feat: pressure

backend/routers/instruments.py

@@ -4,7 +4,7 @@ Exposes per-instrument snapshot (price, indicators, regime, trend, events) and A
 """
 import json
 import math
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, date as date_type
 from fastapi import APIRouter, HTTPException, Query
 from pydantic import BaseModel
 from typing import List, Dict, Any, Optional
@@ -227,3 +227,166 @@ def get_theoretical_curve(
         result.append({"date": d, **entry})
 
     return result
+
+
+# ── Libellés lisibles par catégorie ───────────────────────────────────────────
+_CAT_LABELS: Dict[str, str] = {
+    "central_bank":   "Banque Centrale",
+    "monetary_shock": "Surprise Macro",
+    "geopolitical":   "Géopolitique",
+    "commodity":      "Commodités",
+    "growth_shock":   "Croissance",
+    "trade_policy":   "Commerce / Tarifs",
+    "credit_stress":  "Stress Crédit",
+    "sentiment":      "Sentiment & Position.",
+    "technical":      "Technique",
+    "positioning":    "Flux Institutionnels",
+    "unclassified":   "Non Classifié",
+}
+
+
+@router.get("/{instrument_id}/factor-state")
+def get_factor_state(
+    instrument_id: str,
+    at_date: Optional[str] = Query(None, description="YYYY-MM-DD (défaut: aujourd'hui)"),
+) -> Dict[str, Any]:
+    """
+    Pression nette actuelle sur l'instrument : somme de toutes les contributions
+    d'events actifs pondérées par leur courbe de dissipation.
+
+    Retourne une décomposition par catégorie causale (Banque Centrale, Surprise Macro…)
+    avec détail par event, ainsi que le NET en pips et la direction.
+    """
+    from services.database import get_conn
+
+    try:
+        ref_date = date_type.fromisoformat(at_date) if at_date else datetime.utcnow().date()
+    except ValueError:
+        ref_date = datetime.utcnow().date()
+
+    # Cherche les analyses pour cet instrument dans les 180 jours précédents
+    extended_from = ref_date - timedelta(days=180)
+    inst_upper = instrument_id.upper()
+
+    conn = get_conn()
+    try:
+        rows = conn.execute("""
+            SELECT a.prediction_json,
+                   e.start_date   AS event_date,
+                   e.name         AS event_name,
+                   e.sub_type     AS event_sub_type,
+                   e.end_date     AS event_end_date,
+                   t.name         AS template_name,
+                   t.category     AS category,
+                   t.calibration_json
+            FROM   causal_event_analyses a
+            JOIN   market_events          e ON e.id = a.market_event_id
+            JOIN   causal_graph_templates t ON t.id = a.template_id
+            WHERE  a.instrument = ?
+              AND  e.start_date >= ?
+              AND  e.start_date <= ?
+            ORDER  BY e.start_date DESC
+        """, (inst_upper, str(extended_from), str(ref_date))).fetchall()
+    finally:
+        conn.close()
+
+    inst_lower = inst_upper.lower()
+    by_category: Dict[str, Dict] = {}
+    seen_events: set = set()  # évite les doublons (même event × multi-analyse)
+
+    for row in rows:
+        r = dict(row)
+        event_key = (r["event_name"], r["event_date"])
+        if event_key in seen_events:
+            continue
+        seen_events.add(event_key)
+
+        try:
+            predictions = json.loads(r["prediction_json"] or "{}")
+            calib       = json.loads(r["calibration_json"] or "{}")
+        except Exception:
+            continue
+
+        # Pips prédits pour cet instrument (cherche node_id == inst_lower ou contenant)
+        pips_full: Optional[float] = None
+        if inst_lower in predictions:
+            pips_full = float(predictions[inst_lower])
+        else:
+            for k, v in predictions.items():
+                if inst_lower in k.lower():
+                    try:
+                        pips_full = float(v)
+                        break
+                    except (TypeError, ValueError):
+                        pass
+
+        if pips_full is None or pips_full == 0:
+            continue
+
+        absorption_days = max(1, int(calib.get("absorption_days", 7)))
+        decay_type      = str(calib.get("decay_type", "exp"))
+
+        try:
+            ev_date = date_type.fromisoformat(r["event_date"][:10])
+        except ValueError:
+            continue
+
+        # Pour les guidance events : end_date = meeting date → absorption dynamique
+        ev_end = r.get("event_end_date")
+        if ev_end and r.get("event_sub_type", "").startswith("rate_guidance"):
+            try:
+                meeting = date_type.fromisoformat(ev_end[:10])
+                absorption_days = max(1, (meeting - ev_date).days)
+                decay_type = "linear"  # anticipation linéaire jusqu'à la réunion
+            except ValueError:
+                pass
+
+        days_elapsed = (ref_date - ev_date).days
+        df = _decay(days_elapsed, absorption_days, decay_type)
+        if df < 0.01:
+            continue
+
+        current_pips = round(pips_full * df, 1)
+        cat = r["category"]
+
+        if cat not in by_category:
+            by_category[cat] = {
+                "label":         _CAT_LABELS.get(cat, cat),
+                "pips":          0.0,
+                "contributions": [],
+            }
+
+        by_category[cat]["pips"]    += current_pips
+        by_category[cat]["contributions"].append({
+            "event_name":      r["event_name"],
+            "event_date":      r["event_date"][:10],
+            "template_name":   r["template_name"],
+            "pips_full":       round(pips_full, 1),
+            "days_elapsed":    days_elapsed,
+            "absorption_days": absorption_days,
+            "decay_pct":       round(df * 100),
+            "pips_current":    current_pips,
+        })
+
+    # Arrondi + tri par |pips| décroissant
+    for v in by_category.values():
+        v["pips"] = round(v["pips"], 1)
+        v["contributions"].sort(key=lambda c: abs(c["pips_current"]), reverse=True)
+
+    categories = sorted(by_category.values(), key=lambda x: abs(x["pips"]), reverse=True)
+    net_pips = round(sum(v["pips"] for v in by_category.values()), 1)
+
+    direction = "neutral"
+    if net_pips > 5:
+        direction = "bullish"
+    elif net_pips < -5:
+        direction = "bearish"
+
+    return {
+        "instrument": inst_upper,
+        "at_date":    str(ref_date),
+        "net_pips":   net_pips,
+        "direction":  direction,
+        "categories": categories,
+        "n_events":   len(seen_events),
+    }

