Screeners

Your Fundamental Analysis school gave you questions: is ROCE consistently high? Is debt manageable? Does profit convert to cash? A screener is simply those questions, asked of every listed company simultaneously — a filter that turns 2,000 stocks into a shortlist of 20 that deserve your ten-minute human look. It's the first honest division of labor in your stack: machines filter, humans judge.

Translating your schools into filters — worked examples on real, free tools: On screener.in (its query language reads almost like English), your FA school's quality checks become literal filter lines: ROCE > 15 AND Debt to equity < 0.5 AND Sales growth 5Years > 10 — the moat-verification, fire-inspection, and growth checks from Chapters 6, 7, and 14, running on all of India in a second. Add the honesty check from Chapter 9 (profit vs. cash) by screening on cash-flow ratios. On the technical side, most charting platforms include screeners where Market Structure concepts translate too: new 52-week highs on elevated volume (breakout candidates), stocks within X% of long bases (Chapter 14's ranges, hunted at scale).

The four screening traps — where machines mislead:

Trap 1 — The screen sees the number, not the reason. A stock passes ROCE > 20 on a one-time land sale (FA school, Chapter 24's "other income" makeup). Screens read database fields; they can't read footnotes. This is precisely why screening is the first filter, never the last — every survivor still gets the human eight-question note (FA school, Chapter 22).

Trap 2 — Overfitted filters. Stack twelve conditions and you'll produce a list of three stocks that perfectly match the past — a preview of Module 3's overfitting lesson: the more precisely a filter fits history, the less it usually says about the future. Strong screens are coarse: 3–5 conditions capturing a principle, not a portrait.

Trap 3 — Sector blindness. One-size filters judge banks by debt-to-equity (meaningless — FA school, Chapter 7's special case) and IT firms by inventory turns. Either exclude special sectors or screen them by their own dashboard (FA school, Module 9).

Trap 4 — Everyone owns the same screener. A vanilla low PE + high ROCE screen is run by lakhs of users — its output is pre-crowded. Edge in screening comes from your combinations: filters encoding your specific process (e.g., your FA red-flag checks inverted into exclusion rules — screening out rising-debtor-days companies — which almost nobody does) rather than the textbook's defaults.

The funnel, formalized: universe (2,000) → screen (20) → two-minute eyeball per survivor (kill obvious mismatches — Trap 1 casualties) → full research note on the final 3–5 → watchlist with alert levels (Chapter 4's sentries). Run the same screen on a schedule (weekly/monthly, calendared like your Behavioural Finance school's reviews) rather than on impulse — an on-impulse screen is usually confirmation bias asking the database to agree with a position you already want (Behavioural Finance school, Chapter 4, wearing a spreadsheet).