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Real-Time tick by tick crypto data in Node.js and without callbacks

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Real-Time tick by tick crypto data in Node.js and without callbacks

Hi, I've released Node.js lib that provides real-time market data feeds for top crypto exchanges. It handles data normalization, order book reconstruction, custom trade bars (eg volume based etc), consolidates multiple feeds into one etc. and connects directly to exchanges APIs via WebSockets, has built-in reconnection and stale connection detection logic and more. It also uses async iteration (for await..of) instead of callbacks. https://github.com/tardis-dev/tardis-node

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
56%56% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% 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 · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apis · Missing: mac, agents, macos
29%29% 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
19%19% 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
11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

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