not indicative of future returns. quantprogram.com and all individuals affiliated with this site assume no responsibilities for your trading and investment results. The contents, videos, columns, articles and all other features are for educational purposes only and should not be construed as investment advice.

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