feat: cockpit

backend/routers/saxo.py

@@ -225,3 +225,26 @@ def catalog(
 @router.get("/catalog/summary")
 def catalog_summary():
     return get_saxo_catalog_summary()
+
+
+# ── Saxo-only options analytics (Options Lab "Saxo" section) ─────────────────
+# Computed exclusively from our own accumulated saxo_option_snapshots history — never
+# blended with the yfinance-based /api/options-vol/* endpoints. Symbols come from the
+# Saxo watchlist above (the same ones already being snapshotted every ~5 min).
+
+@router.get("/iv-watchlist")
+def saxo_iv_watchlist():
+    from services.saxo_iv_engine import get_saxo_iv_watchlist
+    return get_saxo_iv_watchlist()
+
+
+@router.get("/iv-snapshot/{symbol}")
+def saxo_iv_snapshot(symbol: str):
+    from services.saxo_iv_engine import get_saxo_iv_snapshot
+    return get_saxo_iv_snapshot(symbol)
+
+
+@router.get("/iv-history/{symbol}")
+def saxo_iv_history(symbol: str, days: int = Query(90, ge=1, le=730)):
+    from services.saxo_iv_engine import get_saxo_iv_history
+    return get_saxo_iv_history(symbol, days)

backend/services/ai_analyzer.py

@@ -1747,7 +1747,7 @@ def ai_score_geo_risk(news: List[Dict], algo_score: Dict, log_meta: Optional[Dic
     if not get_client() or not news:
         return {
             "score": algo_score.get("score", 0), "level": algo_score.get("level", "low"),
-            "rationale": "IA indisponible — score algorithmique utilisé tel quel.",
+            "rationale": "AI unavailable — algorithmic score used as-is.",
             "top_risks": [],
         }
 
@@ -1757,31 +1757,31 @@ def ai_score_geo_risk(news: List[Dict], algo_score: Dict, log_meta: Optional[Dic
         for n in top_news
     ]
 
-    user = f"""Evalue le niveau de risque geopolitique global actuel pour les marches financiers, sur une echelle de 0 a 100.
+    user = f"""Assess the current overall geopolitical risk level for financial markets, on a scale of 0 to 100.
 
-Score algorithmique de reference (formule ponderee par categorie, a titre indicatif seulement — exerce ton propre jugement, ne le recopie pas mecaniquement) : {algo_score.get('score')}/100 ({algo_score.get('level')}).
-Repartition par categorie : {json.dumps(algo_score.get('breakdown', {}), ensure_ascii=False)}
+Reference algorithmic score (category-weighted formula, indicative only — use your own judgment, don't just copy it): {algo_score.get('score')}/100 ({algo_score.get('level')}).
+Breakdown by category: {json.dumps(algo_score.get('breakdown', {}), ensure_ascii=False)}
 
-Top actualites (triees par impact) :
+Top news (sorted by impact):
 {json.dumps(compact, ensure_ascii=False)}
 
-Consignes :
-- Un score eleve doit refleter un risque REEL et actuel pour les marches (escalade militaire active, rupture commerciale majeure, crise politique...), pas juste un volume de news.
-- Un evenement de desescalade/resolution doit FAIRE BAISSER le score meme si son impact brut est eleve.
-- Ne t'ancre pas mecaniquement sur le score algorithmique si le contexte reel (titres) justifie un score different.
-- rationale : 2-3 phrases en francais expliquant precisement pourquoi ce score, en citant les evenements les plus determinants.
-- top_risks : 3 a 5 items les plus determinants pour ce score (titres courts).
+Instructions:
+- A high score must reflect a REAL, current risk to markets (active military escalation, major trade rupture, political crisis...), not just a volume of news.
+- A de-escalation/resolution event should LOWER the score even if its raw impact is high.
+- Don't mechanically anchor on the algorithmic score if the real context (headlines) justifies a different one.
+- rationale: 2-3 sentences in English precisely explaining why this score, citing the most determining events.
+- top_risks: the 3 to 5 most determining items for this score (short titles).
 
 JSON: {{"score": <0-100 float>, "level": "low"|"medium"|"high"|"extreme", "rationale": "...", "top_risks": ["...", ...]}}"""
 
     result = _chat(
-        "Tu es un analyste geopolitique senior qui evalue le risque marche global, pas evenement par evenement.",
+        "You are a senior geopolitical analyst assessing overall market risk, not event by event.",
         user, model="gpt-4o", json_mode=True, max_tokens=700, log_meta=log_meta,
     )
     if not result:
         return {
             "score": algo_score.get("score", 0), "level": algo_score.get("level", "low"),
-            "rationale": "Erreur IA — score algorithmique utilisé tel quel.",
+            "rationale": "AI error — algorithmic score used as-is.",
             "top_risks": [],
         }
 

backend/services/data_fetcher.py

@@ -109,7 +109,14 @@ def get_quote(symbol: str) -> Optional[Dict[str, Any]]:
             if hist.empty:
                 continue
             price = float(hist["Close"].iloc[-1])
-            prev = float(hist["Close"].iloc[-2]) if len(hist) > 1 else price
+            # Explicit D-1 close: the latest row whose calendar date differs from the
+            # most recent row's date, not just "the row before last" — near-24h
+            # instruments (FX, futures) can otherwise return two rows for the same
+            # session, silently comparing "today vs today" and making change_pct swing
+            # around against a moving reference instead of a fixed prior close.
+            last_date = hist.index[-1].date()
+            prior_rows = hist[hist.index.date < last_date]
+            prev = float(prior_rows["Close"].iloc[-1]) if not prior_rows.empty else price
             change = price - prev
             change_pct = (change / prev * 100) if prev else 0
             return {
@@ -447,13 +454,13 @@ MACRO_GAUGE_CONFIG = [
 
 SCENARIO_META = {
     "goldilocks":      {"label": "Goldilocks",                "color": "#10b981", "emoji": "🟢"},
-    "desinflation":    {"label": "Désinflation / Baisse taux","color": "#3b82f6", "emoji": "🔵"},
+    "desinflation":    {"label": "Disinflation / Rate Cuts",  "color": "#3b82f6", "emoji": "🔵"},
     "soft_landing":    {"label": "Soft Landing",              "color": "#06b6d4", "emoji": "🔷"},
     "reflation":       {"label": "Reflation",                 "color": "#f97316", "emoji": "🟠"},
     "stagflation":     {"label": "Stagflation",               "color": "#f59e0b", "emoji": "🟡"},
-    "inflation_shock": {"label": "Choc Inflationniste",       "color": "#dc2626", "emoji": "🔥"},
-    "recession":       {"label": "Récession",                 "color": "#ef4444", "emoji": "🔴"},
-    "crise_liquidite": {"label": "Crise de liquidité",        "color": "#7c3aed", "emoji": "🟣"},
+    "inflation_shock": {"label": "Inflation Shock",           "color": "#dc2626", "emoji": "🔥"},
+    "recession":       {"label": "Recession",                 "color": "#ef4444", "emoji": "🔴"},
+    "crise_liquidite": {"label": "Liquidity Crisis",          "color": "#7c3aed", "emoji": "🟣"},
 }
 
 SCENARIO_ASSET_BIAS = {
@@ -807,165 +814,165 @@ def _score_raw(gauges: Dict[str, Any]) -> tuple:
     elif vix < 18: s += 20; r.append("VIX<18")
     elif vix < 22: s += 10
     if slope is not None:
-        if slope > 1.0:   s += 20; r.append("Courbe +1%pt")
-        elif slope > 0.3: s += 10; r.append("Courbe légèrement positive")
+        if slope > 1.0:   s += 20; r.append("Curve +1%pt")
+        elif slope > 0.3: s += 10; r.append("Curve slightly positive")
     if gcr is not None:
-        if gcr < 500:   s += 20; r.append(f"Or/Cu {gcr} (croissance)")
+        if gcr < 500:   s += 20; r.append(f"Gold/Cu {gcr} (growth)")
         elif gcr < 600: s += 10
-    if hyg_c > 0.2:  s += 15; r.append("HYG↑ (crédit OK)")
+    if hyg_c > 0.2:  s += 15; r.append("HYG↑ (credit OK)")
     elif hyg_c > 0:  s += 5
     if vs200 is not None:
-        if vs200 > 5:   s += 15; r.append(f"S&P+{vs200}% vs 200j")
+        if vs200 > 5:   s += 15; r.append(f"S&P+{vs200}% vs 200d")
         elif vs200 > 0: s += 7
-    if copper_c > 0.5: s += 10; r.append("Cuivre↑")
+    if copper_c > 0.5: s += 10; r.append("Copper↑")
     if skew_v < 115:          s += 6; r.append(f"SKEW {skew_v:.0f} (no tail hedge)")
     if vvix_v < 85:           s += 5; r.append(f"VVIX {vvix_v:.0f} (vol stable)")
-    if tech_vs_staples > 0.5: s += 7; r.append("Tech > Défensifs (risk-on sectoriel)")
-    if eem_c > 0.3:           s += 6; r.append("EM↑ (croissance globale)")
-    if usdjpy_c > 0.2:        s += 4; r.append("JPY↓ (carry actif = risk-on)")
+    if tech_vs_staples > 0.5: s += 7; r.append("Tech > Defensives (sector risk-on)")
+    if eem_c > 0.3:           s += 6; r.append("EM↑ (global growth)")
+    if usdjpy_c > 0.2:        s += 4; r.append("JPY↓ (active carry = risk-on)")
     scores["goldilocks"] = min(100, s); reasons["goldilocks"] = r
 
