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feat: macro gauge DB + regime triggers + date-aware instrument snapshot

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DB:
- New table macro_gauge_snapshots (daily snapshot of all 28+ gauges + dominant + scores)
- save_macro_gauge_snapshot / get_macro_gauge_snapshot_at / get_macro_gauge_history
- Auto-save once per calendar day on every macro-regime fetch (not just force=True)

API:
- GET /api/market/macro-gauges/at?date=YYYY-MM-DD — nearest snapshot ≤ date
- GET /api/market/macro-gauges/history?days=N

Detector (_check_macro_gauges in Eco Desk):
- Regime transition events (goldilocks→stagflation etc.) with severity scoring
- Yield curve inversion / désinversion (slope_10y3m sign change)
- DXY shock (% change over lookback window)
- Credit stress (HYG drop threshold)
- Gold/Copper ratio regime crossings

InstrumentDashboard:
- macroAtDate state: fetches /api/market/macro-gauges/at when crosshair date ≠ last date
- RegimeCard uses historical macro regime when on a past date
- MacroGaugePanel: full breakdown of all gauges by bloc (liquidité, crédit, énergie...)
  visible only when on a historical date — shows value + change_pct + regime scores bar

AIDesks: added fundamental + sentiment to AIDesk type

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
OpenSquared
2026-06-26 09:34:35 +02:00
parent a12d7a1ef3
commit 85eb864584
5 changed files with 513 additions and 5 deletions
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238
backend/services/market_event_detector.py
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@@ -1058,6 +1058,232 @@ def _check_reports(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]:
return created
# ── Source 7: Macro gauge transitions (Eco Desk extension) ───────────────────
# Scenarios ordered by risk level for determining transition severity
_REGIME_SEVERITY = {
"goldilocks": 1, "desinflation": 2, "soft_landing": 2,
"reflation": 3, "stagflation": 4, "inflation_shock": 5,
"recession": 5, "crise_liquidite": 6, "incertain": 0,
}
_REGIME_LABELS = {
"goldilocks": "Goldilocks", "desinflation": "Désinflation",
"soft_landing": "Soft Landing", "reflation": "Reflation",
"stagflation": "Stagflation", "inflation_shock": "Choc Inflationniste",
"recession": "Récession", "crise_liquidite": "Crise de liquidité",
"incertain": "Incertain",
}
def _check_macro_gauges(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Detect macro regime transitions and key gauge threshold crossings.
Uses macro_gauge_snapshots table (daily persistence) as source.
Falls back to macro_regime_history for regime transitions if no snapshots yet.
"""
from services.database import get_macro_gauge_history, get_macro_regime_history
gauge_signals = desk_cfg.get("gauge_signals", {})
def sig_on(k: str) -> Optional[Dict]:
c = gauge_signals.get(k, {})
return c if c.get("enabled", True) else None
regime_cfg = sig_on("regime_transition")
curve_cfg = sig_on("yield_curve_inversion")
dxy_cfg = sig_on("dxy_shock")
credit_cfg = sig_on("credit_stress")
gcr_cfg = sig_on("gold_copper_ratio")
existing = _existing_event_keys()
created: List[Dict] = []
def _emit(name: str, date_str: str, category: str, sub_type: str,
score: float, level: str, desc: str, assets: List[str]):
if _is_dup(name, existing):
return
ev = {
"name": name,
"start_date": date_str,
"level": level,
"category": category,
"sub_type": sub_type,
"description": desc,
"market_impact": "",
"affected_assets": assets,
"impact_score": score,
"source_refs": [{
"title": f"Macro signal: {name}",
"source": "MacroRegime/DB",
"url": "",
"date": date_str,
"original_score": score,
}],
"origin": "detector_macro_gauge",
}
result = _save_and_evaluate(ev, existing)
if result:
result["source"] = "eco"
created.append(result)
# ── Regime transition ─────────────────────────────────────────────────────
if regime_cfg:
