Let’s break down your ambitious dream step by step. You want to build (or assemble, or prompt-engineer) something that:

1. Monitors social media and other platforms (Twitter/X, Instagram, TikTok, Reddit, YouTube, Telegram, dark web) for signals or information.
2. Identifies trading opportunities in real-time or near real-time (e.g., big announcements, influencer mentions, unusual volume/spikes, etc.).
3. Discovers unconventional/“borderline” business ideas (like ticket reselling, renting out GPUs, arbitrage opportunities) that aren’t mainstream yet.

You’re basically describing a pipeline or system that continuously ingests a firehose of online data, filters and prioritizes that data, then surfaces insights about “who said what” and “why it might matter” so you can act on it quickly. Below, I’ll walk through the major components, challenges, required skills, and nuances. Afterward, we can refine each area in more detail.

1. Data Collection

1.1. Web Scraping & APIs

• Challenge: Many platforms (Twitter/X, Reddit, Telegram, TikTok, etc.) have strict APIs or rate limits. Scraping them might violate Terms of Service if not done carefully. Dark web scrapers require specialized tooling and strong security measures.
• Skills:
  • Python (common scraping libraries: requests, BeautifulSoup, Selenium, or Playwright)
  • Familiarity with each platform’s API or unofficial APIs (e.g., the Reddit API, Twitter API, etc.)
  • DevOps or cloud knowledge to handle large-scale scraping reliably (think AWS Lambda, Kubernetes, etc.)

1.2. Data Pipeline & Storage

• Challenge: Handling large volumes of unstructured text data (and possibly images/videos if you want to parse them for sentiment or mention extraction).
• Skills:
  • Setting up databases/data lakes (SQL, NoSQL, or object storage like S3)
  • Real-time stream processing (Kafka, RabbitMQ, or frameworks like Apache Flink/Spark Streaming)

Nuance: You might not need to store all data. You can do some “pre-filtering” or real-time triage (e.g., ignoring random chatter; only saving posts from certain influencers/keywords).

2. Preprocessing & Filtering

2.1. Language Processing

• Challenge: You want to pull key signals out of huge amounts of text. That could include:
  • Named Entity Recognition (NER) to identify companies, people, and tickers.
  • Sentiment analysis to gauge positive/negative mood.
  • Topic classification to see if something’s related to finance, technology, etc.
• Skills:
  • NLP fundamentals (tokenization, embedding, sentiment classification)
  • Frameworks like spaCy, NLTK, Hugging Face Transformers
  • Possibly building or fine-tuning your own LLM or using smaller specialized models (for finance, for instance).

2.2. Prioritizing Sources & Quality