113 Interest Score
32 Discussions
0.28 Engagement
Dec 2023 Launched

Want to know why your stock's price fell today? Just check the fnchart! We built fnchart to answer the question "what happened?" We do this by aggregating financial news, adding user generated discussion and voting, and map it onto a stock chart.

What the Community Said

fnchart is basically: Stock Chart + Reddit == facts + context. We then build on top of that a whole new way to navigate historical posts, so that you can actually look and interact with a stock chart by pulling up news & discussion from any time in the history of the stock. Use-case Scenario: You're interested in stock AAA, but you notice on the chart that it fell 30% 2 years ago, and hasn't begun recovering yet. But today you read that they are embarking on a big turn around plan. So you cl

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Hello everybody! I believe this is a really useful app. I personally have a problem to understand why stock has grown or has fell so I checked it out. I did it on MacBook and it was so tough to move the chart, i didn't understand where am I and why i'm here. I'd be very grateful for the opportunity to communicate with the founder of this startup. I believe that this idea has a great future @watr

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Great idea and implementation. I really like it. Good luck in further growth!

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Wow, fchart sounds like an incredible tool for tracking stock prices and understanding the reasons behind price fluctuations. I'm really intrigued by the idea of aggregating financial news, user discussions, and voting to provide a comprehensive view on the stock market. I have a few questions for you: 1. How do you ensure the accuracy and reliability of the financial news sources that you aggregate? 2. Can users customize the types of news sources they want to see or filter out certain sources?

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I love investing in the market - this innovative tool is pretty incredible.

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Frequently Asked Questions

A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.

Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.

Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.

The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.

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