जो PODCAST में गरजा था क्या वो मार्केट में बरसा हैं?

Ever felt a wave of skepticism when a finfluencer delivers a thunderous claim about a stock, the economy, or an entire sector? You’re not alone. The 'gyan' from podcasts has become a staple of the financial world, but with so much noise, it’s hard to tell which clouds truly bring rain.

Now that we have enough podcast videos, what if you could track their lightning strikes? What if you could see if those bold predictions actually showered investors with returns, or if they were just empty thunder?

That’s where Garaj Baras Check comes in. This tool analyzes YouTube videos from financial influencers, identifies their sentiment on specific stocks, and measures whether those stocks actually "rained" returns in the market.

Garaj Baras Check Performance

To craft this experience, the tool takes a video of a popular finance expert and understands the different stocks mentioned by them. By leveraging Gemini Models, Yahoo Finance APIs, and YouTube transcript tools, we segment the mentions by sentiment and fetch the market returns from the video upload date till now. We provide a deep-dive analysis including three-month returns, six-month returns, 1-year returns, and the CAGR, so you can see for yourself which predictions held water and which ones just evaporated.

Note: To ensure a fair analysis and let the weather play out, we only process videos that are at least 180 days old.

How It Works

So, how does Garaj Baras Check forecast the truth? Here’s a simple breakdown of the process:

  1. Start with a YouTube video: You provide a link to a video from a financial influencer.
  2. Check the date: We only analyze videos at least six months old to give the market enough time to react to the influencer's thunder.
  3. Listen with AI: We use AI to "watch" the video, read the transcript, and gauge the sentiment—whether they are positive, negative, or neutral about a specific company.
  4. Separate lightning from static: The AI filters for publicly traded Indian companies, ensuring we focus only on relevant data.
  5. Do the math: We check the price on the day the "storm" started (upload date) and compare it to current prices to calculate returns.
  6. Get the results: We present the data in a clear table, so you can see if the predictions actually trickled down to the bottom line.

We also use a caching system. If a video has been "checked" before, you get the results instantly without waiting for the clouds to clear again.

Logic Flow

graph TD A[YouTube URL] --> B[Metadata Fetcher] B --> C{Age Check > 180d?} C -- No --> D[Skip: Too Recent] C -- Yes --> E[Transcript Fetcher] E --> F[Gemini: Sentiment Extraction] F --> G[Gemini: Stock Validation] G --> H{Is Listed Indian Stock?} H -- Yes --> I[YFinance: Price Lookup] I --> J[Performance Calculation] J --> K[Terminal Table & CSV Output] subgraph Caching Layer E -.-> L[(Video-Centric Cache)] F -.-> L G -.-> L I -.-> M[(Prices Cache)] end

If you want to try this out, shoot me a message on LinkedIn:

Disclaimer: Financial markets involve high risk. The data provided by Garaj Baras Check is generated by an AI model and is for educational purposes only. It does NOT constitute professional financial advice. AI can make mistakes. Always consult with a certified financial advisor before making any investment decisions.