- Revenue quality: Recurring vs one-time. A company with 80% recurring revenue is worth more than 100% transactional at same margins.
- ROIC trend over 5-10 years, not just current. Declining ROIC often precedes margin compression.
- FCF yield vs earnings yield. Big divergence = accounting shenanigans or capex cycle.
- Insider buying/selling. Insiders are often early on inflection points.
For workflow: ability to save screens and get alerts when new stocks qualify would be sticky.
A few patterns that stress different parts of the engine:
Window functions (tests optimizer):
SELECT , ROW_NUMBER() OVER (PARTITION BY category ORDER BY date DESC)
FROM large_table
Large aggregations (tests hash tables):
SELECT category, COUNT(), AVG(value), PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY value)
FROM large_table GROUP BY category
Self-joins (tests join algorithms):
SELECT a., b. FROM table a
JOIN table b ON a.foreign_key = b.id
WHERE a.date BETWEEN '2024-01-01' AND '2024-12-31';
String operations (often overlooked):
SELECT * FROM table WHERE column LIKE '%pattern%';
Also test with varying data sizes (1K, 100K, 10M rows). Some engines optimize differently at scale.
1. Scan economic calendar: Takes 2 min to see what Fed/earnings announcements are coming. Helps contextualize news later.
2. Review one chart/metric: Pick one stock or index, look at 1-year trend. Not trading, just pattern recognition over time. After a few months you start noticing things faster.
Both are low-effort but compound, you build intuition without committing to deep analysis.
Cool idea. One thing that could differentiate this from "another budgeting app":
Add market context to the coaching. Right now most finance apps show spending in isolation. But "should I invest?" is different when:
- S&P is at ATH vs down 20%
- Fed just raised rates vs cutting
- Your industry is laying off vs hiring
A few data points that could make the coaching smarter:
- Historical market returns (so "invest in index funds" has numbers behind it)
- Inflation rate (to show why cash loses value)
- Average savings rate by age bracket (peer comparison)
The "memory" system you built could remember their risk tolerance AND market conditions when they made past decisions, then reference both.
A few metrics I'd want to see:
- Revenue quality: Recurring vs one-time. A company with 80% recurring revenue is worth more than 100% transactional at same margins. - ROIC trend over 5-10 years, not just current. Declining ROIC often precedes margin compression. - FCF yield vs earnings yield. Big divergence = accounting shenanigans or capex cycle. - Insider buying/selling. Insiders are often early on inflection points.
For workflow: ability to save screens and get alerts when new stocks qualify would be sticky.
What data source are you using for fundamentals?