"""
Instrument Models — graphe causal exhaustif par instrument.

Deux types de nœuds :
  - structural  : facteurs éditables manuellement (carry, taux réels, croissance…)
  - event_driven: pressions auto depuis causal_event_analyses, groupées par catégorie template
  - output      : nœud résultat (somme en pips)

Valeur d'un nœud structural  = override utilisateur × coefficient → pips
Valeur d'un nœud event_driven = sum(pips × decay) depuis les analyses actives
Output = sum(tous les nœuds en pips)
"""
import json
import math
from datetime import datetime, timedelta, date as date_type
from typing import Optional

# ── Helpers ────────────────────────────────────────────────────────────────────

def _decay(days: int, absorption: int, dtype: str) -> float:
    if days < 0: return 0.0
    if dtype == "step":   return 1.0 if days <= absorption else 0.0
    if dtype == "linear": return max(0.0, 1.0 - days / max(absorption, 1))
    lam = 3.0 / max(absorption, 1)
    return math.exp(-lam * days)


# ── Comprehensive node definitions ─────────────────────────────────────────────
# coefficient (structural only) : native_unit × coefficient = pips impact

INSTRUMENT_MODELS: dict[str, dict] = {

# ═══════════════════════════════════════════════════════════════════════════════
"EURUSD": {
  "name": "EUR/USD", "output_node": "eurusd",
  "description": "Taux de change Euro / Dollar — pair G10 la plus liquide au monde",
  "nodes": [
    # ── Monétaire ──────────────────────────────────────────────────────────────
    {"id":"rate_diff_ois_2y","label":"Spread OIS 2Y USD-EUR","type":"structural","category":"monetary","unit":"bps","coefficient":1.5,
     "description":"Différentiel taux swap OIS 2 ans USD vs EUR. Principal déterminant court terme. Positif = USD plus rémunérateur → EUR/USD ↓"},
    {"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":0.8,
     "description":"Variation cumulée taux Fed attendue sur 12m (négatif = cuts → USD ↓ → EUR/USD ↑)"},
    {"id":"ecb_path_12m","label":"Anticipation BCE 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-0.8,
     "description":"Variation cumulée taux BCE attendue sur 12m (négatif = cuts → EUR ↓ → EUR/USD ↓)"},
    {"id":"us_real_rate","label":"Taux réel US 10Y (TIPS)","type":"structural","category":"monetary","unit":"bps","coefficient":-0.5,
     "description":"Rendement TIPS 10Y. Hausse = USD attractif pour capitaux → EUR/USD ↓"},
    {"id":"eu_real_rate","label":"Taux réel EU 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":0.5,
     "description":"Taux réel zone euro 10Y. Hausse = EUR attractif → EUR/USD ↑"},
    {"id":"carry_attractiveness","label":"Attractivité carry EUR/USD","type":"structural","category":"monetary","unit":"score","coefficient":0.6,
     "description":"Score synthétique carry trade EUR vs USD (+= EUR avantageux, -=USD avantageux)"},
    # ── Macro ──────────────────────────────────────────────────────────────────
    {"id":"us_growth_advantage","label":"Avantage croissance US/EU","type":"structural","category":"macro","unit":"pts","coefficient":-15.0,
     "description":"Différentiel PIB US - EU (annualisé). US surperformance → USD fort → pair ↓"},
    {"id":"us_labor_market","label":"Marché emploi US","type":"structural","category":"macro","unit":"score","coefficient":-0.4,
     "description":"Score santé marché emploi US (NFP, chômage, salaires). Fort = USD ↑"},
    {"id":"eu_pmi_composite","label":"PMI composite Eurozone","type":"structural","category":"macro","unit":"pts","coefficient":0.3,
     "description":"PMI composite zone euro (>50 = expansion). Hausse = EUR ↑"},
    # ── Inflation ──────────────────────────────────────────────────────────────
    {"id":"us_cpi_yoy","label":"CPI US YoY","type":"structural","category":"inflation","unit":"%","coefficient":-0.8,
     "description":"Inflation américaine. Hausse surprise → Fed plus hawkish → USD ↑ → pair ↓"},
    {"id":"eu_cpi_yoy","label":"HICP Eurozone YoY","type":"structural","category":"inflation","unit":"%","coefficient":0.8,
     "description":"Inflation zone euro. Hausse surprise → BCE plus hawkish → EUR ↑"},
    # ── Géopolitique & Politique ────────────────────────────────────────────────
    {"id":"eu_fragmentation_risk","label":"Risque fragmentation UE","type":"structural","category":"political","unit":"score","coefficient":-0.5,
     "description":"Risque politique/fragmentation zone euro (0=stable, 100=crise). Hausse → EUR ↓"},
    {"id":"us_political_risk","label":"Incertitude politique US","type":"structural","category":"political","unit":"score","coefficient":0.3,
     "description":"Incertitude politique américaine (debt ceiling, shutdown, élections). Hausse → USD ↓"},
    {"id":"energy_price_impact","label":"Prix énergie (termes échanges EU)","type":"structural","category":"macro","unit":"$/bbl","coefficient":-0.25,
     "description":"Prix pétrole/gaz. Hausse élargit déficit commercial EU → EUR ↓ structurellement"},
    # ── Sentiment & Risque ─────────────────────────────────────────────────────
    {"id":"risk_appetite","label":"Appétit risque mondial","type":"structural","category":"sentiment","unit":"score","coefficient":0.5,
     "description":"Score risk-on/off global (+100=risk-on max). Risk-on → sorties USD → EUR/USD ↑"},
    {"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.4,
     "description":"VIX. Spike → flight to USD safe haven → EUR/USD ↓"},
    # ── Positionnement & Flux ─────────────────────────────────────────────────
    {"id":"cftc_eur_net","label":"Positions nettes EUR (CoT)","type":"structural","category":"positioning","unit":"k contrats","coefficient":0.1,
     "description":"Positions spéculatives nettes EUR sur CME (CoT). Long extrême → risque retournement."},
    {"id":"dollar_reserve_demand","label":"Demande réserves USD","type":"structural","category":"flows","unit":"score","coefficient":-0.4,
     "description":"Demande globale de réserves en USD (score). Fort = USD structurellement demandé → pair ↓"},
    # ── Event-driven (auto depuis causal_event_analyses) ────────────────────────
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips",
     "description":"Contributions cumulées des événements banques centrales actifs (décisions, minutes, guidance). Décroissance exp."},
    {"id":"ev_macro_surprise","label":"Surprise Données Macro","type":"event_driven","category":"monetary_shock","unit":"pips",
     "description":"Surprises publications économiques (CPI, NFP, PIB, PMI, ventes détail). Décroissance rapide ~12j."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips",
     "description":"Chocs géopolitiques actifs (conflits, sanctions, tensions). Décroissance linéaire ~21j."},
    {"id":"ev_trade_policy","label":"Choc Commercial / Tarifs","type":"event_driven","category":"trade_policy","unit":"pips",
     "description":"Annonces commerciales (tarifs, accords, menaces). Décroissance lente."},
    {"id":"ev_growth_shock","label":"Choc de Croissance","type":"event_driven","category":"growth_shock","unit":"pips",
     "description":"Chocs perspectives croissance (récession, rebond inattendu)."},
    {"id":"ev_commodity","label":"Choc Commodités","type":"event_driven","category":"commodity","unit":"pips",
     "description":"Chocs matières premières (pétrole, gaz, métaux) impactant USD ou EUR."},
    {"id":"ev_credit_stress","label":"Stress Crédit / Liquidité","type":"event_driven","category":"credit_stress","unit":"pips",
     "description":"Événements stress crédit/liquidité (banking stress, spreads IG/HY)."},
    {"id":"ev_sentiment","label":"Sentiment & Positionnement","type":"event_driven","category":"sentiment","unit":"pips",
     "description":"Changements sentiment et flux positionnement institutionnel."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips",
     "description":"Signaux techniques (cassures, croisements MA, niveaux clés)."},
    # ── Output ─────────────────────────────────────────────────────────────────
    {"id":"eurusd","label":"EUR/USD Impact Net","type":"output","category":"output","unit":"pips",
     "description":"Pression nette = Σ(structurels × coefficient) + Σ(event-driven en pips)"},
  ]
},

