304 lines
14 KiB
Python
304 lines
14 KiB
Python
"""
|
||
Gauge Sync — mappe les dernières données de marché (macro_regime_history)
|
||
vers les nœuds manuels des modèles instruments.
|
||
|
||
Logique :
|
||
- Récupère les derniers gauges (VIX, DXY, US10Y, Brent, LQD, cuivre...)
|
||
- Pour chaque instrument, propose une valeur pour chaque nœud mappable
|
||
- Confidence : HIGH (direct), MEDIUM (dérivé simple), LOW (proxy)
|
||
- L'utilisateur review et confirme dans le frontend
|
||
"""
|
||
import json
|
||
import math
|
||
from typing import Optional
|
||
|
||
# ── Structures de suggestion ───────────────────────────────────────────────────
|
||
|
||
def _conf(level: str, value: float, node_id: str, label: str,
|
||
unit: str, source: str, note: str) -> dict:
|
||
return {
|
||
"node_id": node_id,
|
||
"label": label,
|
||
"value": round(value, 2),
|
||
"unit": unit,
|
||
"source": source,
|
||
"confidence": level, # HIGH | MEDIUM | LOW
|
||
"note": note,
|
||
}
|
||
|
||
|
||
# ── Dérivations communes ───────────────────────────────────────────────────────
|
||
|
||
def _us_real_rate_bps(g: dict) -> float:
|
||
"""Taux réel US approximé : 10Y nominal - breakeven estimé.
|
||
TIPS ETF 109.76 → taux TIPS ≈ 4.487% - 2.0% = ~2.0% → 200bps.
|
||
Approximation : 10Y - 2.2% (breakeven historique moyen).
|
||
"""
|
||
us10y = g.get("us10y", {}).get("value", 4.5)
|
||
breakeven_est = 2.2 # %
|
||
return (us10y - breakeven_est) * 100 # bps
|
||
|
||
|
||
def _risk_appetite_score(g: dict) -> float:
|
||
"""Score appétit risque -5 à +5.
|
||
VIX < 15 = risk-on (+), VIX > 25 = risk-off (−).
|
||
"""
|
||
vix = g.get("vix", {}).get("value", 20.0)
|
||
spx_200d = g.get("spx_vs_200d", {}).get("value", 0.0)
|
||
# Base from VIX
|
||
score = 3.0 - vix / 8.0
|
||
# Adjust from SPX vs 200d MA
|
||
score += spx_200d * 0.05
|
||
return max(-5.0, min(5.0, round(score, 2)))
|
||
|
||
|
||
def _ig_spread_bps(g: dict) -> float:
|
||
"""Proxy spread IG depuis prix LQD ETF.
|
||
LQD à 109+ = spreads très serrés (~80bps). Chaque point de LQD ≈ 5bps spread.
|
||
Baseline : LQD=109 → 80bps spreads.
|
||
"""
|
||
lqd = g.get("lqd", {}).get("value", 109.0)
|
||
return max(0, round((110.0 - lqd) * 8 + 80, 0))
|
||
|
||
|
||
def _recession_prob_pct(g: dict) -> float:
|
||
"""Probabilité récession % depuis pente 10Y-3M.
|
||
Pente positive (0.87%) → faible risque récession ~20%.
|
||
Pente inversée (<0) → risque élevé.
|
||
"""
|
||
slope = g.get("slope_10y3m", {}).get("value", 1.0)
|
||
prob = 50.0 - slope * 28.0
|
||
return max(0.0, min(95.0, round(prob, 1)))
|
||
|
||
|
||
def _energy_delta(g: dict, baseline: float = 70.0) -> float:
|
||
"""Delta prix énergie (Brent) vs baseline ($/bbl)."""
|
||
brent = g.get("brent", {}).get("value", baseline)
|
||
return round(brent - baseline, 2)
|
||
|
||
|
||
def _copper_score(g: dict) -> float:
|
||
"""Score cycle cuivre -5 à +5. Baseline ~$4.5/lb."""
|
||
copper = g.get("copper", {}).get("value", 4.5)
|
||
return round(max(-5, min(5, (copper - 4.5) * 2)), 2)
|
||
|
||
|
||
def _dxy_level(g: dict) -> float:
|
||
return g.get("dxy", {}).get("value", 100.0)
|
||
|
||
|
||
def _vix(g: dict) -> float:
|
||
return g.get("vix", {}).get("value", 18.0)
|
||
|
||
|
||
def _us10y_bps(g: dict) -> float:
|
||
return round(g.get("us10y", {}).get("value", 4.5) * 100, 0)
|
||
|
||
|
||
def _us30y_bps(g: dict) -> float:
|
||
"""Approxime 30Y = 10Y + 25bps prime terme."""