-    # DÉSINFLATION / BAISSE DE TAUX
+    # DISINFLATION / RATE CUTS
     s = 0; r = []
-    if brent_c < -1.0:  s += 25; r.append("Brent↓↓ (désinflationniste)")
+    if brent_c < -1.0:  s += 25; r.append("Brent↓↓ (disinflationary)")
     elif brent_c < 0:   s += 10
-    if ng_c < -1.0:     s += 10; r.append("Gaz↓")
-    if ief_c > 0.2:     s += 20; r.append("IEF↑ (taux longs baissent)")
+    if ng_c < -1.0:     s += 10; r.append("Gas↓")
+    if ief_c > 0.2:     s += 20; r.append("IEF↑ (long rates falling)")
     elif ief_c > 0:     s += 10
     if vix < 20:        s += 15; r.append("VIX<20")
-    if vs200 is not None and vs200 > 0: s += 20; r.append("S&P au-dessus 200j")
+    if vs200 is not None and vs200 > 0: s += 20; r.append("S&P above 200d")
     if hyg_c > 0:       s += 10; r.append("HYG↑")
-    if gold_c > 0 and brent_c < 0: s += 10; r.append("Or↑+Brent↓ (taux réels ↓)")
-    if tlt_c > 0.5:     s += 12; r.append("TLT↑↑ (désinflation confirmée)")
-    elif tlt_c > 0.2:   s += 6;  r.append("TLT↑ (bonds longs soutiennent)")
-    if xlf_c > 0:       s += 5;  r.append("XLF↑ (anticipent baisse taux)")
+    if gold_c > 0 and brent_c < 0: s += 10; r.append("Gold↑+Brent↓ (real rates ↓)")
+    if tlt_c > 0.5:     s += 12; r.append("TLT↑↑ (disinflation confirmed)")
+    elif tlt_c > 0.2:   s += 6;  r.append("TLT↑ (long bonds supportive)")
+    if xlf_c > 0:       s += 5;  r.append("XLF↑ (pricing in rate cuts)")
     scores["desinflation"] = min(100, s); reasons["desinflation"] = r
 
     # STAGFLATION
     s = 0; r = []
     if brent_c > 2.0:   s += 30; r.append("Brent↑↑")
     elif brent_c > 0.5: s += 15; r.append("Brent↑")
-    if ng_c > 2.0:      s += 15; r.append("Gaz↑↑")
+    if ng_c > 2.0:      s += 15; r.append("Gas↑↑")
     elif ng_c > 0.5:    s += 7
     if slope is not None:
-        if slope < 0:    s += 20; r.append("Courbe inversée")
-        elif slope < 0.3: s += 10; r.append("Courbe plate")
-    if gold_c > 0.5:    s += 15; r.append("Or↑ (protection inflation)")
-    if copper_c < 0:    s += 15; r.append("Cuivre↓ (demande faible)")
-    if vix > 18:        s += 10; r.append("VIX élevé")
-    if xlp_c > xlk_c + 0.5: s += 8; r.append("Défensifs > Tech (rotation stagflationniste)")
-    if xlu_c > 0.4:          s += 6; r.append("Utilities↑ (revenus stables)")
-    if skew_v > 130:         s += 5; r.append(f"SKEW {skew_v:.0f} (tail risk croissant)")
-    if tlt_c < -0.3:         s += 5; r.append("TLT↓ (inflation persistante)")
+        if slope < 0:    s += 20; r.append("Curve inverted")
+        elif slope < 0.3: s += 10; r.append("Curve flat")
+    if gold_c > 0.5:    s += 15; r.append("Gold↑ (inflation hedge)")
+    if copper_c < 0:    s += 15; r.append("Copper↓ (weak demand)")
+    if vix > 18:        s += 10; r.append("VIX elevated")
+    if xlp_c > xlk_c + 0.5: s += 8; r.append("Defensives > Tech (stagflationary rotation)")
+    if xlu_c > 0.4:          s += 6; r.append("Utilities↑ (stable income)")
+    if skew_v > 130:         s += 5; r.append(f"SKEW {skew_v:.0f} (rising tail risk)")
+    if tlt_c < -0.3:         s += 5; r.append("TLT↓ (persistent inflation)")
     scores["stagflation"] = min(100, s); reasons["stagflation"] = r
 
-    # RÉCESSION
+    # RECESSION
     s = 0; r = []
     if slope is not None:
-        if slope < -0.5: s += 30; r.append("Courbe fortement inversée")
-        elif slope < 0:  s += 15; r.append("Courbe inversée")
+        if slope < -0.5: s += 30; r.append("Curve deeply inverted")
+        elif slope < 0:  s += 15; r.append("Curve inverted")
     if gcr is not None:
-        if gcr > 750:   s += 25; r.append(f"Or/Cu {gcr} (peur)")
+        if gcr > 750:   s += 25; r.append(f"Gold/Cu {gcr} (fear)")
         elif gcr > 650: s += 10
     if vix > 28:        s += 25; r.append("VIX>28")
     elif vix > 22:      s += 12
-    if copper_c < -1.5:   s += 20; r.append("Cuivre↓↓")
+    if copper_c < -1.5:   s += 20; r.append("Copper↓↓")
     elif copper_c < -0.5: s += 8
-    if hyg_c < -0.5:    s += 15; r.append("HYG↓ (spreads s'écartent)")
+    if hyg_c < -0.5:    s += 15; r.append("HYG↓ (spreads widening)")
     elif hyg_c < 0:     s += 5
-    if gold_c > 0.3:    s += 10; r.append("Or↑ (refuge)")
-    if tlt_c > 0.5:     s += 15; r.append("TLT↑↑ (signal recessionnaire fort)")
-    elif tlt_c > 0.2:   s += 7;  r.append("TLT↑ (obligations soutenues)")
-    if xlf_c < -1.0:    s += 12; r.append("Financières↓↓ (leading indicator récession)")
+    if gold_c > 0.3:    s += 10; r.append("Gold↑ (safe haven)")
+    if tlt_c > 0.5:     s += 15; r.append("TLT↑↑ (strong recession signal)")
+    elif tlt_c > 0.2:   s += 7;  r.append("TLT↑ (bonds supported)")
+    if xlf_c < -1.0:    s += 12; r.append("Financials↓↓ (recession leading indicator)")
     elif xlf_c < -0.3:  s += 5
     if eem_c < -1.0:    s += 8;  r.append("EM↓ (global slowdown)")
-    if usdjpy_c < -1.0:  s += 10; r.append("JPY↑↑ (carry unwind = risk-off global)")
+    if usdjpy_c < -1.0:  s += 10; r.append("JPY↑↑ (carry unwind = global risk-off)")
     elif usdjpy_c < -0.5: s += 5
-    if skew_v > 135:    s += 8;  r.append(f"SKEW {skew_v:.0f} (tail risk extrême)")
+    if skew_v > 135:    s += 8;  r.append(f"SKEW {skew_v:.0f} (extreme tail risk)")
     scores["recession"] = min(100, s); reasons["recession"] = r
 