# ═══════════════════════════════════════════════════════════════════════════════
"USDJPY": {
  "name": "USD/JPY", "output_node": "usdjpy",
  "description": "Taux de change Dollar / Yen — pair carry & safe haven par excellence",
  "nodes": [
    {"id":"yield_diff_10y","label":"Différentiel rendement 10Y US-JP","type":"structural","category":"monetary","unit":"bps","coefficient":1.2,
     "description":"Spread rendement UST10Y - JGB10Y. Principal moteur USD/JPY. Hausse = USD/JPY ↑"},
    {"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":0.6,
     "description":"Variation cumulée taux Fed 12m. Hausse = USD ↑ → USD/JPY ↑"},
    {"id":"boj_policy_stance","label":"Biais BoJ (hawkish/dovish)","type":"structural","category":"monetary","unit":"score","coefficient":-8.0,
     "description":"Score posture BoJ (+= hawkish). Hawkish BoJ → JPY apprécie → USD/JPY ↓"},
    {"id":"jgb_yield_10y","label":"Rendement JGB 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":-0.8,
     "description":"Rendement JGB 10Y. Hausse = JPY attractif → USD/JPY ↓"},
    {"id":"us_real_rate","label":"Taux réel US 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":0.6,
     "description":"TIPS 10Y. Hausse = USD attractif vs actifs risqués → USD/JPY ↑"},
    {"id":"risk_appetite","label":"Appétit risque mondial","type":"structural","category":"sentiment","unit":"score","coefficient":-1.2,
     "description":"Score risk-on. Risk-off → fuite vers JPY safe haven → USD/JPY ↓"},
    {"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.8,
     "description":"VIX. Spike → demande JPY refuge → USD/JPY ↓"},
    {"id":"carry_trade_momentum","label":"Momentum carry USD/JPY","type":"structural","category":"positioning","unit":"score","coefficient":0.4,
     "description":"Force du carry trade. Positif = flux acheteurs USD/JPY (emprunter JPY, investir USD)."},
    {"id":"cftc_jpy_net_short","label":"Positions nettes JPY short (CoT)","type":"structural","category":"positioning","unit":"k contrats","coefficient":0.15,
     "description":"Positions short JPY spéculatifs CoT CFTC. Extrême = risque short squeeze → USD/JPY ↓ brutal."},
    {"id":"mof_intervention_risk","label":"Risque intervention MoF","type":"structural","category":"political","unit":"score","coefficient":-0.8,
     "description":"Probabilité intervention verbale/physique du Trésor japonais (0=aucune, 100=imminente). Hausse → USD/JPY ↓ préventif."},
    {"id":"japan_current_account","label":"Balance courante Japon","type":"structural","category":"flows","unit":"Mds¥","coefficient":-0.05,
     "description":"Excédent courant japonais. Large surplus = rapatriements YEN → USD/JPY ↓ structurel."},
    {"id":"us_japan_trade_tension","label":"Tensions commerciales US-Japon","type":"structural","category":"political","unit":"score","coefficient":0.3,
     "description":"Tensions bilatérales US-Japon (tarifs, pressions). Hausse → USD/JPY incertain."},
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"Fed, BoJ decisions/minutes actifs."},
    {"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"NFP, CPI US, Tankan, CPI Japon surprises."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Tensions NK, conflits régionaux → JPY safe haven."},
    {"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs US-Japon, accords commerciaux."},
    {"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Chocs PIB/récession → risk-off → JPY."},
    {"id":"ev_credit_stress","label":"Stress Crédit","type":"event_driven","category":"credit_stress","unit":"pips","description":"Stress bancaire → flight to JPY."},
    {"id":"ev_sentiment","label":"Sentiment & Flux","type":"event_driven","category":"sentiment","unit":"pips","description":"Positionnement spéculatif, flux risk-on/off."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Niveaux clés, tendances USD/JPY."},
    {"id":"usdjpy","label":"USD/JPY Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée USD/JPY"},
  ]
},