backend/services/guidance_sync.py

@@ -96,18 +96,33 @@ def _find_existing(conn, currency: str, meeting_date: str) -> Optional[dict]:
 def _upsert_analyses(conn, event_id: int, template_id: int, instruments: list[str], signal: float):
     """
     Crée/recrée les causal_event_analyses pour que l'event apparaisse
-    dans la Frise de chaque instrument concerné.
+    dans la Frise et dans la theoretical-curve de chaque instrument.
+    Calcule prediction_json via evaluate_graph pour alimenter le factor engine.
     """
     conn.execute(
         "DELETE FROM causal_event_analyses WHERE market_event_id=?", (event_id,)
     )
-    inputs = json.dumps({"guidance_signal": signal})
+    inputs_dict = {"guidance_signal": signal}
+    inputs_json = json.dumps(inputs_dict)
+
+    # Évalue le graphe pour obtenir les pips prédits par instrument
+    node_values: dict = {}
+    try:
+        from services.causal_graphs import evaluate_graph, get_template
+        tmpl = get_template(conn, template_id)
+        if tmpl:
+            node_values = evaluate_graph(tmpl["graph_json"], inputs_dict)
+    except Exception as e:
+        logger.warning(f"[guidance_sync] evaluate_graph failed: {e}")
+
+    prediction_json = json.dumps(node_values) if node_values else None
+
     for inst in instruments:
         conn.execute("""
             INSERT INTO causal_event_analyses
-                (market_event_id, template_id, instrument, inputs_json, analyzed_at)
-            VALUES (?, ?, ?, ?, datetime('now'))
-        """, (event_id, template_id, inst, inputs))
+                (market_event_id, template_id, instrument, inputs_json, prediction_json, analyzed_at)
+            VALUES (?, ?, ?, ?, ?, datetime('now'))
+        """, (event_id, template_id, inst, inputs_json, prediction_json))
 
 
 def _build_title(currency: str, signal: float, meeting_date: str) -> str:

frontend/src/pages/InstrumentDashboard.tsx

@@ -1213,6 +1213,117 @@ const PERIODS = [
   { key: '5y',  label: '5Y'  },
 ]
 
