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.