# ═══════════════════════════════════════════════════════════════════════════════
"XAUUSD": {
  "name": "XAU/USD (Or)", "output_node": "xauusd",
  "description": "Or vs Dollar — actif refuge, couverture inflation et géopolitique",
  "nodes": [
    {"id":"us_real_rate_10y","label":"Taux réel US 10Y (TIPS)","type":"structural","category":"monetary","unit":"bps","coefficient":-2.5,
     "description":"PLUS IMPORTANT pour l'or. Taux réel US négatif/en baisse → or ↑. Chaque -10bps ≈ +25 pips or."},
    {"id":"dxy_level","label":"Indice Dollar (DXY)","type":"structural","category":"monetary","unit":"pts","coefficient":-3.0,
     "description":"Niveau DXY. Or libellé en USD → DXY ↑ = or ↓ mécaniquement (corrélation ~-0.75)."},
    {"id":"inflation_breakeven_10y","label":"Breakeven inflation 10Y US","type":"structural","category":"inflation","unit":"bps","coefficient":1.5,
     "description":"Anticipations inflation 10Y US (TIPS). Hausse = valeur réelle USD ↓ → or demandé comme couverture."},
    {"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-1.0,
     "description":"Variation taux Fed attendue. Cuts attendus → taux réels ↓ → or ↑."},
    {"id":"global_uncertainty_score","label":"Indice incertitude mondiale","type":"structural","category":"sentiment","unit":"score","coefficient":0.3,
     "description":"Score incertitude géopolitique/économique globale. Hausse → demande refuge or."},
    {"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":0.4,
     "description":"VIX. Spike → or comme couverture risque extrême."},
    {"id":"central_bank_gold_buying","label":"Achats CB (or)","type":"structural","category":"flows","unit":"tonnes/mois","coefficient":2.0,
     "description":"Achats nets d'or par les banques centrales mondiales. Driver structurel majeur depuis 2022."},
    {"id":"etf_gold_flows","label":"Flux ETF or (GLD, IAU)","type":"structural","category":"flows","unit":"tonnes","coefficient":1.5,
     "description":"Flux net dans les ETF adossés à l'or. Entrées → demande physique → or ↑."},
    {"id":"cftc_gold_net_long","label":"Positions nettes or (CoT)","type":"structural","category":"positioning","unit":"k contrats","coefficient":0.15,
     "description":"Positions spéculatives nettes or COMEX. Extrême long → risque liquidation → or ↓."},
    {"id":"us_fiscal_deficit","label":"Déficit fiscal US (%PIB)","type":"structural","category":"macro","unit":"%","coefficient":0.5,
     "description":"Inquiétudes sur la trajectoire fiscale US → doutes sur USD → or refuge."},
    {"id":"gold_mine_cost","label":"Coût d'extraction (AISC)","type":"structural","category":"supply","unit":"$/oz","coefficient":0.05,
     "description":"All-in sustaining cost mines d'or. Support fondamental pour le prix."},
    {"id":"india_china_demand","label":"Demande physique Inde/Chine","type":"structural","category":"flows","unit":"score","coefficient":0.4,
     "description":"Demande physique joaillerie/investment Inde et Chine. Saisonnalité (Diwali, Nouvel An chinois)."},
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"Décisions Fed/BoE/BCE → impact taux réels → or."},
    {"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"CPI/PCE surprises, NFP → réévaluation taux réels → or."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Conflits, tensions → demande refuge or maximale."},
    {"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs/guerres commerciales → incertitude → or."},
    {"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Récession → easing monétaire attendu → or ↑."},
    {"id":"ev_credit_stress","label":"Stress Crédit / Systémique","type":"event_driven","category":"credit_stress","unit":"pips","description":"Crises bancaires, stress liquidité → or refuge absolu."},
    {"id":"ev_commodity","label":"Choc Commodités","type":"event_driven","category":"commodity","unit":"pips","description":"Chocs matières premières (inflation des inputs)."},
    {"id":"ev_sentiment","label":"Sentiment & Flux","type":"event_driven","category":"sentiment","unit":"pips","description":"Sentiment risk-off, flux vers or."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Cassures ATH, niveaux clés or."},
    {"id":"xauusd","label":"XAU/USD Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée sur or/USD"},
  ]
},

