feat: eco desk enhanced config + EUR/USD causal simulator
Eco desk (market_event_detector.py + AIDesks.tsx): - Add currencies filter (USD via FRED, EUR/GBP/JPY/etc via ff_calendar) - Add min_impact filter (high / high+medium / all levels) - Add create_market_event toggle — detect surprises without creating events - Add lookback_releases — inject last N historical releases into event description - New _check_ff_calendar_surprises() for non-USD surprising releases - Frontend EcoConfig: currency chips, impact dropdown, releases input, toggle EUR/USD Simulator (EuroSimulator.tsx): - Pure frontend causal model — no API calls, no historical data - 3-column layout: controls | causal chain SVG | results - FED/BCE rate sliders + hawkish/dovish tone selector - CPI/NFP/PMI surprise inputs - SVG causal chain: CPI→CB→2Y→ΔRate→EURUSD with dynamic colors - Real-time pip decomposition by factor, sensitivity bars - Route /simulator + sidebar entry Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
OpenSquared
@@ -31,6 +31,37 @@ SUBTYPE_FROM_SERIES = {
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"BAMLH0A0HYM2": "Credit", "BAMLC0A0CM": "Credit",
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}
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# Impact classification for FRED series
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_SERIES_IMPACT = {
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"UNRATE": "high", "PAYEMS": "high",
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"CPIAUCSL": "high", "CPILFESL": "high",
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"A191RL1Q225SBEA": "high", "GDP": "high",
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"FEDFUNDS": "high", "DFF": "high",
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"PCEPILFE": "high", "PCEPI": "high",
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"BAMLH0A0HYM2": "medium", "BAMLC0A0CM": "medium",
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}
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_IMPACT_RANKS = {"high": 3, "medium": 2, "low": 1}
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def _parse_numeric(s: Optional[str]) -> Optional[float]:
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"""Parse numeric string with optional K/M/B/% suffix → float or None."""
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if not s:
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return None
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s = s.strip()
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mult = 1.0
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if s.endswith(("B", "b")):
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mult, s = 1e9, s[:-1]
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elif s.endswith(("M", "m")):
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mult, s = 1e6, s[:-1]
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elif s.endswith(("K", "k")):
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mult, s = 1e3, s[:-1]
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s = s.rstrip("%").replace(",", "").strip()
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try:
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return float(s) * mult
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except ValueError:
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return None
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# ── Helpers ───────────────────────────────────────────────────────────────────
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@@ -293,52 +324,105 @@ FORMAT JSON STRICT:
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return created
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# ── Source 2: Eco calendar — FRED surprises ───────────────────────────────────
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# ── Source 2: Eco calendar — FRED surprises + ff_calendar ────────────────────
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def _check_eco(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]:
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from services.database import get_recent_economic_surprises
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def _check_ff_calendar_surprises(
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currencies: List[str],
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min_impact: str,
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days: int,
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min_surprise_pct: float,
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lookback_releases: int,
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create_evt: bool,
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existing: set,
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) -> List[Dict]:
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"""
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Detect surprising releases in ff_calendar for the given currencies.
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Used when currencies other than USD are configured (FRED only covers USD).
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"""
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from services.database import get_conn
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z_threshold = float(desk_cfg.get("z_threshold", 1.5))
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days = int(desk_cfg.get("days", 7))
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impact_map = {
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"high": ("high",),
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"medium": ("high", "medium"),
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"low": ("high", "medium", "low"),
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}
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allowed_impacts = impact_map.get(min_impact, ("high", "medium"))
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cutoff = (datetime.utcnow() - timedelta(days=days)).strftime("%Y-%m-%d")
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try:
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releases = get_recent_economic_surprises(days=days, min_zscore=z_threshold)
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conn = get_conn()
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ccy_ph = ",".join("?" * len(currencies))
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imp_ph = ",".join("?" * len(allowed_impacts))
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rows = conn.execute(
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f"""SELECT event_date, currency, impact, event_name,
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actual_value, forecast_value, previous_value
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FROM ff_calendar
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WHERE currency IN ({ccy_ph})
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AND impact IN ({imp_ph})
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AND event_date >= ?
