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Indicator Dash – Rank and compare to mitigate crypto market uncertainty

Hacker News

Indicator Dash – Rank and compare to mitigate crypto market uncertainty

For the last two years, I have been hard at work to launch Indicator Dash, along with its parent company, Hex Datum OÜ ( https://hexdatum.com/ ). Before that, I was an academic for over 10 years, a career that culminated with my PhD thesis, entitled "Graph-Based Entity-Oriented Search", successfully defended in 2021. More about me here: https://josedevezas.com/ Now, a bit about Indicator Dash. Indicator Dash is a crypto analytics product where we provide normalized signals, ready to be combined by the user in a personalized manner to support learning or decision-making through rankings. I believe this approach to be a middle ground between raw indicators and algorithmic trading approaches. Here, users only need to understand what the indicator represents, but the interpretation process is already done, in the backstage, by Indicator Dash, resulting in specific signals extracted from multiple indicator statistics or configurations. This enables regular users to create custom pair ranking solutions, at the distance of a click, without the need to learn about algorithmic trading and implement their own script. Of course, this comes at a cost: only indicator interpretations implemented by us are supported, but we plan to keep expanding our indicator signals for a long time! We also collect, categorize, classify, and index crypto news articles, extracting and organizing entities into a knowledge graph. All this content is available through our search engines, or through beautiful ego network visualizations, that let you explore crypto contexts, to learn about new actors in the field and discover new coins. If you'd like to test Indicator Dash, you can sign up for a free account, or use the coupon, specifically created for Hacker News, to get a 50% discount on basic or premium accounts until October 2022. The coupon can only be redeemed 10 times, so get it while it's fresh! You can also watch our demo video here, if you'd prefer: https://youtu.be/Pa5WujQ8bXA Let me know what you like, dislike, or would like to see in Indicator Dash! Your feedback is invaluable. If you'd like to structure feedback, you can also leave an entry here: https://feedback.indicatordash.app/ And thank you for taking the time to learn about our first Hex Datum product!

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
95%95% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, users · Missing: mobile apps, ios, entrepreneurs
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, hacker news, ide · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, context · Missing: mac, agents, macos
28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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 · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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