Coreopsis | Frequently Asked Questions

Q: What time are articles published?
A: Typically by 5:35 PM ET the evening prior—ready before the 9:30 AM ET open.

Q: When do I enter a trade?
A: tag: coreopsis - The model uses a 12:00 PM ET cutoff. Entries are evaluated between noon and close on Day 1.

Q: What if it gaps hard at the open?
A: Each article includes expected gap/trend context. A large divergence from expectation is useful information—sometimes a “no trade” signal in disguise.

Q: Can I adjust stops and targets?
A: Yes. They’re volatility-anchored guidelines. Adjust based on your risk tolerance.

Q: What if I miss the entry zone?
A: Don’t chase. Markets produce infinite opportunities; chasing increase immeasurable risk.

Coreopsis | What is Entry Upper and Entry Lower ?

Entry Lower and Entry Upper are simply the bounds of the recommended entry zone on Day 1 (after 12:00 PM ET through the close). * Entry Lower = the lower price of the entry band * Entry Upper = the upper price of the entry band If price trades anywhere inside that band

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Q: “Today’s sheet” vs “Edge” (what’s the difference?)
A: Today’s sheet is your daily model-specific watchlist. Edge is the multi-model overlap lens.

• Today’s sheet = tickers grouped by a specific model (e.g., Coreopsis swing list)
• Edge = a cross-model view showing tickers that may appear across multiple models/services (useful when you have swing + intraday + options later)

Q: What does the model uses (and why don’t you stack extra indicators) ?
A: Bloom’s prediction pipeline uses 180+ historical features (price, volatility, momentum, volume, sector/regime inputs, multi-timescale context, etc.). The Bloom Profile is an amalgam of many signals—already.

So adding a second layer of technical indicators on top is usually double counting the same information. The page already contains the relevant technical and contextual features in a coherent, model-consistent way.

Q: How is the model trained, and how often is it retrained?
A: Bloom Profiles are powered by a proprietary modeling pipeline that combines statistical methods, machine learning, and neural-network models.

• Training history: Typically 5+ years of data (when available for a given ticker)
• Retraining cadence: Monthly, to incorporate recent market behavior while preserving stable signal structure
• Stock universe: Primarily mid-cap and larger names, where liquidity and volume support cleaner execution and more reliable scenario behavior