# ═══════════════════════════════════════════════════════════════════════════════
"SP500": {
  "name": "S&P 500", "output_node": "sp500",
  "description": "Indice actions US large cap — baromètre risque global",
  "nodes": [
    {"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-0.6,
     "description":"Cuts attendus → taux discount ↓ → PE expansion → SP500 ↑. Chaque -25bps ≈ +15 pts SP500."},
    {"id":"us_real_rate_10y","label":"Taux réel US 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":-1.5,
     "description":"Taux réel = taux d'actualisation des bénéfices futurs. Hausse → PE compression → SP500 ↓."},
    {"id":"eps_growth_12m","label":"Croissance BPA attendue 12m","type":"structural","category":"earnings","unit":"%","coefficient":8.0,
     "description":"Révisions croissance bénéfices S&P 500. +1% révision haussière ≈ +8 pts SP500."},
    {"id":"eps_revision_ratio","label":"Ratio révisions BPA","type":"structural","category":"earnings","unit":"ratio","coefficient":2.0,
     "description":"Ratio révisions haussières/baissières (>1 = majorité haussière). Fort driver momentum."},
    {"id":"pe_expansion","label":"Expansion multiple PE","type":"structural","category":"valuation","unit":"x","coefficient":12.0,
     "description":"Variation multiple PE du S&P 500. +1x PE ≈ +12 pts SP500 (base ~4500)."},
    {"id":"financial_conditions","label":"Indice conditions financières","type":"structural","category":"monetary","unit":"score","coefficient":1.5,
     "description":"Chicago Fed NFCI ou Goldman GSFCI. Desserrement = SP500 ↑."},
    {"id":"credit_spread_ig","label":"Spread crédit IG (bps)","type":"structural","category":"credit","unit":"bps","coefficient":-0.8,
     "description":"Spread obligations IG. Hausse = conditions crédit durcissent → SP500 ↓."},
    {"id":"credit_spread_hy","label":"Spread crédit HY (bps)","type":"structural","category":"credit","unit":"bps","coefficient":-0.5,
     "description":"Spread high yield. Baromètre stress financier. Spike → SP500 ↓."},
    {"id":"buyback_volume","label":"Volume rachats actions","type":"structural","category":"flows","unit":"Mds$/sem","coefficient":0.5,
     "description":"Flux de rachats d'actions S&P 500 en cours. Support technique majeur."},
    {"id":"retail_flow","label":"Flux retail (FOMO)","type":"structural","category":"flows","unit":"score","coefficient":0.3,
     "description":"Score flux acheteurs retail (ETF, options call). Momentum-driven."},
    {"id":"consumer_confidence","label":"Confiance consommateur US","type":"structural","category":"macro","unit":"pts","coefficient":0.3,
     "description":"Conference Board ou Michigan. Signal dépenses → bénéfices → SP500."},
    {"id":"us_gdp_growth","label":"Croissance PIB US","type":"structural","category":"macro","unit":"%","coefficient":5.0,
     "description":"Croissance réelle US annualisée. Surprise haussière → SP500 ↑."},
    {"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.8,
     "description":"VIX. Spike → risk-off → SP500 ↓. Normalisation VIX → SP500 ↑."},
    {"id":"geopolitical_risk_premium","label":"Prime risque géopolitique","type":"structural","category":"political","unit":"score","coefficient":-0.5,
     "description":"Tensions géopolitiques majeures → incertitude → prime de risque → SP500 ↓."},
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"Fed pivot, pause, hawkish surprise → SP500 directement."},
    {"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"NFP, CPI, PIB, ISM surprises."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Conflits, sanctions → risk-off SP500."},
    {"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs → chaînes supply, marges bénéficiaires SP500."},
    {"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Récession/reprise choc."},
    {"id":"ev_credit_stress","label":"Stress Crédit","type":"event_driven","category":"credit_stress","unit":"pips","description":"Banking stress → SP500 ↓ brutal."},
    {"id":"ev_commodity","label":"Choc Commodités","type":"event_driven","category":"commodity","unit":"pips","description":"Choc énergie → marges → SP500."},
    {"id":"ev_sentiment","label":"Sentiment & Positionnement","type":"event_driven","category":"sentiment","unit":"pips","description":"Flux institutionnels, CTA positioning."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Niveaux clés SP500, MA200, cassures."},
    {"id":"sp500","label":"S&P 500 Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée S&P 500"},
  ]
},