-    # CRISE DE LIQUIDITÉ
+    # LIQUIDITY CRISIS
     s = 0; r = []
-    if vix > 35:     s += 35; r.append("VIX>35 (panique)")
+    if vix > 35:     s += 35; r.append("VIX>35 (panic)")
     elif vix > 28:   s += 20; r.append("VIX>28")
     elif vix > 22:   s += 8
-    if hyg_c < -1.5:   s += 35; r.append("HYG↓↓ (crise crédit)")
+    if hyg_c < -1.5:   s += 35; r.append("HYG↓↓ (credit crisis)")
     elif hyg_c < -0.5: s += 15
-    if lqd_c < -0.5: s += 10; r.append("IG↓ (spreads s'écartent)")
+    if lqd_c < -0.5: s += 10; r.append("IG↓ (spreads widening)")
     if vs200 is not None:
-        if vs200 < -10: s += 25; r.append("S&P<200j -10%")
+        if vs200 < -10: s += 25; r.append("S&P<200d -10%")
         elif vs200 < -3: s += 10
-    if gold_c > 1.0 and copper_c < -1.0: s += 20; r.append("Or↑+Cuivre↓ (fuite sécurité)")
+    if gold_c > 1.0 and copper_c < -1.0: s += 20; r.append("Gold↑+Copper↓ (flight to safety)")
     if dxy_c > 1.0:  s += 15; r.append("Dollar↑↑")
-    if ief_c > 0.5:  s += 10; r.append("Obligations souveraines↑↑")
-    if skew_v > 145:      s += 15; r.append(f"SKEW {skew_v:.0f} — tail risk extrême")
+    if ief_c > 0.5:  s += 10; r.append("Sovereign bonds↑↑")
+    if skew_v > 145:      s += 15; r.append(f"SKEW {skew_v:.0f} — extreme tail risk")
     elif skew_v > 135:    s += 8
-    if vvix_v > 115:      s += 15; r.append(f"VVIX {vvix_v:.0f} — vol-of-vol panique")
-    elif vvix_v > 100:    s += 8;  r.append(f"VVIX {vvix_v:.0f} — vol élevée")
-    if usdjpy_c < -1.5:   s += 15; r.append("JPY↑↑↑ (carry unwind = panique globale)")
+    if vvix_v > 115:      s += 15; r.append(f"VVIX {vvix_v:.0f} — vol-of-vol panic")
+    elif vvix_v > 100:    s += 8;  r.append(f"VVIX {vvix_v:.0f} — elevated vol")
+    if usdjpy_c < -1.5:   s += 15; r.append("JPY↑↑↑ (carry unwind = global panic)")
     elif usdjpy_c < -0.8: s += 7;  r.append("JPY↑ (risk-off carry)")
-    if xlf_c < -2.0:      s += 15; r.append("Banques↓↓ (stress bancaire systémique)")
+    if xlf_c < -2.0:      s += 15; r.append("Banks↓↓ (systemic banking stress)")
     elif xlf_c < -1.0:    s += 7
-    if emb_c < -1.0:      s += 10; r.append("EM Bonds↓ (fuite liquidité EM)")
+    if emb_c < -1.0:      s += 10; r.append("EM Bonds↓ (EM liquidity flight)")
     scores["crise_liquidite"] = min(100, s); reasons["crise_liquidite"] = r
 
     # REFLATION
     s = 0; r = []
-    if copper_c > 1.5:   s += 25; r.append("Cuivre↑↑ (Dr Copper = croissance)")
-    elif copper_c > 0.5: s += 12; r.append("Cuivre↑")
-    if xli_c > 0.8:      s += 20; r.append("Industriels↑↑ (activité mfg forte)")
-    elif xli_c > 0.2:    s += 10; r.append("Industriels↑")
-    if brent_c > 1.5:    s += 15; r.append("Brent↑ (reflation énergie)")
+    if copper_c > 1.5:   s += 25; r.append("Copper↑↑ (Dr Copper = growth)")
+    elif copper_c > 0.5: s += 12; r.append("Copper↑")
+    if xli_c > 0.8:      s += 20; r.append("Industrials↑↑ (strong mfg activity)")
+    elif xli_c > 0.2:    s += 10; r.append("Industrials↑")
+    if brent_c > 1.5:    s += 15; r.append("Brent↑ (energy reflation)")
     elif brent_c > 0.3:  s += 6
-    if vs200 is not None and vs200 > 8:   s += 20; r.append(f"S&P+{vs200}% vs 200j (bull fort)")
+    if vs200 is not None and vs200 > 8:   s += 20; r.append(f"S&P+{vs200}% vs 200d (strong bull)")
     elif vs200 is not None and vs200 > 3: s += 10
-    if slope is not None and slope > 1.0:  s += 15; r.append("Courbe pentue (croissance)")
+    if slope is not None and slope > 1.0:  s += 15; r.append("Curve steep (growth)")
     elif slope is not None and slope > 0.3: s += 6
-    if rel_perf > 0.3: s += 10; r.append("Small caps > large (risk-on large)")
+    if rel_perf > 0.3: s += 10; r.append("Small caps > large (broad risk-on)")
     elif rel_perf > 0: s += 4
     if vix < 18: s += 5
-    if eem_c > 1.0:    s += 10; r.append("EM↑↑ (reflation globale)")
+    if eem_c > 1.0:    s += 10; r.append("EM↑↑ (global reflation)")
     elif eem_c > 0.3:  s += 5;  r.append("EM↑ (global risk-on)")
-    if xlk_c > 1.0:   s += 8;  r.append("Tech↑↑ (croissance+momentum)")
-    if silver_c > 1.5: s += 8;  r.append("Argent↑↑ (reflation industrielle)")
-    if usdjpy_c > 0.5: s += 6;  r.append("JPY↓ (carry trades actifs)")
+    if xlk_c > 1.0:   s += 8;  r.append("Tech↑↑ (growth+momentum)")
+    if silver_c > 1.5: s += 8;  r.append("Silver↑↑ (industrial reflation)")
+    if usdjpy_c > 0.5: s += 6;  r.append("JPY↓ (active carry trades)")
     scores["reflation"] = min(100, s); reasons["reflation"] = r
 
     # SOFT LANDING
     s = 0; r = []
-    if vs200 is not None and vs200 > 0: s += 20; r.append("S&P > MA200 (croissance intacte)")
-    if brent_c < -0.5 and brent_c > -3: s += 20; r.append("Brent légèrement ↓ (désinflation graduelle)")
+    if vs200 is not None and vs200 > 0: s += 20; r.append("S&P > MA200 (growth intact)")
+    if brent_c < -0.5 and brent_c > -3: s += 20; r.append("Brent slightly ↓ (gradual disinflation)")
     elif brent_c < 0:  s += 8
-    if vix < 20:       s += 15; r.append("VIX<20 (pas de stress)")
-    if hyg_c > 0:      s += 12; r.append("HYG↑ (crédit solide)")
-    if lqd_c > 0:      s += 8;  r.append("IG↑ (spreads IG calmes)")
-    if slope is not None and slope > 0: s += 10; r.append("Courbe non-inversée")
-    if xli_c > 0:      s += 8;  r.append("Industriels positifs")
-    if copper_c > 0:   s += 5;  r.append("Cuivre stable")
-    if ief_c > 0 and brent_c < 0: s += 7; r.append("Taux baissent + énergie recule")
-    if xlf_c > 0:      s += 8;  r.append("Financières↑ (économie saine)")
-    if eem_c > 0:      s += 5;  r.append("EM stable (croissance globale intacte)")
-    if skew_v < 130:   s += 4;  r.append(f"SKEW {skew_v:.0f} (tail risk non-extrême)")
+    if vix < 20:       s += 15; r.append("VIX<20 (no stress)")
+    if hyg_c > 0:      s += 12; r.append("HYG↑ (solid credit)")
+    if lqd_c > 0:      s += 8;  r.append("IG↑ (calm IG spreads)")
+    if slope is not None and slope > 0: s += 10; r.append("Curve not inverted")
+    if xli_c > 0:      s += 8;  r.append("Industrials positive")
+    if copper_c > 0:   s += 5;  r.append("Copper stable")
+    if ief_c > 0 and brent_c < 0: s += 7; r.append("Rates falling + energy retreating")
+    if xlf_c > 0:      s += 8;  r.append("Financials↑ (healthy economy)")
+    if eem_c > 0:      s += 5;  r.append("EM stable (global growth intact)")
+    if skew_v < 130:   s += 4;  r.append(f"SKEW {skew_v:.0f} (non-extreme tail risk)")
     scores["soft_landing"] = min(100, s); reasons["soft_landing"] = r
 
