Market Insights Server
by jashchawla8
A real-time commodity tracking system that uses Apache Spark, OpenAI GPT, and the MCP protocol to generate actionable market insights. It collects data from various sources, processes it with Spark, and uses GPT-4 for natural language insights.
Last updated: N/A
What is Market Insights Server?
The Market Insights Server is a system designed to provide real-time tracking and analysis of commodity markets. It leverages Apache Spark for scalable data processing, OpenAI GPT-4 for generating natural language insights, and the MCP protocol for data handling.
How to use Market Insights Server?
To use the server, install the required dependencies using pip install -r requirements.txt. Then, run the spark_market_insights_server.py script, specifying the commodity you want to analyze using the --commodity flag (e.g., python spark_market_insights_server.py --commodity "nickel"). The server will output cleaned text data, TF-IDF features, a GPT-4-powered insight report, and a JSON export of insights.
Key features of Market Insights Server
Real-time data collection from Reddit, News APIs, and Yahoo Finance
Scalable processing using Apache Spark (PySpark 3.5.0)
Natural language insights powered by GPT-4
Configurable for any commodity market
Built-in dynamic configuration generation and subreddit discovery
Ready for deployment with error handling, retries, and async collection
Use cases of Market Insights Server
Analyzing market sentiment for specific commodities
Identifying emerging trends in commodity markets
Generating actionable insights for traders and investors
Monitoring the impact of news events on commodity prices
FAQ from Market Insights Server
Spark stage stuck
Spark stage stuck
Check memory settings, repartition input
API returns 429
API returns 429
Add backoff/retry logic, rotate API keys
GPT returns empty
GPT returns empty
Use latest models, tune prompt
How does it collect data?
How does it collect data?
It scrapes Reddit and news articles asynchronously and uses Yahoo Finance for live price feeds.
What kind of processing is done?
What kind of processing is done?
Tokenization, stop words removal, and TF-IDF vectorization are performed using Apache Spark and Spark NLP pipelines.