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AND actual_value IS NOT NULL
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AND forecast_value IS NOT NULL
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ORDER BY event_date DESC
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LIMIT 200""",
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(*currencies, *allowed_impacts, cutoff),
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).fetchall()
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conn.close()
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except Exception as e:
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logger.warning(f"[check_events/eco] query failed: {e}")
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logger.warning(f"[check_events/eco/ff] query failed: {e}")
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return []
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existing = _existing_event_keys()
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created: List[Dict] = []
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created = []
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for row in rows:
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d = dict(row)
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actual = _parse_numeric(d.get("actual_value"))
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forecast = _parse_numeric(d.get("forecast_value"))
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if actual is None or forecast is None or abs(forecast) < 1e-9:
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continue
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s_pct = (actual - forecast) / abs(forecast) * 100
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if abs(s_pct) < min_surprise_pct:
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continue
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for rel in releases:
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z = abs(rel.get("surprise_zscore") or 0)
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s_pct = rel.get("surprise_pct") or 0
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ev_date = (rel.get("event_date") or "")[:10]
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s_id = rel.get("series_id", "")
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ev_name_base = rel.get("event_name", s_id)
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direction = rel.get("surprise_direction", "neutral")
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sign = "+" if s_pct >= 0 else ""
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ev_name = f"{ev_name_base} — Surprise {sign}{s_pct:.1f}% ({ev_date[:7]})"
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ev_date = (d.get("event_date") or "")[:10]
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ev_name_base = d.get("event_name", "Unknown")
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ccy = d.get("currency", "")
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sign = "+" if s_pct >= 0 else ""
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ev_name = f"{ccy} {ev_name_base} — Surprise {sign}{s_pct:.1f}% ({ev_date[:7]})"
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if _is_dup(ev_name, existing):
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continue
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sub_type = SUBTYPE_FROM_SERIES.get(s_id, s_id[:10]) if s_id else ev_name_base[:10]
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level = "long" if z >= 3 else ("medium" if z >= 2 else "short")
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assets = rel.get("assets_impacted") or []
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if isinstance(assets, str):
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# Historical context
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context_str = ""
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if lookback_releases > 0:
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try:
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assets = json.loads(assets)
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conn2 = get_conn()
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hist = conn2.execute(
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"""SELECT event_date, actual_value, forecast_value
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FROM ff_calendar
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WHERE event_name = ? AND currency = ? AND event_date < ?
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AND actual_value IS NOT NULL
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ORDER BY event_date DESC LIMIT ?""",
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(ev_name_base, ccy, ev_date, lookback_releases),
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).fetchall()
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conn2.close()
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if hist:
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context_str = " Historique récent: " + ", ".join(
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f"{r[0][:7]}: réel={r[1]} consensus={r[2]}" for r in hist
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)
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except Exception:
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assets = []
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pass
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impact = d.get("impact", "low")
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level = "medium" if impact == "high" else "short"
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direction = "hausse" if s_pct > 0 else "baisse"
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score = min(0.85, 0.30 + abs(s_pct) / 100)
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source_ref = {
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"title": f"FRED release: {ev_name_base} ({ev_date})",
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"source": "FRED",
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"url": f"https://fred.stlouisfed.org/series/{s_id}" if s_id else "",
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"title": f"Release: {ccy} {ev_name_base} ({ev_date})",
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"source": "ff_calendar",
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"url": "",
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"date": ev_date,
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"original_score": round(min(0.95, 0.35 + z * 0.15), 3),
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"original_score": round(score, 3),
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}
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ev = {
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@@ -346,26 +430,154 @@ def _check_eco(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]:
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"start_date": ev_date,
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"level": level,
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"category": "event_calendar",
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"sub_type": sub_type,
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"sub_type": ccy,
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"description": (
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f"Surprise {direction} {sign}{s_pct:.1f}% vs baseline "
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f"(z-score: {z:.1f}σ). "
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f"Réel: {rel.get('actual_value', '?')} {rel.get('actual_unit', '')} "
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f"/ Prévision: {rel.get('forecast_value', '?')}."
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f"Surprise en {direction} de {sign}{s_pct:.1f}% vs consensus. "
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f"Réel: {d['actual_value']} / Consensus: {d['forecast_value']}."