-    # CHOC INFLATIONNISTE
+    # INFLATION SHOCK
     s = 0; r = []
-    if brent_c > 4.0:   s += 40; r.append("Brent↑↑↑ (choc énergie majeur)")
+    if brent_c > 4.0:   s += 40; r.append("Brent↑↑↑ (major energy shock)")
     elif brent_c > 2.0: s += 25; r.append("Brent↑↑")
     elif brent_c > 0.8: s += 10
-    if ng_c > 4.0:      s += 20; r.append("Gaz↑↑↑ (choc supply gaz)")
-    elif ng_c > 2.0:    s += 12; r.append("Gaz↑↑")
-    if gold_c > 1.0:    s += 20; r.append("Or↑↑ (refuge inflation/géo)")
-    elif gold_c > 0.3:  s += 8;  r.append("Or↑")
-    if vix > 22:        s += 15; r.append("VIX↑ (stress montant)")
+    if ng_c > 4.0:      s += 20; r.append("Gas↑↑↑ (gas supply shock)")
+    elif ng_c > 2.0:    s += 12; r.append("Gas↑↑")
+    if gold_c > 1.0:    s += 20; r.append("Gold↑↑ (inflation/geo hedge)")
+    elif gold_c > 0.3:  s += 8;  r.append("Gold↑")
+    if vix > 22:        s += 15; r.append("VIX↑ (rising stress)")
     elif vix > 18:      s += 5
-    if copper_c < -0.5: s += 8; r.append("Cuivre↓ (demand destruction)")
-    if ief_c < -0.2:    s += 8; r.append("Trésor↓ (taux longs remontent)")
-    if ovx_v > 45:      s += 15; r.append(f"OVX {ovx_v:.0f} — vol pétrole extrême")
-    elif ovx_v > 35:    s += 8;  r.append(f"OVX {ovx_v:.0f} — vol pétrole élevée")
-    if gvz_v > 22:      s += 8;  r.append(f"GVZ {gvz_v:.0f} — vol or élevée")
-    if xlp_c > 0.5:     s += 6;  r.append("Défensifs↑ (rotation anti-inflation)")
-    if tlt_c < -0.5:    s += 8;  r.append("TLT↓↓ (anticipation inflation)")
+    if copper_c < -0.5: s += 8; r.append("Copper↓ (demand destruction)")
+    if ief_c < -0.2:    s += 8; r.append("Treasuries↓ (long rates rising)")
+    if ovx_v > 45:      s += 15; r.append(f"OVX {ovx_v:.0f} — extreme oil vol")
+    elif ovx_v > 35:    s += 8;  r.append(f"OVX {ovx_v:.0f} — elevated oil vol")
+    if gvz_v > 22:      s += 8;  r.append(f"GVZ {gvz_v:.0f} — elevated gold vol")
+    if xlp_c > 0.5:     s += 6;  r.append("Defensives↑ (anti-inflation rotation)")
+    if tlt_c < -0.5:    s += 8;  r.append("TLT↓↓ (inflation expectations)")
     scores["inflation_shock"] = min(100, s); reasons["inflation_shock"] = r
 
     return scores, reasons

backend/services/database.py

@@ -6239,6 +6239,32 @@ def get_latest_saxo_snapshot_rows(symbol: str) -> List[Dict[str, Any]]:
     return [dict(r) for r in rows]
 
 
+def get_saxo_daily_snapshot_rows(symbol: str, days: int = 365) -> List[Dict[str, Any]]:
+    """One row per (snapshot_date, expiry_date, strike, option_type) — the day's last
+    capture per contract, for every day in the last `days` that has any snapshot. Unlike
+    get_latest_saxo_snapshot_rows (overall-latest only), this gives an end-of-day
+    cross-section per day, used to build a Saxo-only ATM IV history (services.saxo_iv_engine)
+    for IV Rank/Percentile computed purely from our own accumulated Saxo captures — never
+    blended with the yfinance-based iv_history table."""
+    conn = get_conn()
+    rows = conn.execute("""
+        SELECT s.* FROM saxo_option_snapshots s
+        JOIN (
+            SELECT snapshot_date, expiry_date, strike, option_type, MAX(created_at) AS max_created
+            FROM saxo_option_snapshots
+            WHERE symbol = ? AND snapshot_date >= date('now', ?)
+            GROUP BY snapshot_date, expiry_date, strike, option_type
+        ) latest
+        ON s.snapshot_date = latest.snapshot_date AND s.expiry_date = latest.expiry_date
+           AND s.strike = latest.strike AND s.option_type = latest.option_type
+           AND s.created_at = latest.max_created
+        WHERE s.symbol = ?
+        ORDER BY s.snapshot_date, s.expiry_date, s.strike
+    """, (symbol, f"-{days} days", symbol)).fetchall()
+    conn.close()
+    return [dict(r) for r in rows]
+
+
 def get_snapshot_rows_asof(symbol: Optional[str] = None, as_of: Optional[str] = None) -> List[Dict[str, Any]]:
     """One row per (symbol, expiry_date, strike, option_type) — the freshest capture at or
     before `as_of` (an ISO datetime string), or simply the freshest capture overall when

backend/services/iv_engine.py

@@ -278,11 +278,11 @@ def get_skew(ticker: str, target_days: int = 30) -> Dict[str, Any]:
         result["iv_call_25d"] = round(iv_call * 100, 1)
 
         if put_skew > 0.03:
-            result["interpretation"] = "Marché achète des puts — protection baissière élevée"
+            result["interpretation"] = "Market buying puts — high downside protection"
         elif put_skew < -0.02:
-            result["interpretation"] = "Marché achète des calls — biais haussier spéculatif"
+            result["interpretation"] = "Market buying calls — speculative bullish bias"
         else:
-            result["interpretation"] = "Skew équilibré — pas de biais directionnel fort"
+            result["interpretation"] = "Balanced skew — no strong directional bias"
 
     except Exception as e:
         logger.debug(f"[Skew] {proxy}: {e}")

backend/services/market_event_detector.py

@@ -1316,11 +1316,11 @@ _REGIME_SEVERITY = {
 }
 
 _REGIME_LABELS = {
-    "goldilocks": "Goldilocks", "desinflation": "Désinflation",
+    "goldilocks": "Goldilocks", "desinflation": "Disinflation",
     "soft_landing": "Soft Landing", "reflation": "Reflation",
-    "stagflation": "Stagflation", "inflation_shock": "Choc Inflationniste",
-    "recession": "Récession", "crise_liquidite": "Crise de liquidité",
-    "incertain": "Incertain",
+    "stagflation": "Stagflation", "inflation_shock": "Inflation Shock",
+    "recession": "Recession", "crise_liquidite": "Liquidity Crisis",
+    "incertain": "Uncertain",
 }
 
 

