670 lines
53 KiB
Python
670 lines
53 KiB
Python
"""
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Instrument Models — graphe causal exhaustif par instrument.
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Deux types de nœuds :
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- structural : facteurs éditables manuellement (carry, taux réels, croissance…)
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- event_driven: pressions auto depuis causal_event_analyses, groupées par catégorie template
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- output : nœud résultat (somme en pips)
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Valeur d'un nœud structural = override utilisateur × coefficient → pips
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Valeur d'un nœud event_driven = sum(pips × decay) depuis les analyses actives
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Output = sum(tous les nœuds en pips)
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"""
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import json
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import math
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from datetime import datetime, timedelta, date as date_type
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from typing import Optional
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# ── Helpers ────────────────────────────────────────────────────────────────────
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def _decay(days: int, absorption: int, dtype: str) -> float:
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if days < 0: return 0.0
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if dtype == "step": return 1.0 if days <= absorption else 0.0
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if dtype == "linear": return max(0.0, 1.0 - days / max(absorption, 1))
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lam = 3.0 / max(absorption, 1)
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return math.exp(-lam * days)
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# ── Comprehensive node definitions ─────────────────────────────────────────────
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# coefficient (structural only) : native_unit × coefficient = pips impact
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INSTRUMENT_MODELS: dict[str, dict] = {
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# ═══════════════════════════════════════════════════════════════════════════════
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"EURUSD": {
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"name": "EUR/USD", "output_node": "eurusd",
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"description": "Taux de change Euro / Dollar — pair G10 la plus liquide au monde",
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"nodes": [
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# ── Monétaire ──────────────────────────────────────────────────────────────
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{"id":"rate_diff_ois_2y","label":"Spread OIS 2Y USD-EUR","type":"structural","category":"monetary","unit":"bps","coefficient":1.5,
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"description":"Différentiel taux swap OIS 2 ans USD vs EUR. Principal déterminant court terme. Positif = USD plus rémunérateur → EUR/USD ↓"},
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{"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":0.8,
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"description":"Variation cumulée taux Fed attendue sur 12m (négatif = cuts → USD ↓ → EUR/USD ↑)"},
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{"id":"ecb_path_12m","label":"Anticipation BCE 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-0.8,
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"description":"Variation cumulée taux BCE attendue sur 12m (négatif = cuts → EUR ↓ → EUR/USD ↓)"},
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{"id":"us_real_rate","label":"Taux réel US 10Y (TIPS)","type":"structural","category":"monetary","unit":"bps","coefficient":-0.5,
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"description":"Rendement TIPS 10Y. Hausse = USD attractif pour capitaux → EUR/USD ↓"},
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{"id":"eu_real_rate","label":"Taux réel EU 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":0.5,
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"description":"Taux réel zone euro 10Y. Hausse = EUR attractif → EUR/USD ↑"},
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{"id":"carry_attractiveness","label":"Attractivité carry EUR/USD","type":"structural","category":"monetary","unit":"score","coefficient":0.6,
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"description":"Score synthétique carry trade EUR vs USD (+= EUR avantageux, -=USD avantageux)"},
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# ── Macro ──────────────────────────────────────────────────────────────────
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{"id":"us_growth_advantage","label":"Avantage croissance US/EU","type":"structural","category":"macro","unit":"pts","coefficient":-15.0,
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"description":"Différentiel PIB US - EU (annualisé). US surperformance → USD fort → pair ↓"},
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{"id":"us_labor_market","label":"Marché emploi US","type":"structural","category":"macro","unit":"score","coefficient":-0.4,
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"description":"Score santé marché emploi US (NFP, chômage, salaires). Fort = USD ↑"},
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{"id":"eu_pmi_composite","label":"PMI composite Eurozone","type":"structural","category":"macro","unit":"pts","coefficient":0.3,
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"description":"PMI composite zone euro (>50 = expansion). Hausse = EUR ↑"},
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# ── Inflation ──────────────────────────────────────────────────────────────
