Reliable trends start with reliable data. Our research on repeating return-window trends (20–180 trading days) is backed by a layered validation program spanning ingestion to model outputs.

1) Ingestion & Schema

• Strict datatypes, keys, and trading-calendar alignment.

• Duplicate prevention; negative or impossible values rejected.

• Corporate actions normalized (splits/dividends) with sanity checks.

2) Content Quality

• Gap detection (e.g., >3 missing trading days) and staleness alerts.

• Outlier screening via z-scores/IQR, reconciled to events (splits, halts, news).

• Cross-vendor parity checks on prices and corporate actions with defined tolerances.

3) Calculation Integrity

• Recompute rolling 20–180 day returns and annualization independently (SQL vs. Python).

• Edge-window tests ensure correct first/last eligible dates.

• Idempotence: same inputs yield identical outputs.

4) Bias & Leakage Controls

• No look-ahead: features limited to information available at the decision date.

• No survivorship bias: delisted symbols retained; index membership time-stamped.

• Corporate events mapped to preserve continuity (mergers, ticker changes).

5) Monitoring & Governance

• Freshness, completeness, and quality KPIs tracked continually.

• Versioned releases with manifests, immutable raw zone, and lineage to code commits.

• Peer review, canary runs, and incident playbooks; defects quarantined and disclosed.

6) Trend Repeatability

• Year-by-year returns and threshold flags (e.g., ≥30%, ≥40%) recomputed from adjusted data.

• “% of years meeting threshold” validated across variable analysis ranges.

Outcome
Transparent lineage, reproducible results, and rapid anomaly containment—so client decisions rest on defensible, auditable data.