Should I Build It Should I Build It
19 Jul 2025
Investing

An app that scans stock platforms like Yahoo Finance or Degiro to list ...

...the ‘Strong buy’ stocks and their predicted future values

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

Your idea of an app that scans stock platforms for 'Strong buy' stocks and their predicted future values puts you in a crowded space. Our analysis shows 13 similar products already exist, indicating high competition. The 'Swamp' category description fits well: many mediocre solutions are out there, and it's tough to stand out. While the average engagement on these similar products is low, it suggests that users aren't necessarily raving about existing solutions. The lack of use and buy signals from the similar products indicate that it's hard to get any traction in this field. This isn't to discourage you, but to highlight the challenges ahead; you'll need a genuinely unique approach to succeed.

Recommendations

  1. Deeply research why existing stock analysis solutions haven’t achieved widespread success. Look beyond surface-level features and try to understand the underlying reasons for user dissatisfaction or lack of adoption. Are the predictions inaccurate? Is the interface clunky? Is the data outdated? Dig deep to uncover the real pain points.
  2. Identify a specific niche within the investing world that is currently underserved. Instead of targeting all investors, focus on a particular segment like ethical investors, dividend-focused retirees, or crypto-curious newcomers. Tailor your app's features and marketing to this specific audience to gain a foothold.
  3. Consider creating tools or plugins for existing stock analysis platforms like TradingView or MetaStock. Instead of building a standalone app, integrate your unique 'Strong buy' signal into a popular platform to reach a wider audience and leverage their existing infrastructure.
  4. Explore adjacent problems within the financial space that might be more promising. Instead of focusing solely on stock recommendations, consider addressing issues like portfolio tracking, tax optimization, or financial literacy. These areas might have less competition and greater potential for innovation.
  5. Based on the criticism from similar products, prioritize customizability of AI-driven suggestions. Users want more control over the recommendations they receive. Allow them to adjust parameters, set preferences, and fine-tune the algorithms to match their individual investment styles.
  6. Learn from Alpha Insights and Stock Analysis Platform and make sure your service is available in as many markets as possible and avoid the need for mandatory sign-up to create custom screeners and watchlists.

Questions

  1. What specific, unique data sources or algorithms will your app use to generate 'Strong buy' signals that are demonstrably better than existing solutions? How will you validate and prove the accuracy of these predictions?
  2. How will you address the common criticism that AI-driven stock analysis tools lack real-time analysis capabilities and predictive accuracy, especially in volatile market conditions? What measures will you take to mitigate these limitations?
  3. Considering the low engagement with similar products, what innovative strategies will you employ to build a loyal user base and foster a thriving community around your app? How will you differentiate your marketing and communication efforts to stand out from the crowd?

Your are here

Your idea of an app that scans stock platforms for 'Strong buy' stocks and their predicted future values puts you in a crowded space. Our analysis shows 13 similar products already exist, indicating high competition. The 'Swamp' category description fits well: many mediocre solutions are out there, and it's tough to stand out. While the average engagement on these similar products is low, it suggests that users aren't necessarily raving about existing solutions. The lack of use and buy signals from the similar products indicate that it's hard to get any traction in this field. This isn't to discourage you, but to highlight the challenges ahead; you'll need a genuinely unique approach to succeed.

Recommendations

  1. Deeply research why existing stock analysis solutions haven’t achieved widespread success. Look beyond surface-level features and try to understand the underlying reasons for user dissatisfaction or lack of adoption. Are the predictions inaccurate? Is the interface clunky? Is the data outdated? Dig deep to uncover the real pain points.
  2. Identify a specific niche within the investing world that is currently underserved. Instead of targeting all investors, focus on a particular segment like ethical investors, dividend-focused retirees, or crypto-curious newcomers. Tailor your app's features and marketing to this specific audience to gain a foothold.
  3. Consider creating tools or plugins for existing stock analysis platforms like TradingView or MetaStock. Instead of building a standalone app, integrate your unique 'Strong buy' signal into a popular platform to reach a wider audience and leverage their existing infrastructure.
  4. Explore adjacent problems within the financial space that might be more promising. Instead of focusing solely on stock recommendations, consider addressing issues like portfolio tracking, tax optimization, or financial literacy. These areas might have less competition and greater potential for innovation.
  5. Based on the criticism from similar products, prioritize customizability of AI-driven suggestions. Users want more control over the recommendations they receive. Allow them to adjust parameters, set preferences, and fine-tune the algorithms to match their individual investment styles.
  6. Learn from Alpha Insights and Stock Analysis Platform and make sure your service is available in as many markets as possible and avoid the need for mandatory sign-up to create custom screeners and watchlists.

Questions

  1. What specific, unique data sources or algorithms will your app use to generate 'Strong buy' signals that are demonstrably better than existing solutions? How will you validate and prove the accuracy of these predictions?
  2. How will you address the common criticism that AI-driven stock analysis tools lack real-time analysis capabilities and predictive accuracy, especially in volatile market conditions? What measures will you take to mitigate these limitations?
  3. Considering the low engagement with similar products, what innovative strategies will you employ to build a loyal user base and foster a thriving community around your app? How will you differentiate your marketing and communication efforts to stand out from the crowd?

  • Confidence: High
    • Number of similar products: 13
  • Engagement: Low
    • Average number of comments: 2
  • Net use signal: 8.6%
    • Positive use signal: 14.5%
    • Negative use signal: 5.9%
  • Net buy signal: -2.4%
    • Positive buy signal: 3.4%
    • Negative buy signal: 5.9%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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