backend/services/saxo_iv_engine.py

@@ -0,0 +1,211 @@
+"""
+Options analytics computed exclusively from our own accumulated Saxo snapshot history
+(services.database.saxo_option_snapshots) — deliberately never blended with the
+yfinance-based services.iv_engine, so IV/skew/term-structure for a Saxo watchlist symbol
+always reflects what the broker itself quoted. Powers Options Lab's Saxo section.
+
+Mirrors iv_engine.py's output shape (iv_current_pct, iv_rank, iv_percentile,
+history_days, iv_change_1d_pct, skew, term_structure, signal) so the frontend can reuse
+the same rendering patterns — just fed by a different, non-mixed data source. Options
+flow (open interest based) isn't available: Saxo snapshots don't carry OI/volume.
+"""
+from datetime import date, datetime, timezone
+from typing import Any, Dict, List, Optional
+
+_DAYS_TARGETS = {"iv_30d": 30, "iv_60d": 60, "iv_90d": 90, "iv_180d": 180}
+
+
+def _days_to(expiry_date: str, today: date) -> int:
+    return (datetime.strptime(expiry_date[:10], "%Y-%m-%d").date() - today).days
+
+
+def _atm_iv(rows: List[Dict[str, Any]], spot: Optional[float], target_days: int, today: date) -> Optional[Dict[str, Any]]:
+    """Pick the expiry closest to target_days among `rows` (already one row per contract),
+    then the strike closest to spot within it. Averages call+put IV at that strike."""
+    if not rows or not spot:
+        return None
+    by_expiry: Dict[str, List[Dict[str, Any]]] = {}
+    for r in rows:
+        by_expiry.setdefault(r["expiry_date"], []).append(r)
+
+    candidates = [(e, _days_to(e, today)) for e in by_expiry if _days_to(e, today) >= 0]
+    if not candidates:
+        return None
+    best_exp, best_days = min(candidates, key=lambda x: abs(x[1] - target_days))
+
+    exp_rows = by_expiry[best_exp]
+    strikes = sorted({r["strike"] for r in exp_rows})
+    if not strikes:
+        return None
+    atm_strike = min(strikes, key=lambda s: abs(s - spot))
+
+    ivs = [r["volatility_pct"] for r in exp_rows if r["strike"] == atm_strike and r.get("volatility_pct")]
+    if not ivs:
+        return None
+    return {
+        "iv_pct": round(sum(ivs) / len(ivs), 1),
+        "expiry_date": best_exp,
+        "days_to_expiry": best_days,
+    }
+
+
+def _skew(rows: List[Dict[str, Any]], target_days: int, today: date) -> Dict[str, Any]:
+    """25-delta put IV minus 25-delta call IV, using Saxo's own delta field (no strike
+    approximation needed, unlike the yfinance path which has to estimate ~10%/90% OTM)."""
+    result: Dict[str, Any] = {"put_skew": None, "skew_pct": None, "iv_put_25d": None, "iv_call_25d": None, "interpretation": None}
+    if not rows:
+        return result
+    by_expiry: Dict[str, List[Dict[str, Any]]] = {}
+    for r in rows:
+        by_expiry.setdefault(r["expiry_date"], []).append(r)
+    candidates = [(e, _days_to(e, today)) for e in by_expiry if _days_to(e, today) >= 0]
+    if not candidates:
+        return result
+    best_exp, _ = min(candidates, key=lambda x: abs(x[1] - target_days))
+    exp_rows = by_expiry[best_exp]
+
+    puts  = [r for r in exp_rows if r["option_type"] == "put"  and r.get("delta") is not None and r.get("volatility_pct")]
+    calls = [r for r in exp_rows if r["option_type"] == "call" and r.get("delta") is not None and r.get("volatility_pct")]
+    if not puts or not calls:
+        return result
+
+    put_row  = min(puts,  key=lambda r: abs(r["delta"] - (-0.25)))
+    call_row = min(calls, key=lambda r: abs(r["delta"] - 0.25))
+
+    iv_put_pct, iv_call_pct = put_row["volatility_pct"], call_row["volatility_pct"]
+    put_skew = (iv_put_pct - iv_call_pct) / 100  # decimal, matches iv_engine.py's convention
+
+    result["put_skew"] = round(put_skew, 4)
+    result["skew_pct"] = round(iv_put_pct - iv_call_pct, 1)
+    result["iv_put_25d"] = round(iv_put_pct, 1)
+    result["iv_call_25d"] = round(iv_call_pct, 1)
+    if put_skew > 0.03:
+        result["interpretation"] = "Market buying puts — high downside protection"
+    elif put_skew < -0.02:
+        result["interpretation"] = "Market buying calls — speculative bullish bias"
+    else:
+        result["interpretation"] = "Balanced skew — no strong directional bias"
+    return result
+
+
+def _term_structure(rows: List[Dict[str, Any]], spot: Optional[float], today: date) -> Dict[str, Any]:
+    result: Dict[str, Any] = {"iv_30d": None, "iv_60d": None, "iv_90d": None, "iv_180d": None, "structure": None}
+    for field, target in _DAYS_TARGETS.items():
+        atm = _atm_iv(rows, spot, target, today)
+        if atm and abs(atm["days_to_expiry"] - target) <= 20:
+            result[field] = round(atm["iv_pct"] / 100, 4)  # decimal, matches iv_engine.py's convention
+    iv30, iv90 = result.get("iv_30d"), result.get("iv_90d")
+    if iv30 and iv90:
+        diff = iv90 - iv30
+        result["structure"] = "contango" if diff > 0.015 else "backwardation" if diff < -0.015 else "flat"
+    return result
+
+
+def _daily_atm_series(symbol: str, target_days: int = 30, days: int = 365) -> List[Dict[str, Any]]:
+    """One ATM-IV point per day that has Saxo snapshots, oldest first."""
+    from services.database import get_saxo_daily_snapshot_rows
+
+    daily_rows = get_saxo_daily_snapshot_rows(symbol, days=days)
+    by_date: Dict[str, List[Dict[str, Any]]] = {}
+    for r in daily_rows:
+        by_date.setdefault(r["snapshot_date"][:10], []).append(r)
+
+    series = []
+    for d, rows in sorted(by_date.items()):
+        spot = next((r["spot"] for r in rows if r.get("spot") is not None), None)
+        try:
+            ref_date = datetime.strptime(d, "%Y-%m-%d").date()
+        except ValueError:
+            continue
+        atm = _atm_iv(rows, spot, target_days, ref_date)
+        if atm:
+            series.append({"date": d, "iv_pct": atm["iv_pct"]})
+    return series
+
+
+def get_saxo_iv_snapshot(symbol: str, target_days: int = 30) -> Dict[str, Any]:
+    """Full Saxo-only IV snapshot for one watchlist symbol: current ATM IV, rank/percentile
+    (from Saxo's own history), term structure, skew, day-over-day change."""
+    from services.database import get_latest_saxo_snapshot_rows
+
+    symbol = symbol.upper()
+    today = date.today()
+    rows = get_latest_saxo_snapshot_rows(symbol)
+    spot = next((r["spot"] for r in rows if r.get("spot") is not None), None) if rows else None
+
+    atm = _atm_iv(rows, spot, target_days, today) if rows else None
+    iv_current_pct = atm["iv_pct"] if atm else None
+
+    series = _daily_atm_series(symbol, target_days)
+    history_days = len(series)
+    iv_rank = iv_percentile = iv_min_52w = iv_max_52w = None
+    iv_change_1d_pct = None
+    if series:
+        hist_vals = [p["iv_pct"] for p in series]
+        iv_min_52w, iv_max_52w = min(hist_vals), max(hist_vals)
+        if iv_current_pct is not None:
+            if iv_max_52w > iv_min_52w:
+                iv_rank = round((iv_current_pct - iv_min_52w) / (iv_max_52w - iv_min_52w) * 100, 1)
+            else:
+                iv_rank = 50.0
+            iv_percentile = round(sum(1 for v in hist_vals if v < iv_current_pct) / len(hist_vals) * 100, 1)
+        # Day-over-day: last series point strictly before today vs current
+        todays_iso = today.isoformat()
+        prior = [p for p in series if p["date"] < todays_iso]
+        if prior and iv_current_pct is not None:
+            iv_change_1d_pct = round(iv_current_pct - prior[-1]["iv_pct"], 1)
+
+    return {
+        "ticker": symbol,
+        "proxy": symbol,
+        "iv_current_pct": iv_current_pct,
+        "iv_change_1d_pct": iv_change_1d_pct,
+        "iv_rank": iv_rank,
+        "iv_percentile": iv_percentile,
+        "history_days": history_days,
+        "iv_min_52w_pct": iv_min_52w,
+        "iv_max_52w_pct": iv_max_52w,
+        "term_structure": _term_structure(rows, spot, today) if rows else _term_structure([], None, today),
+        "skew": _skew(rows, target_days, today) if rows else _skew([], target_days, today),
+        "options_flow": {},  # not available — Saxo snapshots carry no open interest/volume
+        "fetched_at": datetime.now(timezone.utc).isoformat(),
+        "iv_source": "saxo" if atm else "none",
+        "spot": spot,
+    }
+
+
+def get_saxo_iv_history(symbol: str, days: int = 90) -> Dict[str, Any]:
+    series = _daily_atm_series(symbol.upper(), days=days)
+    # Match iv_engine's get_iv_history row shape (recorded_date, iv_current as a decimal)
+    history = [{"recorded_date": p["date"], "iv_current": p["iv_pct"] / 100} for p in reversed(series)]
+    return {"ticker": symbol, "proxy": symbol, "history": history, "count": len(history)}
+
+
+def get_saxo_iv_watchlist() -> Dict[str, Any]:
+    """Summary row per symbol in the Saxo watchlist (services.saxo_scheduler) — the same
+    symbols already being snapshotted every ~5 min, no separate mapping to maintain."""
+    from services.saxo_scheduler import get_watchlist
+
+    results = []
+    for symbol in get_watchlist():
+        snap = get_saxo_iv_snapshot(symbol)
+        if snap["iv_current_pct"] is None:
+            continue
+        rank = snap["iv_rank"]
+        results.append({
+            "ticker": symbol,
+            "iv_current_pct": snap["iv_current_pct"],
+            "iv_change_1d_pct": snap["iv_change_1d_pct"],
+            "iv_rank": rank,
+            "iv_percentile": snap["iv_percentile"],
+            "history_days": snap["history_days"],
+            "iv_source": "saxo",
+            "signal": (
+                "sell_vol" if (rank or 0) > 80
+                else "buy_vol" if (rank or 100) < 20
+                else "neutral"
+            ),
+        })
+
+    results.sort(key=lambda x: -(x.get("iv_rank") or 0))
+    return {"items": results, "count": len(results)}

frontend/src/hooks/useApi.ts

@@ -919,6 +919,31 @@ export const useIvForTrade = (underlying: string) =>
     staleTime: 60 * 60_000,
   })
 