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{"id":"us_cpi_yoy","label":"CPI US YoY","type":"structural","category":"inflation","unit":"%","coefficient":-0.8,
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"description":"Inflation américaine. Hausse surprise → Fed plus hawkish → USD ↑ → pair ↓"},
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{"id":"eu_cpi_yoy","label":"HICP Eurozone YoY","type":"structural","category":"inflation","unit":"%","coefficient":0.8,
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"description":"Inflation zone euro. Hausse surprise → BCE plus hawkish → EUR ↑"},
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# ── Géopolitique & Politique ────────────────────────────────────────────────
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{"id":"eu_fragmentation_risk","label":"Risque fragmentation UE","type":"structural","category":"political","unit":"score","coefficient":-0.5,
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"description":"Risque politique/fragmentation zone euro (0=stable, 100=crise). Hausse → EUR ↓"},
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{"id":"us_political_risk","label":"Incertitude politique US","type":"structural","category":"political","unit":"score","coefficient":0.3,
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"description":"Incertitude politique américaine (debt ceiling, shutdown, élections). Hausse → USD ↓"},
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{"id":"energy_price_impact","label":"Prix énergie (termes échanges EU)","type":"structural","category":"macro","unit":"$/bbl","coefficient":-0.25,
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"description":"Prix pétrole/gaz. Hausse élargit déficit commercial EU → EUR ↓ structurellement"},
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# ── Sentiment & Risque ─────────────────────────────────────────────────────
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{"id":"risk_appetite","label":"Appétit risque mondial","type":"structural","category":"sentiment","unit":"score","coefficient":0.5,
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"description":"Score risk-on/off global (+100=risk-on max). Risk-on → sorties USD → EUR/USD ↑"},
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{"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.4,
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"description":"VIX. Spike → flight to USD safe haven → EUR/USD ↓"},
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# ── Positionnement & Flux ─────────────────────────────────────────────────
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{"id":"cftc_eur_net","label":"Positions nettes EUR (CoT)","type":"structural","category":"positioning","unit":"k contrats","coefficient":0.1,
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"description":"Positions spéculatives nettes EUR sur CME (CoT). Long extrême → risque retournement."},
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{"id":"dollar_reserve_demand","label":"Demande réserves USD","type":"structural","category":"flows","unit":"score","coefficient":-0.4,
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"description":"Demande globale de réserves en USD (score). Fort = USD structurellement demandé → pair ↓"},
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# ── Event-driven (auto depuis causal_event_analyses) ────────────────────────
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{"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips",
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"description":"Contributions cumulées des événements banques centrales actifs (décisions, minutes, guidance). Décroissance exp."},
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{"id":"ev_macro_surprise","label":"Surprise Données Macro","type":"event_driven","category":"monetary_shock","unit":"pips",
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"description":"Surprises publications économiques (CPI, NFP, PIB, PMI, ventes détail). Décroissance rapide ~12j."},
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{"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips",
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"description":"Chocs géopolitiques actifs (conflits, sanctions, tensions). Décroissance linéaire ~21j."},
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{"id":"ev_trade_policy","label":"Choc Commercial / Tarifs","type":"event_driven","category":"trade_policy","unit":"pips",
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"description":"Annonces commerciales (tarifs, accords, menaces). Décroissance lente."},
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{"id":"ev_growth_shock","label":"Choc de Croissance","type":"event_driven","category":"growth_shock","unit":"pips",
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"description":"Chocs perspectives croissance (récession, rebond inattendu)."},
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{"id":"ev_commodity","label":"Choc Commodités","type":"event_driven","category":"commodity","unit":"pips",
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"description":"Chocs matières premières (pétrole, gaz, métaux) impactant USD ou EUR."},
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{"id":"ev_credit_stress","label":"Stress Crédit / Liquidité","type":"event_driven","category":"credit_stress","unit":"pips",
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"description":"Événements stress crédit/liquidité (banking stress, spreads IG/HY)."},
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{"id":"ev_sentiment","label":"Sentiment & Positionnement","type":"event_driven","category":"sentiment","unit":"pips",
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"description":"Changements sentiment et flux positionnement institutionnel."},
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{"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips",
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"description":"Signaux techniques (cassures, croisements MA, niveaux clés)."},