history = get_macro_gauge_history(days=14)
if len(history) >= 2:
latest = history[0]
prev = history[1]
dom_new = latest.get("dominant") or "incertain"
dom_old = prev.get("dominant") or "incertain"
if dom_new != dom_old and dom_new != "incertain":
date_str = latest["snapshot_date"]
sev_old = _REGIME_SEVERITY.get(dom_old, 0)
sev_new = _REGIME_SEVERITY.get(dom_new, 0)
direction = "bearish" if sev_new > sev_old else "bullish"
score = min(0.90, 0.55 + abs(sev_new - sev_old) * 0.07)
level = "long" if abs(sev_new - sev_old) >= 3 else "medium"
lbl_old = _REGIME_LABELS.get(dom_old, dom_old)
lbl_new = _REGIME_LABELS.get(dom_new, dom_new)
name = f"Transition Régime Macro: {lbl_old} → {lbl_new} ({date_str[:7]})"
desc = (
f"Le régime macro dominant passe de {lbl_old} à {lbl_new}. "
f"Sévérité: {sev_old}→{sev_new}/6. "
f"Révision des biais d'actifs recommandée."
)
scores = latest.get("regime_scores", {})
top3 = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:3]
if top3:
desc += " Top 3 scénarios: " + ", ".join(
f"{_REGIME_LABELS.get(k,k)} ({v:.0%})" for k, v in top3
)
_emit(name, date_str, "event_calendar", "RegimeTransition",
score, level, desc,
["SPY","TLT","GLD","VXX","HYG","EURUSD=X"])
# Fallback: use macro_regime_history if no snapshots yet
elif not history:
hist = get_macro_regime_history(days=14)
if len(hist) >= 2:
latest = hist[0]
prev = hist[1]
dom_new = latest.get("dominant") or "incertain"
dom_old = prev.get("dominant") or "incertain"
if dom_new != dom_old and dom_new != "incertain":
date_str = latest["timestamp"][:10]
sev_old = _REGIME_SEVERITY.get(dom_old, 0)
sev_new = _REGIME_SEVERITY.get(dom_new, 0)
score = min(0.90, 0.55 + abs(sev_new - sev_old) * 0.07)
level = "long" if abs(sev_new - sev_old) >= 3 else "medium"
lbl_old = _REGIME_LABELS.get(dom_old, dom_old)
lbl_new = _REGIME_LABELS.get(dom_new, dom_new)
name = f"Transition Régime Macro: {lbl_old} → {lbl_new} ({date_str[:7]})"
_emit(name, date_str, "event_calendar", "RegimeTransition",
score, level,
f"Transition macro: {lbl_old} → {lbl_new}. Source: macro_regime_history.",
["SPY","TLT","GLD","VXX","HYG"])
# ── Gauge threshold crossings (from saved snapshots) ──────────────────────
history = get_macro_gauge_history(days=desk_cfg.get("lookback_days", 7) + 2)
if len(history) < 2:
return created
latest_snap = history[0]
oldest_snap = history[-1]
latest_gauges = latest_snap.get("gauges", {})
oldest_gauges = oldest_snap.get("gauges", {})
latest_date = latest_snap["snapshot_date"]
def gauge_val(snap_gauges: Dict, gid: str) -> Optional[float]:
g = snap_gauges.get(gid, {})
v = g.get("value")
return float(v) if v is not None else None
# ── Yield curve inversion ─────────────────────────────────────────────────
if curve_cfg:
threshold = float(curve_cfg.get("threshold", 0.0))
# slope_10y3m is a derived gauge: positive = normal, negative = inverted
slope_now = gauge_val(latest_gauges, "slope_10y3m")
slope_old = gauge_val(oldest_gauges, "slope_10y3m")
if slope_now is not None and slope_old is not None:
if slope_old > threshold >= slope_now:
name = f"Inversion Courbe 10Y-3M ({latest_date[:7]})"
_emit(name, latest_date, "event_calendar", "YieldCurveInversion",
0.85, "long",
f"La courbe des taux US (10Y-3M) s'inverse à {slope_now:+.2f} pts "
f"(précédent: {slope_old:+.2f} pts). "
f"Signal historique de récession dans 12-18 mois.",
["TLT","SPY","HYG","GLD","EURUSD=X"])
elif slope_old <= threshold < slope_now:
name = f"Désincurve 10Y-3M — Reflation ({latest_date[:7]})"
_emit(name, latest_date, "event_calendar", "YieldCurveDesinversion",
0.70, "medium",
f"La courbe 10Y-3M revient positive à {slope_now:+.2f} pts. "
f"Signal de détente des craintes de récession.",
["SPY","XLF","IWM","TLT"])
# ── DXY shock ────────────────────────────────────────────────────────────
if dxy_cfg:
pct_thr = float(dxy_cfg.get("pct_threshold", 2.0))
