Sa

SaaS for analyzing Bitcoin price movements

Hacker News

SaaS for analyzing Bitcoin price movements

Hello everyone! Our software offers a SaaS for analyzing Bitcoin price movements. We built an MVP model which recognizes levels in which BTC is at the top or bottom of the cycle. It is primarily intended for investors who are not financially sophisticated as a possible help to protect their investment. A short story about the creation of the product: A few of us, friends and ourselves, have always been maddened by the regular loss of the largest part of unrealized profit during bear markets which tend to be very hard on retail investors.On the other hand, a lot of friends blindly invested into bitcoin at ridiculous price levels based on hypes, bad instagram and YouTube influencers and mouth of word. We think that a large part of BTC and crypto investors live in a similar situation. With this goal, we teamed up with experts in the field of data science and finance, and created an algorithm that gives the BTC price score based on crucial market parameters. This score shows excellent results in real time and on backtesting. Our ultimate goal is to create a platform with various features, which will help retail investors to better navigate through the market, and to protect them from large losses. Therefore, we assumed that our USP has its audience among people who invest in BTC, and who had similar experiences in the bear market as we did. With the help of a landing page and paid Ads we want to test if a product market fit is possible with such a service. Unfortunately this turned out harder than we though. The problem lies in the fact the most popular social networks, among them Twitter as well, have problems with crypto advertisements. Although by definition we do not provide any financial product or services, not even financial advice, it may be that our service is interpreted that way and that we belong to a group where the space for marketing on social platforms is limited, rightly or wrongly. And we don’t know if we can change or adjust it at all, which drastically slows down our strategy of defining the product market fit. With that, we ask for feedback or advises to the following questions: -What do you think of our product? And do you think it is an idea that could be useful for BTC investors? If you don’t think so, how would you change/improve the product? -Is our product, like it is now, considered as financial advice? And is there anyway we can differentiate this kind of product from such a label? -How would you implement the product market fit strategy of our product in the fastest and most favorable way, if you could not publish advertisements on social networks? We are very grateful for any constructive advice or idea! You can find out more on our landing page (link above). Thank you! Best regards, Mladen Pavlovic

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: model · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit, saas · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto, real time, paid · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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