+// ── Saxo-only IV analytics (Options Lab "Saxo" section) — computed from our own
+// accumulated saxo_option_snapshots, never blended with the yfinance-based hooks above ──
+export const useSaxoIvWatchlist = () =>
+  useQuery({
+    queryKey: ['saxo-iv-watchlist'],
+    queryFn: () => api.get('/saxo/iv-watchlist').then(r => r.data),
+    staleTime: 5 * 60_000,
+  })
+
+export const useSaxoIvSnapshot = (symbol: string) =>
+  useQuery({
+    queryKey: ['saxo-iv-snapshot', symbol],
+    queryFn: () => api.get(`/saxo/iv-snapshot/${encodeURIComponent(symbol)}`).then(r => r.data),
+    enabled: !!symbol,
+    staleTime: 5 * 60_000,
+  })
+
+export const useSaxoIvHistory = (symbol: string, days = 90) =>
+  useQuery({
+    queryKey: ['saxo-iv-history', symbol, days],
+    queryFn: () => api.get(`/saxo/iv-history/${encodeURIComponent(symbol)}`, { params: { days } }).then(r => r.data),
+    enabled: !!symbol,
+    staleTime: 5 * 60_000,
+  })
+
 // ── Analytics Phase 4 — Bayesian + Clustering + Embeddings ───────────────────
 
 export const useBayesianPosteriors = () =>

frontend/src/pages/Dashboard.tsx

@@ -122,16 +122,16 @@ function EcoEventRow({ ev, showActual }: { ev: any; showActual: boolean }) {
     <div className="flex items-center gap-1.5 text-[10px]">
       <span className="shrink-0">{CURRENCY_FLAGS[ev.currency] ?? ev.currency}</span>
       <span className={clsx('w-1.5 h-1.5 rounded-full shrink-0', IMPACT_DOT[ev.impact] ?? 'bg-slate-400')} />
-      <span className="text-white truncate flex-1 line-clamp-1">{ev.event_name}</span>
+      <span className="text-slate-200 truncate flex-1 line-clamp-1 max-w-[108px]" title={ev.event_name}>{ev.event_name}</span>
       {showActual && ev.actual_value ? (
-        <span className="text-[9px] font-mono shrink-0">
+        <span className="text-[10px] font-mono shrink-0">
           <span className="text-white font-semibold">{ev.actual_value}</span>
-          {ev.forecast_value && <span className="text-slate-600"> /{ev.forecast_value}</span>}
+          {ev.forecast_value && <span className="text-slate-400"> /{ev.forecast_value}</span>}
         </span>
       ) : ev.forecast_value ? (
-        <span className="text-[9px] font-mono text-slate-600 shrink-0">F {ev.forecast_value}</span>
+        <span className="text-[10px] font-mono text-slate-400 shrink-0">F {ev.forecast_value}</span>
       ) : null}
-      {ev.event_time && <span className="text-slate-700 text-[9px] shrink-0 font-mono">{ev.event_time}</span>}
+      {ev.event_time && <span className="text-slate-500 text-[9px] shrink-0 font-mono">{ev.event_time}</span>}
     </div>
   )
 }
@@ -347,7 +347,7 @@ export default function Dashboard() {
             <div className="section-title flex items-center gap-1 mb-0">
               <Globe className="w-3 h-3" /> Geopolitical Risk
             </div>
-            <Link to="/geo" className="flex items-center gap-0.5 text-[10px] text-slate-600 hover:text-slate-300 transition-colors">
+            <Link to="/geo" className="flex items-center gap-0.5 text-[10px] text-slate-400 hover:text-slate-300 transition-colors">
               Geo news <ArrowUpRight className="w-2.5 h-2.5" />
             </Link>
           </div>
@@ -376,14 +376,16 @@ export default function Dashboard() {
                 <div className={clsx('h-2 rounded-full', gauge.bg)} style={{ width: `${riskScore.score}%` }} />
               </div>
               {riskScore.computed_at && (
-                <div className="mt-1.5 text-[9px] text-slate-600 shrink-0">
-                  Score IA — figé au dernier cycle ({formatTimeShort(riskScore.computed_at)})
-                  {riskScore.rationale && <span className="text-slate-500"> · {riskScore.rationale}</span>}
+                <div className="mt-1.5 text-[9px] text-slate-400 shrink-0">
+                  <span>AI Score — frozen at last cycle ({formatTimeShort(riskScore.computed_at)})</span>
+                  {riskScore.rationale && (
+                    <p className="text-slate-500 line-clamp-2 mt-0.5" title={riskScore.rationale}>{riskScore.rationale}</p>
+                  )}
                 </div>
               )}
               {topNews.length > 0 && (
-                <div className="mt-2.5 pt-2 border-t border-slate-700/30 flex flex-col flex-1 min-h-0">
-                  <div className="flex items-center gap-1 text-[9px] text-slate-600 mb-1 shrink-0">
+                <div className="mt-2 pt-2 border-t border-slate-700/30 flex flex-col flex-1 min-h-0">
+                  <div className="flex items-center gap-1 text-[9px] text-slate-400 mb-1 shrink-0">
                     <Newspaper className="w-2.5 h-2.5" /> Top news
                   </div>
                   <div className="space-y-1.5 overflow-y-auto flex-1 min-h-0">
@@ -410,7 +412,7 @@ export default function Dashboard() {
         <div ref={watchlistCardRef} className="card col-span-1">
           <div className="flex items-center justify-between mb-2">
             <div className="section-title mb-0">📡 Watchlist Radar</div>
-            <Link to="/config" className="flex items-center gap-0.5 text-[10px] text-slate-600 hover:text-slate-300 transition-colors">
+            <Link to="/config" className="flex items-center gap-0.5 text-[10px] text-slate-400 hover:text-slate-300 transition-colors">
               Manage <ArrowUpRight className="w-2.5 h-2.5" />
             </Link>
           </div>
@@ -419,7 +421,7 @@ export default function Dashboard() {
               <ResponsiveContainer width="100%" height={130}>
                 <RadarChart data={watchlistRadarData}>
                   <PolarGrid stroke="#1e2d4d" />
-                  <PolarAngleAxis dataKey="subject" tick={{ fill: '#64748b', fontSize: 9 }} />
+                  <PolarAngleAxis dataKey="subject" tick={{ fill: '#94a3b8', fontSize: 9 }} />
                   <Radar dataKey="ref" stroke="#334155" strokeDasharray="3 3" fill="transparent" isAnimationActive={false} />
                   <Radar dataKey="value" stroke="#3b82f6" fill="#3b82f6" fillOpacity={0.25} />
                 </RadarChart>
@@ -427,9 +429,9 @@ export default function Dashboard() {
               <div className="mt-1.5 pt-1.5 border-t border-slate-700/30 space-y-1">
                 {((watchlistQuotesData as any)?.items ?? []).map((it: any) => (
                   <div key={it.ticker} className="flex items-center justify-between text-[10px]">
-                    <span className="text-slate-400 font-mono">{it.ticker}</span>
+                    <span className="text-slate-300 font-mono">{it.ticker}</span>
                     <div className="flex items-center gap-2">
-                      <span className="text-slate-500 font-mono">{fmtPrice(it.price)}</span>
+                      <span className="text-slate-400 font-mono">{fmtPrice(it.price)}</span>
                       <span className={clsx('font-mono font-bold w-12 text-right', (it.change_pct ?? 0) >= 0 ? 'text-emerald-400' : 'text-red-400')}>
                         {it.change_pct != null ? `${it.change_pct >= 0 ? '+' : ''}${it.change_pct.toFixed(2)}%` : '—'}
                       </span>
@@ -499,7 +501,7 @@ export default function Dashboard() {
               style={row1Height ? { height: row1Height } : undefined}>
               <div className="flex items-center justify-between mb-1">
                 <span className="section-title mb-0 text-[10px]">🌐 Macro Regime</span>
-                <ArrowUpRight className="w-3 h-3 text-slate-600" />
+                <ArrowUpRight className="w-3 h-3 text-slate-500" />
               </div>
               <div className={clsx('text-base font-bold mt-1', colorClass)}>
                 {macroInfo ? `${macroInfo.emoji} ${macroInfo.label}` : '—'}
@@ -512,7 +514,7 @@ export default function Dashboard() {
                     const isDom = key === dom
                     return (
                       <div key={key} className="flex items-center gap-1.5">
-                        <span className={clsx('text-[9px] w-16 truncate', isDom ? 'text-slate-200 font-semibold' : 'text-slate-500')}>
+                        <span className={clsx('text-[10px] w-16 truncate', isDom ? 'text-slate-200 font-semibold' : 'text-slate-400')}>
                           {REGIME_LABELS[key] ?? key}
                         </span>
                         <div className="flex-1 h-1.5 bg-dark-700 rounded-full overflow-hidden">
@@ -521,7 +523,7 @@ export default function Dashboard() {
                             style={{ width: `${pct}%` }}
                           />
                         </div>
-                        <span className={clsx('text-[9px] font-mono w-5 text-right', isDom ? 'text-blue-400' : 'text-slate-600')}>
+                        <span className={clsx('text-[10px] font-mono w-5 text-right', isDom ? 'text-blue-400' : 'text-slate-400')}>
                           {score}
                         </span>
                       </div>
@@ -542,11 +544,11 @@ export default function Dashboard() {
                     const h = hint(val, chg)
                     return (
                       <div key={key} className="flex items-center gap-1.5">
-                        <span className="text-[9px] text-slate-600 w-16 shrink-0">{label}</span>
-                        <span className={clsx('text-[9px] font-mono font-semibold shrink-0', c)}>
+                        <span className="text-[10px] text-slate-400 w-16 shrink-0">{label}</span>
+                        <span className={clsx('text-[10px] font-mono font-semibold shrink-0', c)}>
                           {fmt(val, chg)}
                         </span>
-                        <span className="text-[8px] text-slate-700 truncate">{h}</span>
+                        <span className="text-[9px] text-slate-500 truncate">{h}</span>
                       </div>
                     )
                   })}
@@ -575,7 +577,7 @@ export default function Dashboard() {
               {todayEvents.length > 0 && (
                 <div>
                   <div className="flex items-center gap-1.5 text-[9px] font-semibold text-slate-300 bg-dark-700/50 rounded px-1.5 py-0.5 mb-1">
-                    Aujourd'hui <span className="badge-blue text-[8px] px-1 py-0">TODAY</span>
+                    Today <span className="badge-blue text-[8px] px-1 py-0">TODAY</span>
                   </div>
                   <div className="space-y-1 pl-0.5">
                     {todayEvents.map((ev: any, i: number) => <EcoEventRow key={i} ev={ev} showActual />)}
@@ -593,7 +595,7 @@ export default function Dashboard() {
                 </div>
               ))}
             </div>
-          ) : <div className="text-slate-600 text-xs mt-2">No events this week</div>}
+          ) : <div className="text-slate-500 text-xs mt-2">No events this week</div>}
         </div>
       </div>
 