# ═══════════════════════════════════════════════════════════════════════════════
"TLT": {
  "name": "TLT (US Long Bonds)", "output_node": "tlt",
  "description": "ETF obligations US 20Y+ — duration longue, sensible aux taux et récession",
  "nodes": [
    {"id":"fed_terminal_rate","label":"Taux terminal Fed anticipé","type":"structural","category":"monetary","unit":"bps","coefficient":-0.4,
     "description":"Taux Fed terminal pricé par les marchés. Hausse → taux longs up → TLT ↓ (duration ~18)."},
    {"id":"us_10y_yield","label":"Rendement UST 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":-0.3,
     "description":"Rendement 10Y direct. Chaque +1bps ≈ -$0.18 sur $100 TLT (duration modifiée ~18)."},
    {"id":"us_30y_yield","label":"Rendement UST 30Y","type":"structural","category":"monetary","unit":"bps","coefficient":-0.25,
     "description":"Rendement 30Y. TLT détient principalement des 20-30Y."},
    {"id":"inflation_breakeven_10y","label":"Breakeven inflation 10Y","type":"structural","category":"inflation","unit":"bps","coefficient":-0.2,
     "description":"Anticipations inflation 10Y. Hausse → taux nominaux ↑ → TLT ↓."},
    {"id":"recession_probability","label":"Probabilité récession 12m","type":"structural","category":"macro","unit":"%","coefficient":0.3,
     "description":"Probabilité récession 12m (modèle yield curve ou Fed NY). Hausse → flight to safety → TLT ↑."},
    {"id":"fed_qt_pace","label":"Rythme QT Fed (Mds$/mois)","type":"structural","category":"monetary","unit":"Mds$","coefficient":-0.1,
     "description":"Réduction bilan Fed (quantitative tightening). Plus vite = pression vendeur sur Treasuries → TLT ↓."},
    {"id":"us_fiscal_deficit","label":"Déficit fiscal US (%PIB)","type":"structural","category":"macro","unit":"%","coefficient":-0.4,
     "description":"Déficit croissant → supply massive de Treasuries → pression vendeuse → TLT ↓."},
    {"id":"term_premium","label":"Prime de terme (ACM)","type":"structural","category":"monetary","unit":"bps","coefficient":-0.3,
     "description":"Prime de terme (Adrian-Crump-Moench). Hausse = extra rendement exigé → TLT ↓."},
    {"id":"foreign_treasury_demand","label":"Demande étrangère Treasuries","type":"structural","category":"flows","unit":"Mds$","coefficient":0.05,
     "description":"Achats Treasuries par banques centrales étrangères (Chine, Japon). Fort = TLT ↑."},
    {"id":"yield_curve_slope","label":"Pente courbe 2Y-10Y","type":"structural","category":"monetary","unit":"bps","coefficient":0.1,
     "description":"Steepening (2Y-10Y ↑) = TLT ↑ relatif (marché price fin de hausse Fed)."},
    {"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":0.5,
     "description":"VIX spike → flight to quality Treasuries → TLT ↑ (sauf si crise stagflationniste)."},
    {"id":"sp500_correlation","label":"Corrélation SP500 (inverse)","type":"structural","category":"sentiment","unit":"score","coefficient":-0.3,
     "description":"En régime normal : SP500 ↑ → TLT ↓ (rotation risque). Score + = corrélation positive anormale."},
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"FOMC décisions, minutes → impact direct TLT."},
    {"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"CPI, PCE, NFP → réévaluation taux → TLT."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Crises → flight to safety Treasuries."},
    {"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Récession → TLT ↑ massif."},
    {"id":"ev_credit_stress","label":"Stress Crédit","type":"event_driven","category":"credit_stress","unit":"pips","description":"Banking stress → fuite vers Treasuries."},
    {"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs → incertitude → Treasuries demandés ou vendus."},
    {"id":"ev_sentiment","label":"Sentiment & Flux","type":"event_driven","category":"sentiment","unit":"pips","description":"Flux institutionnels bonds."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Niveaux clés TLT, moyennes mobiles."},
    {"id":"tlt","label":"TLT Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée TLT"},
  ]
},

# ═══════════════════════════════════════════════════════════════════════════════
"GBPUSD": {
  "name": "GBP/USD", "output_node": "gbpusd",
  "description": "Taux de change Livre sterling / Dollar — sensible aux données UK et BoE",
  "nodes": [
    {"id":"boe_path_12m","label":"Anticipation BoE 12m","type":"structural","category":"monetary","unit":"bps","coefficient":0.7,
     "description":"Variation cumulée taux BoE attendue 12m. Hikes → GBP ↑ → GBP/USD ↑."},
    {"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-0.7,
     "description":"Variation cumulée taux Fed attendue 12m. Hikes → USD ↑ → GBP/USD ↓."},
    {"id":"uk_us_rate_differential","label":"Différentiel taux BoE-Fed","type":"structural","category":"monetary","unit":"bps","coefficient":1.0,
     "description":"Spread taux directeurs BoE vs Fed. Principal driver EUR/USD."},
    {"id":"uk_cpi_yoy","label":"CPI UK YoY","type":"structural","category":"inflation","unit":"%","coefficient":0.7,
     "description":"Inflation UK. Persistance → BoE forcé de rester hawkish → GBP ↑."},
    {"id":"uk_gdp_growth","label":"Croissance PIB UK","type":"structural","category":"macro","unit":"%","coefficient":3.0,
     "description":"PIB UK annualisé. Surprise haussière → GBP ↑."},
    {"id":"uk_pmi_composite","label":"PMI composite UK","type":"structural","category":"macro","unit":"pts","coefficient":0.25,
     "description":"PMI composite UK. >50 = expansion → GBP ↑."},
    {"id":"uk_labor_market","label":"Marché emploi UK","type":"structural","category":"macro","unit":"score","coefficient":0.4,
     "description":"Score santé emploi UK (chômage, salaires, emplois). Fort = BoE hawkish → GBP ↑."},
    {"id":"uk_political_risk","label":"Risque politique UK","type":"structural","category":"political","unit":"score","coefficient":-0.4,
     "description":"Incertitude politique UK (snap election, budget, Brexit séquelles). Hausse → GBP ↓."},
    {"id":"uk_current_account","label":"Balance courante UK (%PIB)","type":"structural","category":"flows","unit":"%","coefficient":0.3,
     "description":"Déficit courant UK chronique. Amélioration → moins de pression vendeuse GBP."},
    {"id":"risk_appetite","label":"Appétit risque mondial","type":"structural","category":"sentiment","unit":"score","coefficient":0.4,
     "description":"Risk-on → GBP comme devise cyclique/risquée apprécie vs USD."},
    {"id":"cftc_gbp_net","label":"Positions nettes GBP (CoT)","type":"structural","category":"positioning","unit":"k contrats","coefficient":0.08,
     "description":"Positions spéculatives nettes GBP CME CoT."},
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"BoE, Fed décisions/minutes."},
    {"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"CPI UK/US, NFP, GDP UK surprises."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Conflits → risk-off → GBP vendu."},
    {"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs US → UK exposé."},
    {"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Récession UK/US."},
    {"id":"ev_credit_stress","label":"Stress Crédit","type":"event_driven","category":"credit_stress","unit":"pips","description":"Stress Gilts, banking UK."},
    {"id":"ev_sentiment","label":"Sentiment & Flux","type":"event_driven","category":"sentiment","unit":"pips","description":"Positionnement GBP."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Niveaux clés cable."},
    {"id":"gbpusd","label":"GBP/USD Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée GBP/USD"},
  ]
},

