feat: instrument model
This commit is contained in:
OpenSquared
@@ -194,6 +194,170 @@ def build_macro_node_timeline(
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return result
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def build_node_combined_timeline(
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conn, instrument: str, node_id: str, macro_key: Optional[str],
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date_from: date_type, date_to: date_type,
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) -> dict[str, float]:
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"""
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Construit la timeline complète d'un nœud en fusionnant :
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1. Valeurs passées (ff_calendar actuals via macro_key)
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2. Scénarios utilisateur futurs (CT/MT/LT) avec interpolation pondérée par confidence
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Logique :
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- Pour les dates ≤ aujourd'hui : ff_calendar actuals (si disponibles)
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- Pour les dates futures : interpolation depuis la dernière valeur connue
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vers chaque scénario, pondérée par confidence
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- Si plusieurs scénarios se chevauchent : moyenne pondérée par confidence
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"""
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today = date_type.today()
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# 1. Baseline depuis ff_calendar (passé uniquement en pratique)
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ff_tl = build_macro_node_timeline(conn, macro_key, date_from, date_to) if macro_key else {}
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# 2. Scénarios utilisateur
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try:
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scen_rows = conn.execute(
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"""SELECT id, label, horizon, target_date, target_value, confidence, trajectory, absorption_days
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FROM node_forecast_scenarios
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WHERE instrument=? AND node_id=?
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ORDER BY target_date ASC""",
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(instrument.upper(), node_id)
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).fetchall()
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scenarios = [dict(r) for r in scen_rows]
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except Exception:
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scenarios = []
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if not scenarios:
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return ff_tl # Pas de scénarios → juste ff_calendar
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# 3. Valeur de départ (dernière connue depuis ff_calendar ou override statique)
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base_value: Optional[float] = None
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if ff_tl:
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# Dernière valeur connue avant ou à aujourd'hui
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for d in sorted(ff_tl.keys(), reverse=True):
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if d <= str(today):
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base_value = ff_tl[d]
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break
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if base_value is None:
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base_value = next(iter(ff_tl.values()), None)
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# Fallback sur override statique si pas de ff_calendar
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if base_value is None:
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try:
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ov = conn.execute(
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"SELECT value FROM instrument_node_overrides WHERE instrument=? AND node_id=?",
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(instrument.upper(), node_id)
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).fetchone()
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if ov:
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base_value = float(ov["value"])
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except Exception:
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pass
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if base_value is None:
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return ff_tl # Pas de base → on ne peut pas projeter
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# 4. Construire les waypoints (date, value, confidence) depuis les scénarios
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# Le point de départ est toujours (today, base_value, 1.0)
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waypoints: list[tuple[date_type, float, float]] = [(today, base_value, 1.0)]
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for s in scenarios:
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try:
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td = date_type.fromisoformat(s["target_date"])
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if td > today:
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waypoints.append((td, float(s["target_value"]), float(s["confidence"])))
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except (ValueError, KeyError):
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continue
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waypoints.sort(key=lambda x: x[0])
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# 5. Construire la timeline journalière
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result: dict[str, float] = {}
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cur = date_from
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while cur <= date_to:
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cur_str = str(cur)
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if cur <= today and cur_str in ff_tl:
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# Passé → priorité ff_calendar
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result[cur_str] = ff_tl[cur_str]
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else:
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# Futur → interpolation entre waypoints
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prev_wp: Optional[tuple[date_type, float, float]] = None
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next_wp: Optional[tuple[date_type, float, float]] = None
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for wp in waypoints:
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if wp[0] <= cur:
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prev_wp = wp
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elif next_wp is None:
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next_wp = wp
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break
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if prev_wp is None and next_wp is None:
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if ff_tl:
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# Dernier connu
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result[cur_str] = ff_tl.get(str(today)) or list(ff_tl.values())[-1]
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elif prev_wp is None:
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result[cur_str] = round(next_wp[1], 4) # type: ignore[index]
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elif next_wp is None:
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result[cur_str] = round(prev_wp[1], 4) # hold last value
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else:
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# Interpolation linéaire entre prev et next, pondérée par confidence
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total_d = (next_wp[0] - prev_wp[0]).days
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elapsed = (cur - prev_wp[0]).days
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frac = elapsed / total_d if total_d > 0 else 0.0
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# Valeur interpolée brute
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interp = prev_wp[1] + frac * (next_wp[1] - prev_wp[1])
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# La confidence du prochain scénario pondère le drift :
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# confidence=1.0 → drift complet vers interp
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# confidence=0.5 → 50% du drift, reste à mi-chemin entre prev et interp
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conf = next_wp[2]
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blended = prev_wp[1] + (interp - prev_wp[1]) * conf
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result[cur_str] = round(blended, 4)
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cur += timedelta(days=1)
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return result
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# ── Scenario CRUD ──────────────────────────────────────────────────────────────
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def get_node_scenarios(conn, instrument: str, node_id: Optional[str] = None) -> list[dict]:
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"""Retourne tous les scénarios d'un instrument (ou d'un nœud spécifique)."""
