feat: Phase 2 — economic event surprise tracker (FRED actuals + z-score)
- 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>
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OpenSquared
@@ -255,49 +255,99 @@ def extract_tags(text: str) -> List[str]:
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# ── Economic calendar (using free Trading Economics RSS or static) ────────────
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_FRED_SERIES_TO_CALENDAR = {
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"PAYEMS": "US Non-Farm Payrolls",
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"CPIAUCSL": "US CPI (Consumer Price Index)",
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"UNRATE": "US Unemployment Rate",
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"GDP": "US GDP (Preliminary)",
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"ICSA": "US Initial Jobless Claims",
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"FEDFUNDS": "Fed Funds Rate (FOMC)",
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"T10Y2Y": "10Y-2Y Yield Spread",
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}
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def get_economic_calendar() -> List[Dict[str, Any]]:
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"""Return next 30 days of major economic events (static + scraped)."""
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"""Return recent + upcoming major economic events, enriched with FRED actuals from DB."""
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from datetime import date, timedelta
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today = date.today()
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events = [
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{"title": "US Non-Farm Payrolls", "country": "US", "importance": "high",
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upcoming_static = [
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{"title": "US Non-Farm Payrolls", "country": "US", "importance": "high",
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"date": (today + timedelta(days=(4 - today.weekday()) % 7 + 7)).isoformat(),
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"asset_impact": ["indices", "forex", "rates"]},
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{"title": "US CPI (Consumer Price Index)", "country": "US", "importance": "high",
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{"title": "US CPI (Consumer Price Index)", "country": "US", "importance": "high",
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"date": (today + timedelta(days=12)).isoformat(),
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"asset_impact": ["indices", "forex", "metals"]},
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{"title": "FOMC Meeting / Fed Rate Decision", "country": "US", "importance": "high",
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{"title": "FOMC Meeting / Fed Rate Decision","country": "US", "importance": "high",
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"date": (today + timedelta(days=18)).isoformat(),
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"asset_impact": ["indices", "forex", "metals", "energy"]},
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{"title": "ECB Rate Decision", "country": "EU", "importance": "high",
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{"title": "ECB Rate Decision", "country": "EU", "importance": "high",
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"date": (today + timedelta(days=20)).isoformat(),
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"asset_impact": ["forex", "indices"]},
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{"title": "US GDP (Preliminary)", "country": "US", "importance": "high",
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{"title": "US GDP (Preliminary)", "country": "US", "importance": "high",
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"date": (today + timedelta(days=25)).isoformat(),
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"asset_impact": ["indices", "forex"]},
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{"title": "OPEC+ Meeting", "country": "Global", "importance": "high",
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{"title": "OPEC+ Meeting", "country": "Global", "importance": "high",
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"date": (today + timedelta(days=14)).isoformat(),
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"asset_impact": ["energy"]},
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{"title": "US Crude Oil Inventories (EIA)", "country": "US", "importance": "medium",
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{"title": "US Crude Oil Inventories (EIA)", "country": "US", "importance": "medium",
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"date": (today + timedelta(days=3)).isoformat(),
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"asset_impact": ["energy"]},
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{"title": "EU Inflation (CPI)", "country": "EU", "importance": "medium",
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{"title": "EU Inflation (CPI)", "country": "EU", "importance": "medium",
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"date": (today + timedelta(days=8)).isoformat(),
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"asset_impact": ["forex", "indices"]},
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{"title": "China Trade Balance", "country": "CN", "importance": "medium",
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{"title": "China Trade Balance", "country": "CN", "importance": "medium",
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"date": (today + timedelta(days=10)).isoformat(),
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"asset_impact": ["metals", "agriculture", "forex"]},
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{"title": "US Unemployment Claims", "country": "US", "importance": "medium",
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{"title": "US Unemployment Claims", "country": "US", "importance": "medium",
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"date": (today + timedelta(days=2)).isoformat(),
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"asset_impact": ["forex", "indices"]},
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{"title": "USDA Crop Report", "country": "US", "importance": "medium",
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{"title": "USDA Crop Report", "country": "US", "importance": "medium",
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"date": (today + timedelta(days=6)).isoformat(),
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"asset_impact": ["agriculture"]},
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{"title": "G7 Summit", "country": "Global", "importance": "high",
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{"title": "G7 Summit", "country": "Global", "importance": "high",
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"date": (today + timedelta(days=30)).isoformat(),
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"asset_impact": ["forex", "indices", "metals"]},
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]
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return sorted(events, key=lambda x: x["date"])
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# Merge past FRED actuals from DB
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try:
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from services.database import get_economic_events_for_calendar
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db_events = get_economic_events_for_calendar(days_back=45, days_forward=0)
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past_events = []
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for ev in db_events:
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actual = ev.get("actual_value")
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forecast = ev.get("forecast_value")
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previous = ev.get("previous_value")
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unit = ev.get("actual_unit", "")
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zscore = ev.get("surprise_zscore")
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past_events.append({
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"title": ev.get("event_name", ev.get("series_id", "")),
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"country": "US",
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"importance": "high" if abs(zscore or 0) >= 1.5 else "medium",
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"date": ev.get("event_date", ""),
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"asset_impact": ev.get("assets_impacted", []),
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"actual": f"{actual:.2f}{unit}" if actual is not None else None,
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"forecast": f"{forecast:.2f}{unit}" if forecast is not None else None,
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"previous": f"{previous:.2f}{unit}" if previous is not None else None,
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"surprise_zscore": zscore,
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"surprise_direction": ev.get("surprise_direction"),
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"source": "FRED",
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})
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except Exception:
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past_events = []
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all_events = past_events + upcoming_static
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# Deduplicate by date+title (past events take precedence)
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seen = set()
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result = []
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for ev in sorted(all_events, key=lambda x: x["date"], reverse=True):
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key = (ev["date"], ev["title"][:20])
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if key not in seen:
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seen.add(key)
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result.append(ev)
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return sorted(result, key=lambda x: x["date"])
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# ── Macro Gauges & Scenario Scoring ──────────────────────────────────────────
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