# ═══════════════════════════════════════════════════════════════════════════════
"EEM": {
  "name": "EEM (Marchés Émergents)", "output_node": "eem",
  "description": "ETF actions marchés émergents — sensible au dollar, Chine et commodités",
  "nodes": [
    {"id":"dxy_inverse","label":"Indice Dollar (DXY) — impact inverse","type":"structural","category":"monetary","unit":"pts","coefficient":-0.8,
     "description":"USD fort → pression sur dettes EM en USD, sorties capitaux EM → EEM ↓. DXY ↑ = EEM ↓."},
    {"id":"us_real_rate","label":"Taux réel US 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":-0.6,
     "description":"Taux réel US élevé → capitaux retournent aux US → sorties EM → EEM ↓."},
    {"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-0.5,
     "description":"Cuts Fed → dollar faible + taux attractifs EM → capitaux entrent EM → EEM ↑."},
    {"id":"china_pmi","label":"PMI manufacturier Chine","type":"structural","category":"macro","unit":"pts","coefficient":0.6,
     "description":"PMI Caixin/NBS Chine. Expansion = croissance EM → EEM ↑ (Chine ~28% de l'indice)."},
    {"id":"china_growth_stimulus","label":"Stimulus croissance Chine","type":"structural","category":"macro","unit":"score","coefficient":0.8,
     "description":"Score stimulus PBOC/gouvernement. Annonces majeures → EEM spike haussier."},
    {"id":"commodity_complex","label":"Indice commodités (export EM)","type":"structural","category":"commodity","unit":"score","coefficient":0.4,
     "description":"Commodités elevés → exportateurs EM (Brésil, Russie, Afrique du Sud) profitent → EEM ↑."},
    {"id":"em_credit_spread","label":"Spread crédit souverain EM","type":"structural","category":"credit","unit":"bps","coefficient":-0.5,
     "description":"EMBI spread. Hausse = stress EM → sorties → EEM ↓."},
    {"id":"em_capital_flows","label":"Flux capitaux EM nets","type":"structural","category":"flows","unit":"Mds$","coefficient":0.3,
     "description":"IIF flux nets vers EM. Entrées soutenues = EEM ↑ structurel."},
    {"id":"risk_appetite","label":"Appétit risque mondial","type":"structural","category":"sentiment","unit":"score","coefficient":0.6,
     "description":"Risk-on → recherche de rendement EM → EEM ↑."},
    {"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.5,
     "description":"VIX spike → sorties EM (flight to quality) → EEM ↓."},
    {"id":"us_china_tension","label":"Tensions US-Chine","type":"structural","category":"political","unit":"score","coefficient":-0.5,
     "description":"Tensions US-Chine (tarifs, sanctions, tech). Hausse → risque EM → EEM ↓."},
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"Fed pivot, PBOC mesures."},
    {"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"Données Chine, US macro."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Tensions régionales EM."},
    {"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs US-Chine, sanctions."},
    {"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Récession US/Chine → EEM ↓."},
    {"id":"ev_commodity","label":"Choc Commodités","type":"event_driven","category":"commodity","unit":"pips","description":"Chocs matières premières → exportateurs EM."},
    {"id":"ev_credit_stress","label":"Stress Crédit EM","type":"event_driven","category":"credit_stress","unit":"pips","description":"Stress souverain EM, crise devises EM."},
    {"id":"ev_sentiment","label":"Sentiment & Flux","type":"event_driven","category":"sentiment","unit":"pips","description":"Flux ETF EM, risk-on/off."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Niveaux EEM, tendances."},
    {"id":"eem","label":"EEM Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée EEM"},
  ]
},