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# ── Output ─────────────────────────────────────────────────────────────────
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{"id":"eurusd","label":"EUR/USD Impact Net","type":"output","category":"output","unit":"pips",
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"description":"Pression nette = Σ(structurels × coefficient) + Σ(event-driven en pips)"},
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]
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},
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# ═══════════════════════════════════════════════════════════════════════════════
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"USDJPY": {
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"name": "USD/JPY", "output_node": "usdjpy",
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"description": "Taux de change Dollar / Yen — pair carry & safe haven par excellence",
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"nodes": [
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{"id":"yield_diff_10y","label":"Différentiel rendement 10Y US-JP","type":"structural","category":"monetary","unit":"bps","coefficient":1.2,
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"description":"Spread rendement UST10Y - JGB10Y. Principal moteur USD/JPY. Hausse = USD/JPY ↑"},
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{"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":0.6,
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"description":"Variation cumulée taux Fed 12m. Hausse = USD ↑ → USD/JPY ↑"},
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{"id":"boj_policy_stance","label":"Biais BoJ (hawkish/dovish)","type":"structural","category":"monetary","unit":"score","coefficient":-8.0,
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"description":"Score posture BoJ (+= hawkish). Hawkish BoJ → JPY apprécie → USD/JPY ↓"},
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{"id":"jgb_yield_10y","label":"Rendement JGB 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":-0.8,
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"description":"Rendement JGB 10Y. Hausse = JPY attractif → USD/JPY ↓"},
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{"id":"us_real_rate","label":"Taux réel US 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":0.6,
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"description":"TIPS 10Y. Hausse = USD attractif vs actifs risqués → USD/JPY ↑"},
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{"id":"risk_appetite","label":"Appétit risque mondial","type":"structural","category":"sentiment","unit":"score","coefficient":-1.2,
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"description":"Score risk-on. Risk-off → fuite vers JPY safe haven → USD/JPY ↓"},
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{"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.8,
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"description":"VIX. Spike → demande JPY refuge → USD/JPY ↓"},
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{"id":"carry_trade_momentum","label":"Momentum carry USD/JPY","type":"structural","category":"positioning","unit":"score","coefficient":0.4,
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"description":"Force du carry trade. Positif = flux acheteurs USD/JPY (emprunter JPY, investir USD)."},
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{"id":"cftc_jpy_net_short","label":"Positions nettes JPY short (CoT)","type":"structural","category":"positioning","unit":"k contrats","coefficient":0.15,
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"description":"Positions short JPY spéculatifs CoT CFTC. Extrême = risque short squeeze → USD/JPY ↓ brutal."},
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{"id":"mof_intervention_risk","label":"Risque intervention MoF","type":"structural","category":"political","unit":"score","coefficient":-0.8,
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"description":"Probabilité intervention verbale/physique du Trésor japonais (0=aucune, 100=imminente). Hausse → USD/JPY ↓ préventif."},
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{"id":"japan_current_account","label":"Balance courante Japon","type":"structural","category":"flows","unit":"Mds¥","coefficient":-0.05,
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"description":"Excédent courant japonais. Large surplus = rapatriements YEN → USD/JPY ↓ structurel."},
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{"id":"us_japan_trade_tension","label":"Tensions commerciales US-Japon","type":"structural","category":"political","unit":"score","coefficient":0.3,
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"description":"Tensions bilatérales US-Japon (tarifs, pressions). Hausse → USD/JPY incertain."},
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{"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"Fed, BoJ decisions/minutes actifs."},
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{"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"NFP, CPI US, Tankan, CPI Japon surprises."},
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{"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Tensions NK, conflits régionaux → JPY safe haven."},
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{"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs US-Japon, accords commerciaux."},
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{"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Chocs PIB/récession → risk-off → JPY."},
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{"id":"ev_credit_stress","label":"Stress Crédit","type":"event_driven","category":"credit_stress","unit":"pips","description":"Stress bancaire → flight to JPY."},
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{"id":"ev_sentiment","label":"Sentiment & Flux","type":"event_driven","category":"sentiment","unit":"pips","description":"Positionnement spéculatif, flux risk-on/off."},
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{"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Niveaux clés, tendances USD/JPY."},
