In this video. You’ll learn how to use Python to scrape finance news from the web, natural language processing via Hugging Face to automatically summarise that news and finally calculate sentiment related to each asset. The awesome thing is that you can do this for almost any financial asset as well.
There’s so much news and research out there on financial markets that it can be hard to keep up. But what if you could automate some of that research using Python.
That’s exactly what you’ll learn how to do in this video. You’ll learn how to use Python to scrape finance news from the web, natural language processing via Hugging Face to automatically summarise that news and finally calculate sentiment related to each asset. The awesome thing is that you can do this for almost any financial asset as well. In the video we’ll run through the pipeline using Tesla, Bitcoin and Gamestop but you could plug in virtually any stock or crypto code and have this research done for you. In fact, at the end we’ll update the pipeline in two lines and run the same analysis for Ethereum as well.
In this video, you'll learn how to:
Chapters: 0:00 - Start 0:16 - Explainer 5:07 - Install and Import Baseline Dependencies 8:12 - Setup Summarization Model 11:29 - Auto Summarize a Single Article 12:39 - Scrape a Single Article from Yahoo Finance 16:49 - Cleaning Scraped Data using Text Processing 19:53 - Summarize a Single Article using Hugging Face Transformers 26:19- Building a News and Sentiment Pipeline for TSLA, BTC and GME 28:18 - Search for Stock News URLs using Google and Yahoo Finance 39:14 - Process and strip out unwanted URLs 49:17 - Scrape Cleaned Financial News Articles 56:17 - Summarize the News Pipeline 1:05:19 - Calculate Sentiment 1:13:16 - Export Results to CSV 1:23:18 - Running the Pipeline as a Script 1:24:37 - Running the Pipeline for Different Stocks and Cryptocurrencies
Get the Code: https://github.com/nicknochnack/Stock...
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