- Uses a separate universe and benchmark from the India engine.
- Tracked backtests are encouraging, especially on risk-adjusted metrics.
- Useful for watchlist building and comparative research, not for blind execution.
KR
KRISHA
Knowledge-Rich Investment Screening, Heuristics, and Analysis
Orientation
Phase 3
US stocks
Use the US module when you want a smaller list of US equities to compare before deeper work.
Available now
S&P 500
2026.03
Where it fits
Best uses and visible cautions
Start with fit first. If the module does not match your market or your problem, the rest of the evidence will not save the workflow.
- Still best treated as research support, not as a stand-in for your own valuation work.
- Use it for a structured shortlist, then do deeper business-quality and valuation checks.
How it works
Engine focus, market-native assumptions, and learning links
This section explains what the lane is optimized for and which market-specific frictions or assumptions shape the shortlist.
Optimized for large, liquid US names where benchmark concentration, earnings cycles, and megacap leadership shape most shortlist behavior.
- Expanded large-cap universe with liquidity floor checks.
- Dedicated benchmark and backtest outputs for the module.
- Coverage diagnostics surfaced in the research output.
- US large caps are benchmark-heavy and narrative shifts often show up through megacap concentration first.
- The module works best as a cleaner way to compare large, liquid names before deeper quality and valuation work.
- US results still need tax, currency, and execution context outside the engine.
- A clean shortlist can still hide valuation stretch or event-risk around earnings cycles.
Proof and risks
Diagnostics, walk-forward evidence, and failure modes
This is the evidence layer. It should make the module easier to trust in context, not easier to over-trust.
- US results still need tax, currency, and execution context outside the engine.
- A clean shortlist can still hide valuation stretch or event-risk around earnings cycles.
- Still best treated as research support, not as a stand-in for your own valuation work.
- Use it for a structured shortlist, then do deeper business-quality and valuation checks.
- Fast regime reversals, event shocks, and crowded benchmark leadership can all make a clean-looking screen lag badly.
Walk-forward export is not published for this module yet.
Data transparency
What powers this page and where it can still fail
Last refresh2026-04-16
- Market prices are refreshed into a local store from the current KRISHA ingestion pipeline.
- Public pages and diagnostics are rendered from local snapshots and generated reports.
- Price-led screens do not capture valuation, filings, governance, taxes, or venue-specific execution risk.
- Some modules rely on current scope universes rather than a fully point-in-time constituent history.
- Historical reports may still be affected where the input universe is not fully point-in-time. Diagnostics disclose this explicitly instead of hiding it.
- Regime overlays use lagged benchmark information so the public state does not peek at same-day closes it could not have known in advance.
- United States symbols are normalized to market-native form such as AAPL.
Next step
Run a fresh review
When you are ready, go back to the main KRISHA page and run a fresh review with the market you want to check.