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+ context_str
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),
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"market_impact": "",
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"affected_assets": assets,
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"impact_score": min(0.95, 0.35 + z * 0.15),
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"actual_value": str(rel.get("actual_value", "")),
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"expected_value": str(rel.get("forecast_value", "")),
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"affected_assets": [],
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"impact_score": score,
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"actual_value": str(d["actual_value"]),
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"expected_value": str(d["forecast_value"]),
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"surprise_pct": float(s_pct),
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"source_refs": [source_ref],
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"origin": "detector_eco",
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"origin": "detector_eco_ff",
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}
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result = _save_and_evaluate(ev, existing)
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if result:
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result["source"] = "eco"
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created.append(result)
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if create_evt:
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result = _save_and_evaluate(ev, existing)
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if result:
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result["source"] = "eco"
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created.append(result)
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else:
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logger.info(f"[check_events/eco] create_market_event=False — skipping: {ev_name}")
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return created
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def _check_eco(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]:
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from services.database import get_recent_economic_surprises, get_conn
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z_threshold = float(desk_cfg.get("z_threshold", 1.5))
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days = int(desk_cfg.get("days", 7))
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currencies = list(desk_cfg.get("currencies") or ["USD", "EUR", "GBP", "JPY"])
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min_impact = str(desk_cfg.get("min_impact", "medium")).lower()
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create_evt = bool(desk_cfg.get("create_market_event", True))
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lookback_releases = int(desk_cfg.get("lookback_releases", 3))
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min_rank = _IMPACT_RANKS.get(min_impact, 2)
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# ff_calendar surprise threshold: z_threshold used as rough proxy (×10 → % equivalent)
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ff_surprise_min = max(10.0, z_threshold * 10)
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existing = _existing_event_keys()
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created: List[Dict] = []
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# ── FRED path (USD only) ──────────────────────────────────────────────────
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if "USD" in currencies:
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try:
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releases = get_recent_economic_surprises(days=days, min_zscore=z_threshold)
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except Exception as e:
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logger.warning(f"[check_events/eco] FRED query failed: {e}")
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releases = []
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for rel in releases:
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s_id = rel.get("series_id", "")
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impact = _SERIES_IMPACT.get(s_id, "low")
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if _IMPACT_RANKS.get(impact, 1) < min_rank:
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continue
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z = abs(rel.get("surprise_zscore") or 0)
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s_pct = rel.get("surprise_pct") or 0
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ev_date = (rel.get("event_date") or "")[:10]
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ev_name_base = rel.get("event_name", s_id)
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direction = rel.get("surprise_direction", "neutral")
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sign = "+" if s_pct >= 0 else ""
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ev_name = f"{ev_name_base} — Surprise {sign}{s_pct:.1f}% ({ev_date[:7]})"
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if _is_dup(ev_name, existing):
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continue
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# Lookback context from FRED history
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context_str = ""
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if lookback_releases > 0 and s_id:
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try:
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conn = get_conn()
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hist = conn.execute(
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"""SELECT event_date, actual_value, forecast_value
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FROM economic_events
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WHERE series_id = ? AND event_date < ?
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ORDER BY event_date DESC LIMIT ?""",
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(s_id, ev_date, lookback_releases),
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).fetchall()
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conn.close()
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if hist:
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context_str = " Historique récent: " + ", ".join(
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f"{r[0][:7]}: réel={r[1]} consensus={r[2]}" for r in hist
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)
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except Exception:
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pass
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sub_type = SUBTYPE_FROM_SERIES.get(s_id, s_id[:10]) if s_id else ev_name_base[:10]
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level = "long" if z >= 3 else ("medium" if z >= 2 else "short")
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assets = rel.get("assets_impacted") or []
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if isinstance(assets, str):
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try:
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assets = json.loads(assets)
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except Exception:
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assets = []
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source_ref = {
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"title": f"FRED release: {ev_name_base} ({ev_date})",
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"source": "FRED",
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"url": f"https://fred.stlouisfed.org/series/{s_id}" if s_id else "",
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"date": ev_date,
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"original_score": round(min(0.95, 0.35 + z * 0.15), 3),
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}
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ev = {
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"name": ev_name,
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"start_date": ev_date,
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"level": level,
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"category": "event_calendar",
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"sub_type": sub_type,
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"description": (
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f"Surprise {direction} {sign}{s_pct:.1f}% vs baseline "
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f"(z-score: {z:.1f}σ). "
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f"Réel: {rel.get('actual_value', '?')} {rel.get('actual_unit', '')} "
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f"/ Prévision: {rel.get('forecast_value', '?')}."
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+ context_str
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),
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"market_impact": "",
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"affected_assets": assets,
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"impact_score": min(0.95, 0.35 + z * 0.15),
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"actual_value": str(rel.get("actual_value", "")),
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"expected_value": str(rel.get("forecast_value", "")),
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"surprise_pct": float(s_pct),
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"source_refs": [source_ref],
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"origin": "detector_eco",
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}
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if create_evt:
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result = _save_and_evaluate(ev, existing)
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if result:
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result["source"] = "eco"
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created.append(result)
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else:
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logger.info(f"[check_events/eco] create_market_event=False — skipping: {ev_name}")
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# ── ff_calendar path (non-USD currencies) ────────────────────────────────
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non_usd = [c for c in currencies if c != "USD"]
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if non_usd:
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created += _check_ff_calendar_surprises(
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currencies=non_usd,
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min_impact=min_impact,
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days=days,
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min_surprise_pct=ff_surprise_min,
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lookback_releases=lookback_releases,
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create_evt=create_evt,
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existing=existing,
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)
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return created
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