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if node_id:
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rows = conn.execute(
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"""SELECT * FROM node_forecast_scenarios
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WHERE instrument=? AND node_id=? ORDER BY target_date ASC""",
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(instrument.upper(), node_id)
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).fetchall()
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else:
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rows = conn.execute(
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"SELECT * FROM node_forecast_scenarios WHERE instrument=? ORDER BY node_id, target_date ASC",
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(instrument.upper(),)
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).fetchall()
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return [dict(r) for r in rows]
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def add_node_scenario(
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conn, instrument: str, node_id: str, label: str,
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horizon: str, target_date: str, target_value: float,
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confidence: float = 0.7, trajectory: str = "linear",
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absorption_days: int = 30, notes: str = "",
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) -> int:
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"""Ajoute un scénario forecast sur un nœud. Retourne l'id créé."""
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cur = conn.execute(
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"""INSERT INTO node_forecast_scenarios
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(instrument, node_id, label, horizon, target_date, target_value,
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confidence, trajectory, absorption_days, notes)
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VALUES (?,?,?,?,?,?,?,?,?,?)""",
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(instrument.upper(), node_id, label, horizon, target_date, float(target_value),
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float(confidence), trajectory, int(absorption_days), notes or "")
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)
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conn.commit()
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return cur.lastrowid
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def delete_node_scenario(conn, scenario_id: int) -> bool:
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conn.execute("DELETE FROM node_forecast_scenarios WHERE id=?", (scenario_id,))
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conn.commit()
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return True
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# ── Saturation scales (tanh) par unité native ─────────────────────────────────
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# tanh(x/scale) : slope=1 à l'origine, sature asymptotiquement à ±1
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# pips = coefficient_to_pips * scale * tanh(x / scale)
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@@ -201,10 +365,11 @@ def build_macro_node_timeline(
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# → max pips : coefficient_to_pips * scale (jamais dépassé)
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_SATURATION_SCALES: dict[str, float] = {
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"bps": 200.0, # différentiels de taux : sature autour ±300bps
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"%": 3.0, # CPI / PIB différentiels : sature autour ±5%
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"%": 3.0, # CPI / PIB / taux en % absolu : sature autour ±5%
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"pts%": 3.0,
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"pts": 15.0, # PMI écart depuis 50 : sature autour ±20pts
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"score": 3.0, # scores subjectifs -5 à +5
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"K": 200.0, # emplois en milliers : sature autour ±400K
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"tonnes": 80.0, # tonnes or / CB buying
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"Mds$": 40.0,
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"Mds$/sem": 15.0,
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@@ -364,57 +529,49 @@ INSTRUMENT_MODELS: dict[str, dict] = {
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# ══════════════════════════════════════════════════════════════════════════════
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"EURUSD": {
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"name": "EUR/USD",
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"description": "Taux de change Euro/Dollar — modèle causal 3 couches avec 4 domaines d'influence",
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"description": "EUR/USD — Graphe causal macro-natif : noeuds = variables macro réelles auto-synchronisées depuis FF Calendar",
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"output_node": "eurusd",
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"price_intercept": 1.10,
