feat: Impact Monitor — AI impact evaluation for eco events & geopolitical news
- New DB tables: event_categories (18 bootstrap categories) + instrument_impacts - impact_categories_bootstrap.py: FOMC/NFP/CPI/GDP/PCE/ISM/BOJ/ECB/BOE/OPEC+ + 7 géopolitical categories with per-instrument sensitivity & direction defaults - impact_service.py: GPT-4o-mini evaluation (0-1 score, direction, rationale) + monitor data aggregation - routers/impact.py: GET/POST endpoints for evaluate/bulk/monitor/adjust/categories - ImpactMonitor.tsx: two-tab page (Évalués / À évaluer), top instruments bar, inline score override modal, category browser - Startup auto-bootstrap for impact categories - Route /impact + sidebar nav entry Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
OpenSquared
@@ -766,6 +766,42 @@ def init_db():
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except Exception:
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pass
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# ── Impact categories (event_calendar + geopolitical default impacts) ────────
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c.execute("""CREATE TABLE IF NOT EXISTS event_categories (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT UNIQUE NOT NULL,
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type TEXT NOT NULL,
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sub_type TEXT DEFAULT '',
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description TEXT DEFAULT '',
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default_impacts TEXT DEFAULT '[]',
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is_ai_generated INTEGER DEFAULT 0,
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updated_at TEXT DEFAULT (datetime('now'))
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)""")
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# ── Per-event/news instrument impact assessments ──────────────────────────
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c.execute("""CREATE TABLE IF NOT EXISTS instrument_impacts (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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source_type TEXT NOT NULL,
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source_id INTEGER,
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source_name TEXT NOT NULL,
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source_date TEXT NOT NULL,
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category_name TEXT DEFAULT '',
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instrument_id TEXT NOT NULL,
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impact_score REAL DEFAULT 0.5,
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direction TEXT DEFAULT 'neutral',
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rationale TEXT DEFAULT '',
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confidence REAL DEFAULT 0.7,
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ai_generated INTEGER DEFAULT 0,
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manually_adjusted INTEGER DEFAULT 0,
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adjusted_score REAL,
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adjusted_direction TEXT,
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override_rationale TEXT DEFAULT '',
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created_at TEXT DEFAULT (datetime('now'))
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)""")
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c.execute("CREATE INDEX IF NOT EXISTS idx_ii_source ON instrument_impacts(source_type, source_id)")
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c.execute("CREATE INDEX IF NOT EXISTS idx_ii_date ON instrument_impacts(source_date)")
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c.execute("CREATE INDEX IF NOT EXISTS idx_ii_instrument ON instrument_impacts(instrument_id)")
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c.execute("""CREATE TABLE IF NOT EXISTS timeline_context (
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ref_date TEXT PRIMARY KEY,
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long_event_id INTEGER REFERENCES market_events(id),
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@@ -4610,3 +4646,143 @@ def count_market_events() -> int:
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return conn.execute("SELECT COUNT(*) FROM market_events").fetchone()[0]
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finally:
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conn.close()
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# ── Event categories ──────────────────────────────────────────────────────────
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def get_all_event_categories() -> List[Dict[str, Any]]:
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conn = get_conn()
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try:
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rows = conn.execute("SELECT * FROM event_categories ORDER BY type, name").fetchall()
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return [dict(r) for r in rows]
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finally:
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conn.close()
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def get_event_category(name: str) -> Optional[Dict[str, Any]]:
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conn = get_conn()
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try:
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row = conn.execute("SELECT * FROM event_categories WHERE name=?", (name,)).fetchone()
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return dict(row) if row else None
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finally:
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conn.close()
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def upsert_event_category(cat: Dict[str, Any]) -> int:
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import json as _json
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conn = get_conn()
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try:
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defaults = cat.get("default_impacts", [])
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if isinstance(defaults, list):
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defaults = _json.dumps(defaults)
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row = conn.execute("SELECT id FROM event_categories WHERE name=?", (cat["name"],)).fetchone()
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if row:
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conn.execute("""UPDATE event_categories SET
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type=?, sub_type=?, description=?, default_impacts=?,
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is_ai_generated=?, updated_at=datetime('now')
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WHERE name=?""",
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(cat["type"], cat.get("sub_type",""), cat.get("description",""),
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defaults, int(cat.get("is_ai_generated", 0)), cat["name"]))
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conn.commit()
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return row[0]
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else:
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cur = conn.execute("""INSERT INTO event_categories
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(name, type, sub_type, description, default_impacts, is_ai_generated)