|
||
return round((_us10y_bps(g) / 100 + 0.25) * 100, 0)
|
||
|
||
|
||
def _yield_diff_usdjpy_bps(g: dict) -> float:
|
||
"""Différentiel 10Y US-Japon (JGB ≈ 1.0% depuis politique BoJ)."""
|
||
us10y = g.get("us10y", {}).get("value", 4.5)
|
||
jgb10y_approx = 1.0 # Policy-controlled, approximate
|
||
return round((us10y - jgb10y_approx) * 100, 0)
|
||
|
||
|
||
def _slope_bps(g: dict) -> float:
|
||
return round(g.get("slope_10y3m", {}).get("value", 1.0) * 100, 0)
|
||
|
||
|
||
# ── Mappings par instrument ────────────────────────────────────────────────────
|
||
|
||
def _suggest_eurusd(g: dict) -> list[dict]:
|
||
vix = _vix(g)
|
||
real = _us_real_rate_bps(g)
|
||
risk = _risk_appetite_score(g)
|
||
energy = _energy_delta(g, baseline=70.0)
|
||
return [
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX actuel = {vix:.1f}"),
|
||
_conf("HIGH", real, "m_us_real_rate", "Taux réel US 10Y", "bps", "gauge:us10y+tips", f"10Y {g.get('us10y',{}).get('value',4.5):.2f}% - 2.2% breakeven ≈ {real:.0f}bps"),
|
||
_conf("MEDIUM", risk, "m_risk_appetite","Appétit risque mondial", "score", "gauge:vix+spx", f"Score = 3 - VIX/8 + SPX200d×0.05 = {risk:.2f}"),
|
||
_conf("MEDIUM", energy, "m_energy_price", "Prix énergie delta", "$/bbl", "gauge:brent", f"Brent {g.get('brent',{}).get('value',70):.1f}$ - baseline 70$ = {energy:+.1f}"),
|
||
_conf("LOW", _dxy_level(g), "m_dollar_reserve", "Demande réserves USD", "score", "gauge:dxy", f"DXY {_dxy_level(g):.1f} → proxy demand USD (>100 = fort)"),
|
||
]
|
||
|
||
|
||
def _suggest_usdjpy(g: dict) -> list[dict]:
|
||
vix = _vix(g)
|
||
risk = _risk_appetite_score(g)
|
||
yd = _yield_diff_usdjpy_bps(g)
|
||
slope = _slope_bps(g)
|
||
return [
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX = {vix:.1f}"),
|
||
_conf("HIGH", yd, "m_yield_diff", "Diff 10Y US-JP", "bps", "gauge:us10y", f"US10Y {g.get('us10y',{}).get('value',4.5):.2f}% - JGB≈1.0% = {yd:.0f}bps"),
|
||
_conf("MEDIUM", _us_real_rate_bps(g), "m_us_real_rate", "Taux réel US 10Y", "bps", "gauge:us10y", f"≈ {_us_real_rate_bps(g):.0f}bps"),
|
||
_conf("MEDIUM", risk, "m_risk_appetite", "Appétit risque", "score", "gauge:vix", f"Score = {risk:.2f}"),
|
||
_conf("LOW", slope,"m_carry_momentum","Momentum carry", "score", "gauge:slope", f"Pente 10Y-3M = {slope:.0f}bps → carry actif"),
|
||
]
|
||
|
||
|
||
def _suggest_xauusd(g: dict) -> list[dict]:
|
||
vix = _vix(g)
|
||
dxy = _dxy_level(g)
|
||
real = _us_real_rate_bps(g)
|
||
return [
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX = {vix:.1f}"),
|
||
_conf("HIGH", dxy, "m_dxy", "DXY (indice dollar)", "pts", "gauge:dxy", f"DXY = {dxy:.1f}"),
|
||
_conf("HIGH", real, "m_us_real_rate", "Taux réel US 10Y", "bps", "gauge:us10y+tips", f"≈ {real:.0f}bps (10Y - 2.2% breakeven)"),
|
||
_conf("MEDIUM", _energy_delta(g, 70), "m_fiscal_risk", "Risque fiscal US", "score", "gauge:dxy", f"DXY < 100 = doutes USD → score {round(max(0, (100-dxy)/5), 1)}"),
|
||
_conf("LOW", _copper_score(g), "m_india_china", "Demande physique Asie", "score", "gauge:copper", f"Cuivre {g.get('copper',{}).get('value',4.5):.2f}$/lb → proxy Asie"),
|
||
]
|
||
|
||
|
||
def _suggest_sp500(g: dict) -> list[dict]:
|
||
vix = _vix(g)
|
||
real = _us_real_rate_bps(g)
|
||