+// ── Types factor-state ────────────────────────────────────────────────────────
+interface FactorContrib {
+  event_name: string; event_date: string; template_name: string
+  pips_full: number; days_elapsed: number; absorption_days: number
+  decay_pct: number; pips_current: number
+}
+interface FactorCategory {
+  label: string; pips: number; contributions: FactorContrib[]
+}
+interface FactorState {
+  instrument: string; at_date: string; net_pips: number
+  direction: 'bullish' | 'bearish' | 'neutral'
+  categories: FactorCategory[]; n_events: number
+}
+
+// ── PressureCockpit ───────────────────────────────────────────────────────────
+function PressureCockpit({ instrumentId, refreshKey }: { instrumentId: string; refreshKey: number }) {
+  const [state, setState]     = useState<FactorState | null>(null)
+  const [loading, setLoading] = useState(false)
+  const [expanded, setExpanded] = useState<string | null>(null)
+
+  useEffect(() => {
+    if (!instrumentId) return
+    setLoading(true)
+    api.get(`/instruments/${instrumentId}/factor-state`)
+      .then(r => setState(r.data))
+      .catch(() => setState(null))
+      .finally(() => setLoading(false))
+  }, [instrumentId, refreshKey])
+
+  if (loading) return (
+    <div className="h-16 flex items-center justify-center text-xs text-slate-600 italic">
+      Calcul pression en cours…
+    </div>
+  )
+  if (!state) return null
+
+  const { net_pips, direction, categories } = state
+  const maxAbs = Math.max(...categories.map(c => Math.abs(c.pips)), 1)
+  const netCls  = direction === 'bullish' ? 'text-emerald-400' : direction === 'bearish' ? 'text-red-400' : 'text-slate-400'
+  const netLabel = direction === 'bullish' ? '▲ HAUSSIER' : direction === 'bearish' ? '▼ BAISSIER' : '◼ NEUTRE'
+
+  return (
+    <div className="space-y-2">
+      {/* NET */}
+      <div className="flex items-center gap-3 px-1">
+        <span className="text-xs text-slate-500 uppercase tracking-wide">Pression nette</span>
+        <span className={clsx('font-mono font-bold text-base', netCls)}>
+          {net_pips >= 0 ? '+' : ''}{net_pips} pips
+        </span>
+        <span className={clsx('text-[10px] font-semibold px-1.5 py-0.5 rounded border', netCls,
+          direction === 'bullish' ? 'border-emerald-700/40 bg-emerald-900/20'
+          : direction === 'bearish' ? 'border-red-700/40 bg-red-900/20'
+          : 'border-slate-700/40 bg-slate-800/20')}>
+          {netLabel}
+        </span>
+        <span className="ml-auto text-[10px] text-slate-600">{state.n_events} event{state.n_events > 1 ? 's' : ''} actifs</span>
+      </div>
+
+      {/* Barres par catégorie */}
+      {categories.length === 0 ? (
+        <p className="text-xs text-slate-600 italic px-1">Aucun event actif avec prédiction pour cet instrument.</p>
+      ) : (
+        <div className="space-y-1.5">
+          {categories.map(cat => {
+            const barW   = Math.round(Math.abs(cat.pips) / maxAbs * 100)
+            const isPos  = cat.pips >= 0
+            const isOpen = expanded === cat.label
+            return (
+              <div key={cat.label}>
+                <button
+                  className="w-full flex items-center gap-2 text-xs hover:bg-slate-800/30 rounded px-1 py-0.5 transition-colors"
+                  onClick={() => setExpanded(isOpen ? null : cat.label)}
+                >
+                  <span className="w-32 text-left text-slate-400 shrink-0 truncate">{cat.label}</span>
+                  {/* Barre */}
+                  <div className="flex-1 h-3 bg-slate-800/60 rounded-full overflow-hidden">
+                    <div
+                      className={clsx('h-full rounded-full transition-all', isPos ? 'bg-emerald-600/70' : 'bg-red-600/70')}
+                      style={{ width: `${barW}%` }}
+                    />
+                  </div>
+                  <span className={clsx('font-mono font-semibold w-16 text-right shrink-0', isPos ? 'text-emerald-400' : 'text-red-400')}>
+                    {isPos ? '+' : ''}{cat.pips}
+                  </span>
+                  <span className="text-slate-600 text-[10px] shrink-0">{isOpen ? '▲' : '▼'}</span>
+                </button>
+
+                {/* Détail events */}
+                {isOpen && (
+                  <div className="ml-2 mt-1 space-y-1 border-l border-slate-700/40 pl-3">
+                    {cat.contributions.map((c, i) => (
+                      <div key={i} className="flex items-center gap-2 text-[10px] text-slate-500">
+                        <span className="flex-1 truncate">{c.event_name}</span>
+                        <span className="text-slate-600 shrink-0">{c.event_date} · {c.decay_pct}% actif ({c.days_elapsed}j)</span>
+                        <span className={clsx('font-mono shrink-0 w-12 text-right', c.pips_current >= 0 ? 'text-emerald-500' : 'text-red-500')}>
+                          {c.pips_current >= 0 ? '+' : ''}{c.pips_current}
+                        </span>
+                      </div>
+                    ))}
+                  </div>
+                )}
+              </div>
+            )
+          })}
+        </div>
+      )}
+    </div>
+  )
+}
+
 export default function InstrumentDashboard({ instrumentIdProp, isVisible }: { instrumentIdProp?: string; isVisible?: boolean } = {}) {
   const { id: paramId = localStorage.getItem('last_instrument') || 'EURUSD=X' } = useParams<{ id: string }>()
   const navigate = useNavigate()
@@ -1600,6 +1711,14 @@ export default function InstrumentDashboard({ instrumentIdProp, isVisible }: { i
               : null
             return (
             <div className="space-y-3">
+              {/* Pression nette actuelle */}
+              <div className="rounded-xl border border-slate-700/40 bg-dark-800/60 p-4">
+                <div className="flex items-center gap-2 mb-3">
+                  <span className="text-xs font-semibold text-slate-400 uppercase tracking-wide">Pression Nette</span>
+                </div>
+                <PressureCockpit instrumentId={instrumentId} refreshKey={chartReady} />
+              </div>
+
               {/* Frise des graphes causaux (inclut l'event) */}
               <div className="rounded-xl border border-slate-700/40 bg-dark-800/60 p-4">
                 <div className="flex items-center gap-2 mb-3">