Hello World: Welcome to Automated Stock Market Blog
Welcome to my very first blog post where I introduce an exciting new project—a fully automated stock market blog built using Python and cutting-edge AI tools. This project gathers market data from multiple sources, processes the data with custom prompt templates, and leverages a language model to produce a seamless, human-like blog post.
What Am I Doing?
- Data Collection: I make direct HTTP calls to various endpoints to retrieve the latest ticker summaries, index data, and news articles. To ensure I don’t exceed the rate limit of 10 requests per minute, I’ve implemented a token‐bucket rate limiter that pauses requests when needed.
- Data Processing: Each data source returns raw output that might be a DataFrame, dictionary, list, or even a simple string. I use a helper function to check if the output is empty and then format the data using custom prompts (like “news” or “tweet”) that standardize the structure of the content.
- LLM-Powered Merging: Once all outputs are processed, they’re stored individually and then merged using a dedicated merge prompt. This step ensures that the final blog post is cohesive, well-structured, and formatted in HTML for WordPress. I even include tables, bullet points, headers, and emojis where appropriate.
Why Automate?
By automating the entire process—from fetching and processing data to generating a polished blog post—I can keep the content up-to-date with the latest market trends without manual intervention. Whether it’s pre-market insights or after-market summaries, this system is designed to deliver comprehensive market analysis quickly and efficiently.
Disclaimer: The content provided by this blog is generated programmatically for informational purposes only and should not be considered financial advice.