ig = _ig_spread_bps(g)
|
||
risk = _risk_appetite_score(g)
|
||
spx200d = g.get("spx_vs_200d", {}).get("value", 0)
|
||
rec_prob = _recession_prob_pct(g)
|
||
return [
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX = {vix:.1f}"),
|
||
_conf("HIGH", real, "m_real_rate", "Taux réel US 10Y", "bps", "gauge:us10y+tips", f"≈ {real:.0f}bps"),
|
||
_conf("HIGH", ig, "m_ig_spread", "Spread IG (LQD proxy)", "bps", "gauge:lqd", f"LQD {g.get('lqd',{}).get('value',109):.2f} → spread ≈ {ig:.0f}bps"),
|
||
_conf("MEDIUM", risk, "m_fin_cond", "Conditions financières", "score", "gauge:vix+lqd", f"Score = {risk:.2f}"),
|
||
_conf("MEDIUM", rec_prob,"m_gdp", "Croissance PIB US", "%", "gauge:slope", f"Pente 10Y-3M = {_slope_bps(g):.0f}bps → récession {rec_prob:.0f}% (inversé → PIB)"),
|
||
]
|
||
|
||
|
||
def _suggest_tlt(g: dict) -> list[dict]:
|
||
us10y = _us10y_bps(g)
|
||
us30y = _us30y_bps(g)
|
||
vix = _vix(g)
|
||
slope = _slope_bps(g)
|
||
rec = _recession_prob_pct(g)
|
||
tp = round(slope * 0.6, 0) # term premium proxy
|
||
return [
|
||
_conf("HIGH", us10y, "m_us_10y", "Rendement UST 10Y", "bps", "gauge:us10y", f"UST 10Y = {us10y/100:.3f}%"),
|
||
_conf("HIGH", us30y, "m_us_30y", "Rendement UST 30Y", "bps", "gauge:us10y", f"≈ 10Y + 25bps = {us30y/100:.3f}%"),
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX = {vix:.1f}"),
|
||
_conf("MEDIUM", tp, "m_term_prem", "Prime de terme", "bps", "gauge:slope", f"Pente 10Y-3M {slope:.0f}bps × 0.6 ≈ {tp:.0f}bps"),
|
||
_conf("MEDIUM", rec, "m_recession_prob", "Prob. récession 12m", "%", "gauge:slope", f"Pente {slope:.0f}bps → prob ≈ {rec:.0f}%"),
|
||
_conf("LOW", round(_ig_spread_bps(g)/10, 1), "m_foreign_demand",
|
||
"Demande étrangère", "Mds$", "gauge:lqd", f"LQD sain → proxy demand bonds = {round(_ig_spread_bps(g)/10,1)}"),
|
||
]
|
||
|
||
|
||
def _suggest_gbpusd(g: dict) -> list[dict]:
|
||
vix = _vix(g)
|
||
risk = _risk_appetite_score(g)
|
||
real = _us_real_rate_bps(g)
|
||
return [
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX = {vix:.1f}"),
|
||
_conf("MEDIUM", risk, "m_risk_appetite", "Appétit risque", "score", "gauge:vix", f"GBP devise cyclique, risk-on = {risk:.2f}"),
|
||
_conf("LOW", real, "m_fed_path", "Anticipation Fed 12m", "bps", "gauge:us10y", f"Proxy Fed path depuis taux réels ≈ {real:.0f}bps"),
|
||
]
|
||
|
||
|
||
def _suggest_eem(g: dict) -> list[dict]:
|
||
dxy = _dxy_level(g)
|
||
vix = _vix(g)
|
||
real = _us_real_rate_bps(g)
|
||
risk = _risk_appetite_score(g)
|
||
em_spread = round(_ig_spread_bps(g) * 1.8, 0) # EM spreads ≈ 1.8× IG
|
||
cop = _copper_score(g)
|
||
return [
|
||
_conf("HIGH", dxy, "m_dxy_inv", "Dollar DXY", "pts", "gauge:dxy", f"DXY = {dxy:.1f}"),
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX = {vix:.1f}"),
|
||
_conf("HIGH", real, "m_us_real_rate", "Taux réel US 10Y", "bps", "gauge:us10y", f"≈ {real:.0f}bps"),
|
||
_conf("MEDIUM", risk, "m_risk_appetite", "Appétit risque", "score", "gauge:vix", f"Score = {risk:.2f}"),
|
||
_conf("MEDIUM", em_spread, "m_em_spread", "Spread souverain EM", "bps", "gauge:lqd", f"LQD proxy × 1.8 ≈ {em_spread:.0f}bps"),
|
||
_conf("MEDIUM", cop, "m_commodity_index","Indice commodités", "score", "gauge:copper",f"Cuivre {g.get('copper',{}).get('value',4.5):.2f} → score {cop:.2f}"),