frontend/src/pages/OptionsLab.tsx

@@ -1,6 +1,9 @@
 import { useState } from 'react'
-import { useIvWatchlist, useIvSnapshot, useIvHistory, useWatchlistTickers, useAddWatchlistTicker, useRemoveWatchlistTicker } from '../hooks/useApi'
-import { Activity, TrendingUp, TrendingDown, Minus, RefreshCw, ChevronDown, ChevronUp, Database, Plus, Trash2, List } from 'lucide-react'
+import {
+  useIvWatchlist, useIvSnapshot, useIvHistory, useWatchlistTickers, useAddWatchlistTicker, useRemoveWatchlistTicker,
+  useSaxoIvWatchlist, useSaxoIvSnapshot, useSaxoIvHistory,
+} from '../hooks/useApi'
+import { Activity, TrendingUp, TrendingDown, Minus, RefreshCw, ChevronDown, ChevronUp, Database, Plus, Trash2, List, Link2 } from 'lucide-react'
 import { api } from '../hooks/useApi'
 import { useQueryClient } from '@tanstack/react-query'
 import clsx from 'clsx'
@@ -176,7 +179,7 @@ function TickerDetail({ ticker }: { ticker: string }) {
             {' · '}{snap.history_days}d of history
             {snap.iv_change_1d_pct != null && (
               <>
-                {' · '}vs veille{' '}
+                {' · '}vs prior day{' '}
                 <span className={snap.iv_change_1d_pct > 0 ? 'text-orange-400' : snap.iv_change_1d_pct < 0 ? 'text-blue-400' : 'text-slate-400'}>
                   {snap.iv_change_1d_pct >= 0 ? '+' : ''}{snap.iv_change_1d_pct}pt
                 </span>
@@ -261,6 +264,170 @@ function WatchlistRow({ item }: { item: any }) {
   )
 }
 