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"pip_to_price": 0.0001,
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"yf_ticker": "EURUSD=X",
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"nodes": [
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# ── Layer 0a : event inputs ───────────────────────────────────────────────
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{"id":"in_cb", "label":"Banques Centrales", "node_type":"input_event", "category":"central_bank", "unit":"pips","display_col":0,"description":"Décisions Fed/BCE, minutes, forward guidance. Décroissance exp ~14j.","event_category":"central_bank"},
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{"id":"in_macro", "label":"Surprises Macro (données)","node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"description":"CPI, NFP, PIB, PMI US/EU. Décroissance rapide ~12j.","event_category":"monetary_shock"},
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{"id":"in_geo", "label":"Risque Géopolitique", "node_type":"input_event", "category":"geopolitical", "unit":"pips","display_col":0,"description":"Conflits, sanctions, tensions. Décroissance linéaire ~21j.","event_category":"geopolitical"},
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{"id":"in_trade", "label":"Choc Commercial/Tarifs", "node_type":"input_event", "category":"trade_policy", "unit":"pips","display_col":0,"description":"Tarifs US-UE, accords commerciaux.","event_category":"trade_policy"},
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{"id":"in_growth", "label":"Choc Croissance", "node_type":"input_event", "category":"growth_shock", "unit":"pips","display_col":0,"description":"Chocs perspectives croissance.","event_category":"growth_shock"},
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{"id":"in_credit", "label":"Stress Crédit/Liquidité", "node_type":"input_event", "category":"credit_stress", "unit":"pips","display_col":0,"description":"Banking stress, spreads IG/HY.","event_category":"credit_stress"},
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{"id":"in_technical", "label":"Momentum Technique", "node_type":"input_event", "category":"technical", "unit":"pips","display_col":0,"description":"Cassures niveaux clés, tendances.","event_category":"technical"},
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{"id":"in_commodity", "label":"Choc Commodités", "node_type":"input_event", "category":"commodity", "unit":"pips","display_col":0,"description":"Pétrole, gaz, métaux → inflation EU.","event_category":"commodity"},
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{"id":"in_sentiment", "label":"Sentiment/Positionnement","node_type":"input_event", "category":"sentiment", "unit":"pips","display_col":0,"description":"Flux institutionnels, risk-on/off.","event_category":"sentiment"},
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# ── Layer 0b : manual structural inputs ──────────────────────────────────
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{"id":"m_rate_diff_2y", "label":"Spread OIS 2Y USD-EUR", "node_type":"input_manual","category":"monetary", "unit":"bps", "coefficient_to_pips": 1.5, "display_col":1,"description":"Principal driver court terme. Positif = USD plus rémunérateur → pair ↓"},
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{"id":"m_fed_path", "label":"Anticipation Fed 12m", "node_type":"input_manual","category":"monetary", "unit":"bps", "coefficient_to_pips": 0.8, "display_col":1,"description":"Cuts attendus → USD ↓ → pair ↑. Hikes → USD ↑ → pair ↓"},
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{"id":"m_ecb_path", "label":"Anticipation BCE 12m", "node_type":"input_manual","category":"monetary", "unit":"bps", "coefficient_to_pips":-0.8, "display_col":1,"description":"Cuts BCE → EUR ↓. Hikes → EUR ↑"},
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{"id":"m_us_real_rate", "label":"Taux réel US 10Y (TIPS)", "node_type":"input_manual","category":"monetary", "unit":"bps", "coefficient_to_pips":-0.5, "display_col":1,"description":"Hausse → USD attractif → pair ↓"},
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{"id":"m_eu_real_rate", "label":"Taux réel EU 10Y", "node_type":"input_manual","category":"monetary", "unit":"bps", "coefficient_to_pips": 0.5, "display_col":1,"description":"Hausse → EUR attractif → pair ↑"},
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{"id":"m_carry", "label":"Score carry EUR/USD", "node_type":"input_manual","category":"monetary", "unit":"score", "coefficient_to_pips": 0.6, "display_col":1,"description":"Score attractivité carry (+= EUR avantageux)"},
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{"id":"m_us_growth_adv", "label":"Avantage croissance US vs EU","node_type":"input_manual","category":"macro", "unit":"pts%", "coefficient_to_pips":-15.0,"display_col":1,"description":"Différentiel PIB US-EU. US surperform → USD ↑ → pair ↓"},
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{"id":"m_eu_pmi", "label":"PMI composite Eurozone", "node_type":"input_manual","category":"macro", "unit":"pts", "coefficient_to_pips": 0.3, "display_col":1,"description":"Expansion EU → EUR ↑"},
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{"id":"m_energy_price", "label":"Prix énergie ($/bbl delta)", "node_type":"input_manual","category":"macro", "unit":"$/bbl", "coefficient_to_pips":-0.25,"display_col":1,"description":"Énergie chère → déficit commercial EU → EUR ↓"},
