- economic_events table in DB (series_id, actual, forecast_baseline, surprise_pct, surprise_zscore, direction)
- DB helpers: save_economic_event(), get_recent_economic_surprises(), get_economic_events_for_calendar()
- fred_fetcher.py: _compute_zscore_surprise() computes 12-period MA as implied consensus + z-score deviation; save_fred_releases_to_db() persists releases per cycle; build_economic_surprise_block() formats significant surprises for AI prompt
- auto_cycle.py: saves FRED releases to economic_events each cycle, appends surprise block to fred_block for injection into both suggestion and scoring prompts
- data_fetcher.py: get_economic_calendar() now merges static upcoming events with past FRED actuals from DB (Prev/Fcst/Actual/z-score fields populated)
- CalendarPage.tsx: past events show colored z-score badge (⚡ for |z|≥1.5, bullish/bearish colors)
- EconomicEvent type: added surprise_zscore, surprise_direction, source fields
Activates automatically once fred_api_key is set in Configuration.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace 1-day change_pct with weighted blend (20% 1d / 50% 5d / 30% 10d)
for all 20 scored signals. Add Bayesian prior from rolling 25-day history
(weight 15%→45%) and a 10-point persistence threshold before regime switch.
Bootstrap on first load: replays last 20 trading days via yf.download batch
(45d) to pre-populate _regime_history, so stability is visible immediately.
Frontend: adds 'stable Xj' badge and history depth indicator on regime banner.
Doc: updates v4.0→v4.1, rewrites Étape 1 Régime Macro and glossary entry.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