+// ── Saxo-only expanded detail — separate from TickerDetail above by design: this reads
+// exclusively from /api/saxo/iv-*, computed from our own accumulated saxo_option_snapshots,
+// never blended with the yfinance-based IV watchlist. No Options Flow section — Saxo
+// snapshots carry no open interest/volume, so there's nothing honest to show there. ──
+function SaxoTickerDetail({ ticker }: { ticker: string }) {
+  const { data: snap, isLoading } = useSaxoIvSnapshot(ticker)
+  const { data: histData } = useSaxoIvHistory(ticker, 90)
+
+  if (isLoading) return <div className="h-20 mt-3 animate-pulse bg-dark-700 rounded" />
+  if (!snap) return null
+
+  const ts   = (snap.term_structure || {}) as any
+  const skew = (snap.skew           || {}) as any
+  const history: any[] = histData?.history || []
+
+  return (
+    <div className="mt-3 border-t border-slate-700/30 pt-3 grid grid-cols-2 gap-4">
+      {/* Term Structure */}
+      <div>
+        <div className="text-[9px] text-slate-500 uppercase tracking-wide font-semibold mb-2">Term Structure (Saxo)</div>
+        <div className="space-y-1.5">
+          {([['30d', ts.iv_30d], ['60d', ts.iv_60d], ['90d', ts.iv_90d], ['180d', ts.iv_180d]] as [string, number | undefined][]).map(([label, iv]) =>
+            iv ? (
+              <div key={label} className="flex items-center gap-2">
+                <span className="text-[9px] text-slate-500 w-7 shrink-0">{label}</span>
+                <div className="flex-1 h-1 bg-slate-800 rounded-full overflow-hidden">
+                  <div className="h-full bg-blue-500/50 rounded-full" style={{ width: `${Math.min(iv * 300, 100)}%` }} />
+                </div>
+                <span className="text-[9px] text-slate-300 font-mono w-9 text-right">{(iv * 100).toFixed(1)}%</span>
+              </div>
+            ) : null
+          )}
+          {ts.structure && <div className="mt-1"><StructureBadge structure={ts.structure} /></div>}
+          {!ts.iv_30d && !ts.iv_60d && !ts.iv_90d && !ts.iv_180d && (
+            <div className="text-[9px] text-slate-500 italic">Not enough expiries captured yet</div>
+          )}
+        </div>
+      </div>
+
+      {/* Skew */}
+      <div>
+        <div className="text-[9px] text-slate-500 uppercase tracking-wide font-semibold mb-2">Skew Put/Call (Saxo)</div>
+        {skew.put_skew != null ? (
+          <div className="space-y-1">
+            <div className="flex justify-between text-[9px]">
+              <span className="text-slate-500">Put 25Δ</span>
+              <span className="text-slate-300 font-mono">{skew.iv_put_25d}%</span>
+            </div>
+            <div className="flex justify-between text-[9px]">
+              <span className="text-slate-500">Call 25Δ</span>
+              <span className="text-slate-300 font-mono">{skew.iv_call_25d}%</span>
+            </div>
+            <div className={clsx('text-[10px] font-bold mt-1',
+              skew.skew_pct > 3 ? 'text-red-400' : skew.skew_pct < -2 ? 'text-blue-400' : 'text-slate-400')}>
+              {skew.skew_pct > 0 ? '+' : ''}{skew.skew_pct} pts
+            </div>
+            {skew.interpretation && (
+              <div className="text-[9px] text-slate-500 italic leading-tight">{skew.interpretation}</div>
+            )}
+          </div>
+        ) : (
+          <div className="text-[9px] text-slate-500 italic">Insufficient data</div>
+        )}
+      </div>
+
+      {/* History sparkline */}
+      {history.length > 4 && (
+        <div className="col-span-2 flex items-center gap-3 pt-1 border-t border-slate-700/20">
+          <span className="text-[9px] text-slate-500 shrink-0">IV 90d</span>
+          <IvSparkline history={history} />
+          <div className="text-[9px] text-slate-500">
+            min <span className="text-slate-400">{snap.iv_min_52w_pct}%</span>
+            {' · '}max <span className="text-slate-400">{snap.iv_max_52w_pct}%</span>
+            {' · '}{snap.history_days}d of Saxo history
+            {snap.iv_change_1d_pct != null && (
+              <>
+                {' · '}vs prior day{' '}
+                <span className={snap.iv_change_1d_pct > 0 ? 'text-orange-400' : snap.iv_change_1d_pct < 0 ? 'text-blue-400' : 'text-slate-400'}>
+                  {snap.iv_change_1d_pct >= 0 ? '+' : ''}{snap.iv_change_1d_pct}pt
+                </span>
+              </>
+            )}
+          </div>
+        </div>
+      )}
+      {history.length <= 4 && (
+        <div className="col-span-2 text-[9px] text-slate-500 italic pt-1 border-t border-slate-700/20">
+          Not enough accumulated days yet for a Rank/Percentile — keep the Saxo watchlist snapshotting and it fills in on its own.
+        </div>
+      )}
+    </div>
+  )
+}
+
+// ── Saxo Watchlist Row — same layout as WatchlistRow, fed by /api/saxo/iv-* only ──
+function SaxoWatchlistRow({ item }: { item: any }) {
+  const [expanded, setExpanded] = useState(false)
+  const signal = ivSignalLabel(item.iv_rank)
+
+  return (
+    <div className={clsx('rounded-lg border transition-all', ivRankBg(item.iv_rank))}>
+      <div
+        className="flex items-center gap-3 px-3 py-2.5 cursor-pointer"
+        onClick={() => setExpanded(!expanded)}
+      >
+        <div className="w-14 shrink-0">
+          <div className="text-sm font-bold text-slate-200">{item.ticker}</div>
+          <div className="text-[8px] text-slate-500 flex items-center gap-0.5"><Link2 className="w-2 h-2" /> Saxo</div>
+        </div>
+
+        <div className="w-16 shrink-0">
+          <span className={clsx('text-sm font-bold font-mono', ivRankColor(item.iv_rank))}>
+            {item.iv_current_pct != null ? `${item.iv_current_pct}%` : '—'}
+          </span>
+          {item.iv_change_1d_pct != null && Math.abs(item.iv_change_1d_pct) >= 0.1 && (
+            <span className={clsx('ml-1 text-[9px] font-mono', item.iv_change_1d_pct > 0 ? 'text-orange-400' : 'text-blue-400')}>
+              {item.iv_change_1d_pct >= 0 ? '+' : ''}{item.iv_change_1d_pct.toFixed(1)}
+            </span>
+          )}
+          <div className="text-[8px] text-slate-500">Current IV</div>
+        </div>
+
+        <div className="flex-1 min-w-0 space-y-1">
+          <div className="flex items-center justify-between">
+            <span className="text-[9px] text-slate-500">IV Rank</span>
+            <span className={clsx('text-[10px] font-bold font-mono', ivRankColor(item.iv_rank))}>
+              {item.iv_rank != null ? `${item.iv_rank}%` : 'N/A'}
+            </span>
+          </div>
+          <IvBar rank={item.iv_rank} />
+        </div>
+
+        <div className="w-16 text-right shrink-0">
+          <div className="text-[8px] text-slate-500">Pctile</div>
+          <div className={clsx('text-[10px] font-mono font-bold', ivRankColor(item.iv_rank))}>
+            {item.iv_percentile != null ? `${item.iv_percentile}%` : '—'}
+          </div>
+        </div>
+
+        <div className="w-32 text-right shrink-0">
+          {signal && (
+            <div className={clsx('flex items-center justify-end gap-1 text-[10px] font-semibold', signal.cls)}>
+              {signal.icon} {signal.text}
+            </div>
+          )}
+          {item.history_days > 0 && (
+            <div className="text-[8px] text-slate-500">{item.history_days}d Saxo history</div>
+          )}
+        </div>
+
+        <div className="text-slate-500">
+          {expanded ? <ChevronUp className="w-3.5 h-3.5" /> : <ChevronDown className="w-3.5 h-3.5" />}
+        </div>
+      </div>
+
+      {expanded && (
+        <div className="px-3 pb-3">
+          <SaxoTickerDetail ticker={item.ticker} />
+        </div>
+      )}
+    </div>
+  )
+}
+
 // ── Watchlist Manager ─────────────────────────────────────────────────────────
 function WatchlistManager() {
   const qc = useQueryClient()
@@ -364,9 +531,11 @@ function WatchlistManager() {
 // ── Main page ─────────────────────────────────────────────────────────────────
 export default function OptionsLab() {
   const { data, isLoading, refetch, isFetching } = useIvWatchlist()
+  const { data: saxoData, isLoading: saxoLoading, refetch: refetchSaxo, isFetching: saxoFetching } = useSaxoIvWatchlist()
   const [bootstrapping, setBootstrapping] = useState(false)
   const [bootstrapMsg, setBootstrapMsg] = useState<string | null>(null)
   const items: any[] = data?.items || []
+  const saxoItems: any[] = saxoData?.items || []
 
   const sellVol = items.filter(i => (i.iv_rank ?? 50) >= 80)
   const buyVol  = items.filter(i => i.iv_rank != null && i.iv_rank < 20)
@@ -505,6 +674,45 @@ export default function OptionsLab() {
         </div>
       )}
 
+      {/* ── Saxo section — deliberately separate from the yfinance watchlist above.
+          Symbols come from the Saxo watchlist (Config → Saxo), not from IV Watchlist
+          Manager below — everything here is computed purely from our own accumulated
+          saxo_option_snapshots, never blended with yfinance IV. ── */}
+      <div className="pt-2 border-t border-slate-700/40">
+        <div className="flex items-center justify-between mb-3">
+          <div>
+            <div className="text-sm font-bold text-white flex items-center gap-2">
+              <Link2 className="w-4 h-4 text-emerald-400" /> Saxo Broker Data
+            </div>
+            <p className="text-[10px] text-slate-500 mt-0.5">
+              IV Rank · Term Structure · Skew, computed only from your Saxo watchlist's accumulated option snapshots — manage the symbol list in Config → Saxo.
+            </p>
+          </div>
+          <button
+            onClick={() => refetchSaxo()}
+            disabled={saxoFetching}
+            className="flex items-center gap-1.5 text-xs border border-slate-600 text-slate-400 hover:text-slate-200 hover:border-slate-500 px-3 py-1.5 rounded transition-all disabled:opacity-50"
+          >
+            <RefreshCw className={clsx('w-3.5 h-3.5', saxoFetching && 'animate-spin')} />
+            {saxoFetching ? 'Loading...' : 'Refresh'}
+          </button>
+        </div>
+
+        {saxoLoading ? (
+          <div className="space-y-2">
+            {[...Array(2)].map((_, i) => <div key={i} className="h-12 animate-pulse bg-dark-700 rounded-lg" />)}
+          </div>
+        ) : saxoItems.length === 0 ? (
+          <div className="card text-center py-8 text-slate-500 text-xs">
+            No Saxo IV data yet — add symbols to the Saxo watchlist (Config → Saxo) and let a few snapshot cycles accumulate.
+          </div>
+        ) : (
+          <div className="space-y-1.5">
+            {saxoItems.map(item => <SaxoWatchlistRow key={item.ticker} item={item} />)}
+          </div>
+        )}
+      </div>
+
       <WatchlistManager />
     </div>
   )