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{"id":"usdjpy","label":"USD/JPY Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée USD/JPY"},
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]
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},
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# ═══════════════════════════════════════════════════════════════════════════════
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"XAUUSD": {
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"name": "XAU/USD (Or)", "output_node": "xauusd",
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"description": "Or vs Dollar — actif refuge, couverture inflation et géopolitique",
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"nodes": [
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{"id":"us_real_rate_10y","label":"Taux réel US 10Y (TIPS)","type":"structural","category":"monetary","unit":"bps","coefficient":-2.5,
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"description":"PLUS IMPORTANT pour l'or. Taux réel US négatif/en baisse → or ↑. Chaque -10bps ≈ +25 pips or."},
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{"id":"dxy_level","label":"Indice Dollar (DXY)","type":"structural","category":"monetary","unit":"pts","coefficient":-3.0,
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"description":"Niveau DXY. Or libellé en USD → DXY ↑ = or ↓ mécaniquement (corrélation ~-0.75)."},
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{"id":"inflation_breakeven_10y","label":"Breakeven inflation 10Y US","type":"structural","category":"inflation","unit":"bps","coefficient":1.5,
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"description":"Anticipations inflation 10Y US (TIPS). Hausse = valeur réelle USD ↓ → or demandé comme couverture."},
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{"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-1.0,
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"description":"Variation taux Fed attendue. Cuts attendus → taux réels ↓ → or ↑."},
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{"id":"global_uncertainty_score","label":"Indice incertitude mondiale","type":"structural","category":"sentiment","unit":"score","coefficient":0.3,
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"description":"Score incertitude géopolitique/économique globale. Hausse → demande refuge or."},
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{"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":0.4,
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"description":"VIX. Spike → or comme couverture risque extrême."},
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{"id":"central_bank_gold_buying","label":"Achats CB (or)","type":"structural","category":"flows","unit":"tonnes/mois","coefficient":2.0,
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"description":"Achats nets d'or par les banques centrales mondiales. Driver structurel majeur depuis 2022."},
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{"id":"etf_gold_flows","label":"Flux ETF or (GLD, IAU)","type":"structural","category":"flows","unit":"tonnes","coefficient":1.5,
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"description":"Flux net dans les ETF adossés à l'or. Entrées → demande physique → or ↑."},
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{"id":"cftc_gold_net_long","label":"Positions nettes or (CoT)","type":"structural","category":"positioning","unit":"k contrats","coefficient":0.15,
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"description":"Positions spéculatives nettes or COMEX. Extrême long → risque liquidation → or ↓."},
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{"id":"us_fiscal_deficit","label":"Déficit fiscal US (%PIB)","type":"structural","category":"macro","unit":"%","coefficient":0.5,
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"description":"Inquiétudes sur la trajectoire fiscale US → doutes sur USD → or refuge."},
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{"id":"gold_mine_cost","label":"Coût d'extraction (AISC)","type":"structural","category":"supply","unit":"$/oz","coefficient":0.05,
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"description":"All-in sustaining cost mines d'or. Support fondamental pour le prix."},
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{"id":"india_china_demand","label":"Demande physique Inde/Chine","type":"structural","category":"flows","unit":"score","coefficient":0.4,
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"description":"Demande physique joaillerie/investment Inde et Chine. Saisonnalité (Diwali, Nouvel An chinois)."},
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{"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."},
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{"id":"ev_macro_surprise","label":"Surprise Macro","type":"event_driven","category":"monetary_shock","unit":"pips","description":"CPI/PCE surprises, NFP → réévaluation taux réels → or."},
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{"id":"ev_geopolitical","label":"Risque Géopolitique","type":"event_driven","category":"geopolitical","unit":"pips","description":"Conflits, tensions → demande refuge or maximale."},
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{"id":"ev_trade_policy","label":"Choc Commercial","type":"event_driven","category":"trade_policy","unit":"pips","description":"Tarifs/guerres commerciales → incertitude → or."},
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{"id":"ev_growth_shock","label":"Choc Croissance","type":"event_driven","category":"growth_shock","unit":"pips","description":"Récession → easing monétaire attendu → or ↑."},
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{"id":"ev_credit_stress","label":"Stress Crédit / Systémique","type":"event_driven","category":"credit_stress","unit":"pips","description":"Crises bancaires, stress liquidité → or refuge absolu."},
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{"id":"ev_commodity","label":"Choc Commodités","type":"event_driven","category":"commodity","unit":"pips","description":"Chocs matières premières (inflation des inputs)."},
