feat: cockpit

2026-07-21 16:48:12 +02:00
parent 24e5e4b03b
commit b6e9b96dc4
10 changed files with 644 additions and 142 deletions
--- a/backend/routers/saxo.py
+++ b/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)
--- a/backend/services/ai_analyzer.py
+++ b/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": [],
        }

--- a/backend/services/data_fetcher.py
+++ b/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
--- a/backend/services/database.py
+++ b/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
--- a/backend/services/iv_engine.py
+++ b/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}")
--- a/backend/services/market_event_detector.py
+++ b/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",
 }


--- a/backend/services/saxo_iv_engine.py
+++ b/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)}