# ═══════════════════════════════════════════════════════════════════════════════
"QQQ": {
  "name": "QQQ (NASDAQ-100 Tech)", "output_node": "qqq",
  "description": "ETF NASDAQ-100 — tech et croissance US, sensible aux taux réels et bénéfices tech",
  "nodes": [
    {"id":"us_real_rate_10y","label":"Taux réel US 10Y (duration tech)","type":"structural","category":"monetary","unit":"bps","coefficient":-1.5,
     "description":"PRINCIPAL driver : tech = duration longue (flux futurs). Taux réels ↑ → actualisation ↑ → PE tech ↓ → QQQ ↓."},
    {"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-0.8,
     "description":"Cuts attendus → taux discount ↓ → PE tech expansion → QQQ ↑."},
    {"id":"big_tech_eps_revision","label":"Révisions BPA Big Tech (M7)","type":"structural","category":"earnings","unit":"%","coefficient":15.0,
     "description":"Révisions bénéfices Magnificent 7 (AAPL, MSFT, NVDA, GOOGL, AMZN, META, TSLA). +1% ≈ +15 pts QQQ."},
    {"id":"ai_capex_cycle","label":"Cycle capex IA","type":"structural","category":"tech","unit":"score","coefficient":0.8,
     "description":"Score momentum investissements IA (Hyperscalers capex, NVDA demande GPU). Positif = QQQ ↑."},
    {"id":"semiconductor_cycle","label":"Cycle semi-conducteurs","type":"structural","category":"tech","unit":"score","coefficient":0.6,
     "description":"Phase cycle semis (book-to-bill, inventaires, commandes). Upcycle = QQQ ↑."},
    {"id":"tech_pe_multiple","label":"Multiple PE tech (NTM)","type":"structural","category":"valuation","unit":"x","coefficient":12.0,
     "description":"PE forward NASDAQ-100. Expansion = QQQ ↑. Actuellement élevé → sensible aux déceptions."},
    {"id":"regulation_risk_tech","label":"Risque réglementaire tech","type":"structural","category":"political","unit":"score","coefficient":-0.5,
     "description":"Risque antitrust, régulation IA, vie privée (EU AI Act, FTC actions). Hausse → QQQ ↓."},
    {"id":"us_consumer_spending","label":"Dépenses consommateur US","type":"structural","category":"macro","unit":"score","coefficient":0.4,
     "description":"Dépenses techno consommateur (iPhone, cloud, publicité). Solide = revenus tech → QQQ ↑."},
    {"id":"cloud_growth_enterprise","label":"Croissance cloud enterprise","type":"structural","category":"tech","unit":"%","coefficient":0.6,
     "description":"Croissance AWS/Azure/GCP. Moteur marges operating → QQQ."},
    {"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.8,
     "description":"VIX. Spike → vente tech en premier (beta élevé) → QQQ ↓ plus que SP500."},
    {"id":"retail_options_flow","label":"Flux options retail (call buying)","type":"structural","category":"flows","unit":"score","coefficient":0.4,
     "description":"Momentum gamma/delta flows retail sur options tech. FOMO = QQQ squeeze haussier."},
    {"id":"china_tech_risk","label":"Risque restrictions tech US-Chine","type":"structural","category":"political","unit":"score","coefficient":-0.4,
     "description":"Restrictions export chips, sanctions tech US-Chine. Hausse → revenus tech ↓ → QQQ ↓."},
    {"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"Fed pivot → QQQ amplificateur."},
    {"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"CPI, NFP → réévaluation Fed → QQQ."},
    {"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Tensions → risk-off → sell tech."},
    {"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs tech, restrictions exports."},
    {"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Récession → dépenses IT coupées → QQQ."},
    {"id":"ev_credit_stress","label":"Stress Crédit","type":"event_driven","category":"credit_stress","unit":"pips","description":"Conditions financières → tech financement."},
    {"id":"ev_commodity","label":"Choc Commodités","type":"event_driven","category":"commodity","unit":"pips","description":"Énergie → data centers coûts."},
    {"id":"ev_sentiment","label":"Sentiment & Positionnement","type":"event_driven","category":"sentiment","unit":"pips","description":"Flux CTAs, hedge funds tech."},
    {"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Niveaux QQQ, MA200, RSI."},
    {"id":"qqq","label":"QQQ Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée QQQ"},
  ]
},

}  # end INSTRUMENT_MODELS


# ── Category labels (French) ────────────────────────────────────────────────────
CAT_LABELS: dict[str, str] = {
    "monetary":    "Monétaire",
    "inflation":   "Inflation",
    "macro":       "Macro / Croissance",
    "political":   "Politique",
    "flows":       "Flux & Réserves",
    "positioning": "Positionnement",
    "sentiment":   "Sentiment & Risque",
    "credit":      "Crédit",
    "earnings":    "Bénéfices",
    "valuation":   "Valorisation",
    "tech":        "Technologie",
    "commodity":   "Commodités",
    "supply":      "Offre",
    "output":      "Résultat",
    # event-driven categories (from causal_graph_templates.category)
    "central_bank":   "Banques Centrales",
    "monetary_shock": "Surprise Macro",
    "geopolitical":   "Géopolitique",
    "trade_policy":   "Commerce / Tarifs",
    "growth_shock":   "Choc Croissance",
    "commodity":      "Commodités",
    "credit_stress":  "Stress Crédit",
    "sentiment":      "Sentiment",
    "technical":      "Technique",
    "positioning":    "Positionnement",
    "unclassified":   "Non Classifié",
}


# ── DB init ─────────────────────────────────────────────────────────────────────

def init_instrument_model_tables(conn):
    conn.executescript("""
        CREATE TABLE IF NOT EXISTS instrument_models (
            id          INTEGER PRIMARY KEY,
            instrument  TEXT UNIQUE NOT NULL,
            graph_json  TEXT NOT NULL,
            updated_at  TEXT DEFAULT (datetime('now'))
        );
        CREATE TABLE IF NOT EXISTS instrument_node_overrides (
            id          INTEGER PRIMARY KEY,
            instrument  TEXT NOT NULL,
            node_id     TEXT NOT NULL,
            value       REAL NOT NULL,
            note        TEXT,
            set_at      TEXT DEFAULT (datetime('now')),
            UNIQUE(instrument, node_id)
        );
    """)
    conn.commit()


def seed_instrument_models(conn):
    """Insert/update instrument models on startup. Never overwrites user overrides."""
    init_instrument_model_tables(conn)
    for inst, model in INSTRUMENT_MODELS.items():
        existing = conn.execute(
            "SELECT id FROM instrument_models WHERE instrument=?", (inst,)
        ).fetchone()
        graph_json = json.dumps({
            "name": model["name"],
            "description": model["description"],
            "output_node": model["output_node"],
            "nodes": model["nodes"],
        })
        if existing:
            conn.execute(
                "UPDATE instrument_models SET graph_json=?, updated_at=datetime('now') WHERE instrument=?",
                (graph_json, inst)
            )
        else:
            conn.execute(
                "INSERT INTO instrument_models (instrument, graph_json) VALUES (?,?)",
                (inst, graph_json)
            )
    conn.commit()