|
||
_conf("LOW", round(cop * 0.5, 2), "m_china_pmi", "PMI manuf. Chine", "pts", "gauge:copper",f"Proxy cuivre → PMI-like = {round(50+cop*0.5,1)}"),
|
||
]
|
||
|
||
|
||
def _suggest_qqq(g: dict) -> list[dict]:
|
||
vix = _vix(g)
|
||
real = _us_real_rate_bps(g)
|
||
risk = _risk_appetite_score(g)
|
||
spx_vs200 = g.get("spx_vs_200d", {}).get("value", 0)
|
||
return [
|
||
_conf("HIGH", vix, "m_vix", "Niveau VIX", "pts", "gauge:vix", f"VIX = {vix:.1f} (beta élevé QQQ)"),
|
||
_conf("HIGH", real, "m_real_rate", "Taux réel US 10Y (duration)","bps","gauge:us10y", f"≈ {real:.0f}bps → taux d'actualisation tech"),
|
||
_conf("MEDIUM", risk, "m_retail_options", "Flux options retail", "score", "gauge:spx", f"Risk appetite {risk:.2f} → proxy momentum options"),
|
||
_conf("LOW", round(spx_vs200 * 0.8, 2), "m_tech_pe",
|
||
"Multiple PE tech (NTM)", "x", "gauge:spx_vs_200d", f"SPX vs 200d = {spx_vs200:.1f}% → PE trend"),
|
||
]
|
||
|
||
|
||
# ── Registry ───────────────────────────────────────────────────────────────────
|
||
|
||
_SUGGEST_FN = {
|
||
"EURUSD": _suggest_eurusd,
|
||
"USDJPY": _suggest_usdjpy,
|
||
"XAUUSD": _suggest_xauusd,
|
||
"SP500": _suggest_sp500,
|
||
"TLT": _suggest_tlt,
|
||
"GBPUSD": _suggest_gbpusd,
|
||
"EEM": _suggest_eem,
|
||
"QQQ": _suggest_qqq,
|
||
}
|
||
|
||
|
||
# ── Public API ─────────────────────────────────────────────────────────────────
|
||
|
||
def get_latest_gauges(conn, at_date: Optional[str] = None) -> tuple[dict, str]:
|
||
"""Retourne (gauges_dict, snapshot_date) depuis macro_regime_history.
|
||
Si at_date fourni, retourne le snapshot le plus proche <= at_date."""
|
||
if at_date:
|
||
row = conn.execute(
|
||
"SELECT gauges_summary_json, timestamp FROM macro_regime_history "
|
||
"WHERE timestamp <= ? ORDER BY timestamp DESC LIMIT 1",
|
||
(at_date,)
|
||
).fetchone()
|
||
else:
|
||
row = conn.execute(
|
||
"SELECT gauges_summary_json, timestamp FROM macro_regime_history "
|
||
"ORDER BY timestamp DESC LIMIT 1"
|
||
).fetchone()
|
||
|
||
if row:
|
||
r = dict(row)
|
||
gauges = json.loads(r.get("gauges_summary_json") or "{}")
|
||
date = str(r.get("timestamp", ""))[:10]
|
||
if gauges:
|
||
return gauges, date
|
||
|
||
# Fallback : macro_gauge_snapshots
|
||
if at_date:
|
||
row2 = conn.execute(
|
||
"SELECT gauges_json, snapshot_date FROM macro_gauge_snapshots "
|
||
"WHERE snapshot_date <= ? ORDER BY snapshot_date DESC LIMIT 1",
|
||
(at_date,)
|
||
).fetchone()
|
||
else:
|
||
row2 = conn.execute(
|
||
"SELECT gauges_json, snapshot_date FROM macro_gauge_snapshots "
|
||
"ORDER BY snapshot_date DESC LIMIT 1"
|
||
).fetchone()
|
||
if row2:
|
||
return json.loads(row2["gauges_json"] or "{}"), str(row2["snapshot_date"])
|
||
|
||
return {}, ""
|
||
|
||
|
||
def suggest_from_gauges(instrument: str, gauges: dict) -> list[dict]:
|
||
"""
|
||
Retourne les suggestions de valeurs pour les nœuds manuels d'un instrument
|
||
depuis les gauges de marché actuels.
|
||
"""
|
||
fn = _SUGGEST_FN.get(instrument.upper())
|
||
if not fn or not gauges:
|
||
return []
|
||
suggestions = fn(gauges)
|
||
# Filtre les NaN / Inf
|
||
return [
|
||
s for s in suggestions
|
||
if math.isfinite(s.get("value", 0))
|
||
]
|