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VALUES (?,?,?,?,?,?)""",
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(cat["name"], cat["type"], cat.get("sub_type",""),
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cat.get("description",""), defaults, int(cat.get("is_ai_generated",0))))
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conn.commit()
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return cur.lastrowid
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finally:
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conn.close()
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# ── Instrument impacts ────────────────────────────────────────────────────────
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def save_instrument_impacts(impacts: List[Dict[str, Any]]) -> int:
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import json as _json
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if not impacts:
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return 0
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conn = get_conn()
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try:
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inserted = 0
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for imp in impacts:
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conn.execute("""INSERT INTO instrument_impacts
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(source_type, source_id, source_name, source_date, category_name,
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instrument_id, impact_score, direction, rationale, confidence, ai_generated)
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VALUES (?,?,?,?,?,?,?,?,?,?,?)""",
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(imp["source_type"], imp.get("source_id"), imp["source_name"],
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imp["source_date"], imp.get("category_name",""),
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imp["instrument_id"], imp.get("impact_score", 0.5),
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imp.get("direction","neutral"), imp.get("rationale",""),
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imp.get("confidence", 0.7), int(imp.get("ai_generated", 0))))
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inserted += 1
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conn.commit()
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return inserted
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finally:
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conn.close()
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def get_impacts_for_source(source_type: str, source_id: int) -> List[Dict[str, Any]]:
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conn = get_conn()
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try:
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rows = conn.execute(
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"SELECT * FROM instrument_impacts WHERE source_type=? AND source_id=? ORDER BY impact_score DESC",
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(source_type, source_id)).fetchall()
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return [dict(r) for r in rows]
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finally:
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conn.close()
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def delete_impacts_for_source(source_type: str, source_id: int) -> int:
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conn = get_conn()
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try:
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cur = conn.execute(
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"DELETE FROM instrument_impacts WHERE source_type=? AND source_id=?",
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(source_type, source_id))
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conn.commit()
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return cur.rowcount
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finally:
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conn.close()
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def update_impact_adjustment(impact_id: int, adjusted_score: float,
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adjusted_direction: str, override_rationale: str = "") -> bool:
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conn = get_conn()
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try:
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cur = conn.execute("""UPDATE instrument_impacts SET
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manually_adjusted=1, adjusted_score=?, adjusted_direction=?, override_rationale=?
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WHERE id=?""",
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(adjusted_score, adjusted_direction, override_rationale, impact_id))
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conn.commit()
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return cur.rowcount > 0
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finally:
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conn.close()
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def get_weekly_impact_sources(days: int = 7, min_score: float = 0.3) -> List[Dict[str, Any]]:
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"""Return distinct sources (events/news) with their max impact score from last N days."""
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conn = get_conn()
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try:
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cutoff = (
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__import__('datetime').datetime.utcnow() -
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__import__('datetime').timedelta(days=days)
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).strftime("%Y-%m-%d")
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rows = conn.execute("""
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SELECT source_type, source_id, source_name, source_date, category_name,
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MAX(COALESCE(adjusted_score, impact_score)) as max_score,
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COUNT(*) as n_instruments,
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MAX(created_at) as evaluated_at
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FROM instrument_impacts
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WHERE source_date >= ? AND COALESCE(adjusted_score, impact_score) >= ?
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GROUP BY source_type, source_id
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ORDER BY source_date DESC, max_score DESC
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""", (cutoff, min_score)).fetchall()
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result = []
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for r in rows:
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d = dict(r)
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d["impacts"] = get_impacts_for_source(d["source_type"], d.get("source_id"))
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result.append(d)
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return result
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finally:
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conn.close()
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