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{"id":"ev_sentiment","label":"Sentiment & Flux","type":"event_driven","category":"sentiment","unit":"pips","description":"Sentiment risk-off, flux vers or."},
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{"id":"ev_technical","label":"Momentum Technique","type":"event_driven","category":"technical","unit":"pips","description":"Cassures ATH, niveaux clés or."},
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{"id":"xauusd","label":"XAU/USD Impact Net","type":"output","category":"output","unit":"pips","description":"Pression nette cumulée sur or/USD"},
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]
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},
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# ═══════════════════════════════════════════════════════════════════════════════
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"SP500": {
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"name": "S&P 500", "output_node": "sp500",
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"description": "Indice actions US large cap — baromètre risque global",
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"nodes": [
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{"id":"fed_path_12m","label":"Anticipation Fed 12m","type":"structural","category":"monetary","unit":"bps","coefficient":-0.6,
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"description":"Cuts attendus → taux discount ↓ → PE expansion → SP500 ↑. Chaque -25bps ≈ +15 pts SP500."},
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{"id":"us_real_rate_10y","label":"Taux réel US 10Y","type":"structural","category":"monetary","unit":"bps","coefficient":-1.5,
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"description":"Taux réel = taux d'actualisation des bénéfices futurs. Hausse → PE compression → SP500 ↓."},
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{"id":"eps_growth_12m","label":"Croissance BPA attendue 12m","type":"structural","category":"earnings","unit":"%","coefficient":8.0,
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"description":"Révisions croissance bénéfices S&P 500. +1% révision haussière ≈ +8 pts SP500."},
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{"id":"eps_revision_ratio","label":"Ratio révisions BPA","type":"structural","category":"earnings","unit":"ratio","coefficient":2.0,
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"description":"Ratio révisions haussières/baissières (>1 = majorité haussière). Fort driver momentum."},
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{"id":"pe_expansion","label":"Expansion multiple PE","type":"structural","category":"valuation","unit":"x","coefficient":12.0,
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"description":"Variation multiple PE du S&P 500. +1x PE ≈ +12 pts SP500 (base ~4500)."},
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{"id":"financial_conditions","label":"Indice conditions financières","type":"structural","category":"monetary","unit":"score","coefficient":1.5,
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"description":"Chicago Fed NFCI ou Goldman GSFCI. Desserrement = SP500 ↑."},
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{"id":"credit_spread_ig","label":"Spread crédit IG (bps)","type":"structural","category":"credit","unit":"bps","coefficient":-0.8,
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"description":"Spread obligations IG. Hausse = conditions crédit durcissent → SP500 ↓."},
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{"id":"credit_spread_hy","label":"Spread crédit HY (bps)","type":"structural","category":"credit","unit":"bps","coefficient":-0.5,
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"description":"Spread high yield. Baromètre stress financier. Spike → SP500 ↓."},
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{"id":"buyback_volume","label":"Volume rachats actions","type":"structural","category":"flows","unit":"Mds$/sem","coefficient":0.5,
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"description":"Flux de rachats d'actions S&P 500 en cours. Support technique majeur."},
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{"id":"retail_flow","label":"Flux retail (FOMO)","type":"structural","category":"flows","unit":"score","coefficient":0.3,
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"description":"Score flux acheteurs retail (ETF, options call). Momentum-driven."},
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{"id":"consumer_confidence","label":"Confiance consommateur US","type":"structural","category":"macro","unit":"pts","coefficient":0.3,
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"description":"Conference Board ou Michigan. Signal dépenses → bénéfices → SP500."},
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{"id":"us_gdp_growth","label":"Croissance PIB US","type":"structural","category":"macro","unit":"%","coefficient":5.0,
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"description":"Croissance réelle US annualisée. Surprise haussière → SP500 ↑."},
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{"id":"vix_level","label":"Niveau VIX","type":"structural","category":"sentiment","unit":"pts","coefficient":-0.8,
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"description":"VIX. Spike → risk-off → SP500 ↓. Normalisation VIX → SP500 ↑."},
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{"id":"geopolitical_risk_premium","label":"Prime risque géopolitique","type":"structural","category":"political","unit":"score","coefficient":-0.5,
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"description":"Tensions géopolitiques majeures → incertitude → prime de risque → SP500 ↓."},
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{"id":"ev_central_bank","label":"Pression Banques Centrales","type":"event_driven","category":"central_bank","unit":"pips","description":"Fed pivot, pause, hawkish surprise → SP500 directement."},
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{"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
|