Purpose: Raw, row-level prediction ledger — every macro regime prediction's full
lifecycle (created_at -> resolved_at -> outcome). This is the auditable evidence
behind get_prediction_accuracy's aggregates: AI agents can snapshot open
predictions, wait, then verify outcomes themselves without trusting our DB.
Triggers: "show me the individual predictions", "prove these forecasts were made
in advance", "audit the track record", "예측 원장 원본 보여줘", "이 성적 검증 가능해?".
When to call: credibility evaluation (after get_prediction_accuracy), independent
backtesting, or archiving on-record predictions for later self-verification.
Prerequisites: none. Pairs with get_ledger_integrity for tamper-evidence.
Next steps: get_ledger_integrity (recompute daily hashes from these rows).
Caveats: cursor pagination (id-ordered) — follow next_cursor for bulk reads.
Paper-research forecasts, not investment advice.
Output: full_data { predictions[] {id, source_category, source_regime_change,
target_market, predicted_regime_shift, lag_hours, confidence, created_at,
resolved_at, outcome, actual_regime_shift}, count, next_cursor, has_more, meta }.
Args:
target_market: filter e.g. "coin_market" / "kr_market" / "us_market"
source_category: filter e.g. "vix", "bonds", "commodities"
day: filter by created day "YYYY-MM-DD" (UTC, string prefix of created_at)
status: "all" | "resolved" | "open"
cursor: last id from previous page (0 = start)
limit: page size (max 500)
Disclaimer: Information only, not investment advice.