dxy_now = gauge_val(latest_gauges, "dxy")
dxy_old = gauge_val(oldest_gauges, "dxy")
if dxy_now and dxy_old and dxy_old > 0:
pct_chg = (dxy_now - dxy_old) / dxy_old * 100
if abs(pct_chg) >= pct_thr:
sign = "+" if pct_chg > 0 else ""
direct = "bullish" if pct_chg > 0 else "bearish"
name = f"DXY choc {sign}{pct_chg:.1f}% ({latest_date[:7]})"
_emit(name, latest_date, "event_calendar", "DXYShock",
min(0.80, 0.45 + abs(pct_chg) * 0.07), "medium",
f"Dollar DXY {sign}{pct_chg:.1f}% sur la période "
f"({dxy_old:.1f} → {dxy_now:.1f}). "
f"{'Appréciation USD: pression sur EM et matières premières.' if pct_chg > 0 else 'Dépréciation USD: favorable aux matières premières et EM.'}",
["GLD","EEM","USO","EURUSD=X","USDJPY=X"])
# ── Credit stress (HYG) ───────────────────────────────────────────────────
if credit_cfg:
pct_thr = float(credit_cfg.get("pct_threshold", -1.5))
hyg_chg = gauge_val(latest_gauges, "hyg")
if hyg_chg is None:
# Fallback: compute from values
hyg_now = gauge_val(latest_gauges, "hyg")
hyg_old = gauge_val(oldest_gauges, "hyg")
if hyg_now and hyg_old and hyg_old > 0:
hyg_chg = (hyg_now - hyg_old) / hyg_old * 100
else:
hyg_chg = float(latest_gauges.get("hyg", {}).get("change_pct") or 0)
if hyg_chg is not None and hyg_chg <= pct_thr:
name = f"Stress Crédit HYG ({latest_date[:7]})"
_emit(name, latest_date, "event_calendar", "CreditStress",
min(0.80, 0.45 + abs(hyg_chg) * 0.1), "medium",
f"HYG (High Yield) chute de {hyg_chg:.1f}% — signal de stress crédit. "
f"Spreads HY en élargissement. Surveiller LQD et TLT pour contagion.",
["HYG","LQD","SPY","TLT","VXX"])
# ── Gold/Copper ratio regime ──────────────────────────────────────────────
if gcr_cfg:
fear_thr = float(gcr_cfg.get("fear_threshold", 700))
growth_thr = float(gcr_cfg.get("growth_threshold", 500))
gcr_now = gauge_val(latest_gauges, "gold_copper_ratio")
gcr_old = gauge_val(oldest_gauges, "gold_copper_ratio")
if gcr_now and gcr_old:
if gcr_old < fear_thr <= gcr_now:
name = f"Ratio Or/Cuivre zone peur > {fear_thr:.0f} ({latest_date[:7]})"
_emit(name, latest_date, "event_calendar", "GoldCopperRatio",
0.70, "medium",
f"Ratio Or/Cuivre franchit {fear_thr:.0f} ({gcr_old:.0f} → {gcr_now:.0f}). "
f"L'or surperforme le cuivre — signal de risk-off, craintes de récession.",
["GLD","SPY","EEM","USO"])
elif gcr_old > growth_thr >= gcr_now:
name = f"Ratio Or/Cuivre zone croissance < {growth_thr:.0f} ({latest_date[:7]})"
_emit(name, latest_date, "event_calendar", "GoldCopperRatio",
0.60, "medium",
f"Ratio Or/Cuivre sous {growth_thr:.0f} ({gcr_old:.0f} → {gcr_now:.0f}). "
f"Le cuivre surperforme l'or — signal de risk-on, expansion économique.",
["EEM","XLI","SPY","GLD"])
return created
# ── Main entry point ──────────────────────────────────────────────────────────
def check_new_market_events(
@@ -1109,7 +1335,15 @@ def check_new_market_events(
"dedup_enabled": True, "dedup_lookback_days": 3,
})
eco_cfg = _desk_cfg(eco_desk, {
"z_threshold": eco_z_threshold, "days": eco_days,
"z_threshold": eco_z_threshold,
"days": eco_days,
"gauge_signals": {
"regime_transition": {"enabled": True},
"yield_curve_inversion": {"enabled": True, "threshold": 0.0},
"dxy_shock": {"enabled": True, "pct_threshold": 2.0},
"credit_stress": {"enabled": True, "pct_threshold": -1.5},
"gold_copper_ratio": {"enabled": True, "fear_threshold": 700, "growth_threshold": 500},
},
})
tech_cfg = _desk_cfg(tech_desk, {
"lookback_days": technical_lookback_days,
@@ -1142,7 +1376,7 @@ def check_new_market_events(
runners = [
("news", lambda: _check_news(news_cfg)),
("fundamental", lambda: _check_fundamental(fundamental_cfg)),
("eco", lambda: _check_eco(eco_cfg)),
("eco", lambda: _check_eco(eco_cfg) + _check_macro_gauges(eco_cfg)),
("technical", lambda: _check_technical(tech_cfg)),
("reports", lambda: _check_reports(report_cfg)),
("sentiment", lambda: _check_sentiment(sentiment_cfg)),