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Quantarded, extracting WSB stock signals using OpenAI

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Quantarded, extracting WSB stock signals using OpenAI

I built Quantarded, a side project that turns two noisy public data sources into conservative weekly stock signals. For Reddit (r/wallstreetbets), it uses OpenAI strictly as a semantic parser to extract tickers and buy/sell/neutral intent from text, then applies a mechanical weekly scoring model (recency decay, attention share, buy/sell imbalance). No fundamentals, no price features, no user weighting. For U.S. House trade disclosures, it uses a separate, slower model focused on credibility and position building rather than timing. It’s intentionally not a trading bot or a prediction engine. I publish weekly snapshots and performance publicly, including weeks where signals are weak or inconclusive. Would love feedback, especially on failure modes or things you’d want to see to trust (or falsify) a system like this.

Visit productView on Hacker NewsJan 28, 2026
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, openai · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: trading, para · Missing: mobile apps, ios, personal
51%51% 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, including · Missing: https docs, excited, just released
42%42% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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