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>

backend/services/database.py

@@ -576,6 +576,29 @@ def init_db():
     except Exception:
         pass
 
+    c.execute("""CREATE TABLE IF NOT EXISTS economic_events (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        event_name TEXT NOT NULL,
+        series_id TEXT NOT NULL,
+        event_date TEXT NOT NULL,
+        actual_value REAL,
+        actual_unit TEXT DEFAULT '',
+        forecast_value REAL,
+        previous_value REAL,
+        surprise_pct REAL,
+        surprise_zscore REAL,
+        surprise_direction TEXT DEFAULT 'neutral',
+        assets_impacted TEXT DEFAULT '[]',
+        source TEXT DEFAULT 'FRED',
+        fetched_at TEXT NOT NULL DEFAULT (datetime('now')),
+        UNIQUE(series_id, event_date)
+    )""")
+    try:
+        c.execute("CREATE INDEX IF NOT EXISTS idx_econ_date ON economic_events(event_date DESC)")
+        c.execute("CREATE INDEX IF NOT EXISTS idx_econ_series ON economic_events(series_id, event_date DESC)")
+    except Exception:
+        pass
+
     try:
         c.execute("CREATE INDEX IF NOT EXISTS idx_kb_category ON knowledge_base(category, status)")
         c.execute("CREATE INDEX IF NOT EXISTS idx_rs_version ON reasoning_state(version DESC)")
@@ -3742,3 +3765,99 @@ def get_latest_trade_assessments(limit: int = 20) -> List[Dict[str, Any]]:
             d["issues"] = []
         result.append(d)
     return result
+
+
+# ── Economic events (Phase 2) ─────────────────────────────────────────────────
+
+def save_economic_event(
+    event_name: str,
+    series_id: str,
+    event_date: str,
+    actual_value: Optional[float],
+    actual_unit: str = "",
+    forecast_value: Optional[float] = None,
+    previous_value: Optional[float] = None,
+    surprise_pct: Optional[float] = None,
+    surprise_zscore: Optional[float] = None,
+    surprise_direction: str = "neutral",
+    assets_impacted: Optional[List] = None,
+    source: str = "FRED",
+) -> Optional[int]:
+    conn = get_conn()
+    try:
+        conn.execute(
+            """INSERT INTO economic_events
+               (event_name, series_id, event_date, actual_value, actual_unit,
+                forecast_value, previous_value, surprise_pct, surprise_zscore,
+                surprise_direction, assets_impacted, source)
+               VALUES (?,?,?,?,?,?,?,?,?,?,?,?)
+               ON CONFLICT(series_id, event_date) DO UPDATE SET
+                 actual_value=excluded.actual_value,
+                 forecast_value=excluded.forecast_value,
+                 previous_value=excluded.previous_value,
+                 surprise_pct=excluded.surprise_pct,
+                 surprise_zscore=excluded.surprise_zscore,
+                 surprise_direction=excluded.surprise_direction,
+                 fetched_at=datetime('now')""",
+            (
+                event_name, series_id, event_date, actual_value, actual_unit,
+                forecast_value, previous_value, surprise_pct, surprise_zscore,
+                surprise_direction,
+                json.dumps(assets_impacted or []),
+                source,
+            ),
+        )
+        conn.commit()
+        return conn.execute("SELECT last_insert_rowid()").fetchone()[0]
+    finally:
+        conn.close()
+
+
+def get_recent_economic_surprises(days: int = 30, min_zscore: float = 0.0) -> List[Dict[str, Any]]:
+    conn = get_conn()
+    try:
+        cutoff = (datetime.utcnow() - timedelta(days=days)).strftime("%Y-%m-%d")
+        rows = conn.execute(
+            """SELECT * FROM economic_events
+               WHERE event_date >= ?
+                 AND ABS(COALESCE(surprise_zscore, 0)) >= ?
+               ORDER BY event_date DESC, ABS(COALESCE(surprise_zscore, 0)) DESC
+               LIMIT 20""",
+            (cutoff, min_zscore),
+        ).fetchall()
+        result = []
+        for r in rows:
+            d = dict(r)
+            try:
+                d["assets_impacted"] = json.loads(d.get("assets_impacted") or "[]")
+            except Exception:
+                d["assets_impacted"] = []
+            result.append(d)
+        return result
+    finally:
+        conn.close()
+
+
+def get_economic_events_for_calendar(days_back: int = 30, days_forward: int = 14) -> List[Dict[str, Any]]:
+    """Return stored economic events for calendar enrichment."""
+    conn = get_conn()
+    try:
+        cutoff_past = (datetime.utcnow() - timedelta(days=days_back)).strftime("%Y-%m-%d")
+        cutoff_future = (datetime.utcnow() + timedelta(days=days_forward)).strftime("%Y-%m-%d")
+        rows = conn.execute(
+            """SELECT * FROM economic_events
+               WHERE event_date BETWEEN ? AND ?
+               ORDER BY event_date DESC""",
+            (cutoff_past, cutoff_future),
+        ).fetchall()
+        result = []
+        for r in rows:
+            d = dict(r)
+            try:
+                d["assets_impacted"] = json.loads(d.get("assets_impacted") or "[]")
+            except Exception:
+                d["assets_impacted"] = []
+            result.append(d)
+        return result
+    finally:
+        conn.close()