# ── Value computation ───────────────────────────────────────────────────────────

def _compute_event_driven_values(conn, instrument: str, ref_date: date_type) -> dict[str, float]:
    """
    Pour chaque catégorie event-driven : somme des pips × decay sur toutes les analyses actives.
    Retourne {category: float} en pips.
    """
    extended_from = ref_date - timedelta(days=180)
    inst_upper = instrument.upper()

    rows = conn.execute("""
        SELECT a.prediction_json,
               e.start_date, e.end_date AS event_end_date, e.sub_type,
               t.category,
               t.calibration_json
        FROM   causal_event_analyses a
        JOIN   market_events          e ON e.id = a.market_event_id
        JOIN   causal_graph_templates t ON t.id = a.template_id
        WHERE  a.instrument = ?
          AND  e.start_date >= ?
          AND  e.start_date <= ?
    """, (inst_upper, str(extended_from), str(ref_date))).fetchall()

    by_cat: dict[str, float] = {}
    inst_lower = inst_upper.lower()

    for row in rows:
        r = dict(row)
        try:
            predictions = json.loads(r["prediction_json"] or "{}")
            calib       = json.loads(r["calibration_json"] or "{}")
        except Exception:
            continue

        pips: Optional[float] = None
        if inst_lower in predictions:
            pips = float(predictions[inst_lower])
        else:
            for k, v in predictions.items():
                if inst_lower in k.lower():
                    try: pips = float(v); break
                    except (TypeError, ValueError): pass

        if pips is None or pips == 0:
            continue

        absorption = max(1, int(calib.get("absorption_days", 7)))
        dtype      = str(calib.get("decay_type", "exp"))

        # Guidance events: dynamic absorption until meeting date
        ev_end = r.get("event_end_date")
        if ev_end and str(r.get("sub_type", "")).startswith("rate_guidance"):
            try:
                meeting = date_type.fromisoformat(ev_end[:10])
                ev_start = date_type.fromisoformat(r["start_date"][:10])
                absorption = max(1, (meeting - ev_start).days)
                dtype = "linear"
            except ValueError:
                pass

        try:
            ev_date = date_type.fromisoformat(r["start_date"][:10])
        except ValueError:
            continue

        days_elapsed = (ref_date - ev_date).days
        df = _decay(days_elapsed, absorption, dtype)
        if df < 0.01:
            continue

        cat = r["category"]
        by_cat[cat] = by_cat.get(cat, 0.0) + round(pips * df, 2)

    return by_cat


def get_model_state(conn, instrument: str, at_date: Optional[str] = None) -> Optional[dict]:
    """
    Retourne le graphe instrument avec les valeurs courantes de chaque nœud.
    """
    inst_upper = instrument.upper()
    row = conn.execute(
        "SELECT graph_json FROM instrument_models WHERE instrument=?", (inst_upper,)
    ).fetchone()
    if not row:
        return None

    graph = json.loads(row["graph_json"])

    try:
        ref_date = date_type.fromisoformat(at_date) if at_date else datetime.utcnow().date()
    except ValueError:
        ref_date = datetime.utcnow().date()

    # Load user overrides
    override_rows = conn.execute(
        "SELECT node_id, value, note, set_at FROM instrument_node_overrides WHERE instrument=?",
        (inst_upper,)
    ).fetchall()
    overrides: dict[str, dict] = {r["node_id"]: dict(r) for r in override_rows}

    # Compute event-driven values by category
    ev_by_cat = _compute_event_driven_values(conn, inst_upper, ref_date)

    # Build node states
    net_pips = 0.0
    nodes_out = []

    for node in graph["nodes"]:
        nid   = node["id"]
        ntype = node["type"]
        coeff = node.get("coefficient", 1.0)
        cat   = node["category"]
        state = dict(node)

        if ntype == "structural":
            ov = overrides.get(nid)
            if ov:
                state["current_value"] = ov["value"]
                state["pip_contribution"] = round(ov["value"] * coeff, 1)
                state["source"] = "manual"
                state["override_note"] = ov.get("note", "")
                state["override_set_at"] = ov.get("set_at", "")
            else:
                state["current_value"] = 0.0
                state["pip_contribution"] = 0.0
                state["source"] = "neutral"
            net_pips += state["pip_contribution"]

        elif ntype == "event_driven":
            pips = ev_by_cat.get(cat, 0.0)
            # manual override possible too
            ov = overrides.get(nid)
            if ov:
                pips = ov["value"]
                state["source"] = "manual"
                state["override_note"] = ov.get("note", "")
            else:
                state["source"] = "events" if pips != 0 else "neutral"
            state["current_value"] = round(pips, 1)
            state["pip_contribution"] = round(pips, 1)
            net_pips += pips

        elif ntype == "output":
            state["current_value"] = None  # filled after loop
            state["pip_contribution"] = None
            state["source"] = "computed"

        nodes_out.append(state)

    net_pips = round(net_pips, 1)

    # Fill output node
    for n in nodes_out:
        if n["type"] == "output":
            n["current_value"]    = net_pips
            n["pip_contribution"] = net_pips

    direction = "bullish" if net_pips > 5 else "bearish" if net_pips < -5 else "neutral"

    return {
        "instrument": inst_upper,
        "name":       graph["name"],
        "description": graph.get("description", ""),
        "at_date":    str(ref_date),
        "net_pips":   net_pips,
        "direction":  direction,
        "nodes":      nodes_out,
        "output_node": graph["output_node"],
    }


def set_node_override(conn, instrument: str, node_id: str, value: float, note: str = "") -> bool:
    inst_upper = instrument.upper()
    conn.execute("""
        INSERT INTO instrument_node_overrides (instrument, node_id, value, note, set_at)
        VALUES (?,?,?,?,datetime('now'))
        ON CONFLICT(instrument, node_id) DO UPDATE SET value=excluded.value, note=excluded.note, set_at=datetime('now')
    """, (inst_upper, node_id, value, note))
    conn.commit()
    return True


def clear_node_override(conn, instrument: str, node_id: str) -> bool:
    inst_upper = instrument.upper()
    conn.execute(
        "DELETE FROM instrument_node_overrides WHERE instrument=? AND node_id=?",
        (inst_upper, node_id)
    )
    conn.commit()
    return True