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{"id":"m_us_cpi", "label":"CPI US YoY", "node_type":"input_manual","category":"inflation", "unit":"%", "coefficient_to_pips":-0.8, "display_col":1,"description":"Inflation US → Fed hawkish → USD ↑ → pair ↓"},
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{"id":"m_eu_cpi", "label":"HICP Eurozone YoY", "node_type":"input_manual","category":"inflation", "unit":"%", "coefficient_to_pips": 0.8, "display_col":1,"description":"Inflation EU → BCE hawkish → EUR ↑"},
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{"id":"m_risk_appetite", "label":"Appétit risque mondial", "node_type":"input_manual","category":"sentiment", "unit":"score", "coefficient_to_pips": 0.5, "display_col":1,"description":"Risk-on → sorties USD → pair ↑"},
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{"id":"m_vix", "label":"Niveau VIX", "node_type":"input_manual","category":"sentiment", "unit":"pts", "coefficient_to_pips":-0.4, "display_col":1,"description":"VIX spike → flight to USD → pair ↓"},
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{"id":"m_eu_fragmentation","label":"Risque fragmentation EU", "node_type":"input_manual","category":"political", "unit":"score", "coefficient_to_pips":-0.5, "display_col":1,"description":"Risque politique EU → EUR ↓"},
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{"id":"m_us_political", "label":"Incertitude politique US", "node_type":"input_manual","category":"political", "unit":"score", "coefficient_to_pips": 0.3, "display_col":1,"description":"Incertitude US → USD ↓ → pair ↑"},
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{"id":"m_cftc_eur", "label":"Positions nettes EUR (CoT)", "node_type":"input_manual","category":"positioning","unit":"k lots","coefficient_to_pips": 0.1, "display_col":1,"description":"Net long EUR CFTC. Extrême → risque retournement"},
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{"id":"m_dollar_reserve", "label":"Demande réserves USD", "node_type":"input_manual","category":"flows", "unit":"score", "coefficient_to_pips":-0.4, "display_col":1,"description":"Demande réserves → USD structurellement fort → pair ↓"},
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# ── Layer 1 : intermediate (4 domaines) ──────────────────────────────────
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{"id":"layer_monetary", "label":"▶ Pression Monétaire & Taux", "node_type":"intermediate","category":"intermediate","unit":"pips","display_col":2,
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"formula":"in_cb + in_macro + m_rate_diff_2y + m_fed_path + m_ecb_path + m_us_real_rate + m_eu_real_rate + m_carry",
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"description":"Domaine monétaire : taux directeurs, OIS, anticipations Fed/BCE, carry. Driver n°1 de l'EURUSD."},
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{"id":"layer_growth", "label":"▶ Pression Macro & Croissance", "node_type":"intermediate","category":"intermediate","unit":"pips","display_col":2,
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"formula":"in_growth + in_commodity + m_us_growth_adv + m_eu_pmi + m_energy_price + m_us_cpi + m_eu_cpi",
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"description":"Domaine macro : croissance relative, inflation, énergie. Impact via anticipations BC."},
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{"id":"layer_risk", "label":"▶ Pression Risque & Géopolitique", "node_type":"intermediate","category":"intermediate","unit":"pips","display_col":2,
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"formula":"in_geo + in_credit + in_sentiment + m_risk_appetite + m_vix + m_eu_fragmentation",
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"description":"Domaine risque : géopolitique, stress crédit, aversion au risque (USD safe haven)."},
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{"id":"layer_positioning","label":"▶ Pression Positionnement & Flux", "node_type":"intermediate","category":"intermediate","unit":"pips","display_col":2,
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"formula":"in_trade + in_technical + m_cftc_eur + m_dollar_reserve + m_us_political",
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"description":"Domaine positionnement : flux spéculatifs, technicalités, réserves, politique."},
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# ── Layer 0a : chocs événementiels (surprises court terme) ───────────────
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{"id":"in_cb", "label":"Choc Banques Centrales", "node_type":"input_event","category":"central_bank", "unit":"pips","display_col":0,"event_category":"central_bank", "description":"Surprises Fed/BCE : décisions inattendues, guidance hawkish/dovish, minutes."},
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{"id":"in_geo", "label":"Choc Géopolitique", "node_type":"input_event","category":"geopolitical", "unit":"pips","display_col":0,"event_category":"geopolitical", "description":"Conflits, sanctions, tensions → flight to USD."},
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{"id":"in_trade", "label":"Choc Commercial/Tarifs", "node_type":"input_event","category":"trade_policy", "unit":"pips","display_col":0,"event_category":"trade_policy", "description":"Tarifs US-UE, représailles → USD/EUR volatilité."},
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{"id":"in_credit","label":"Stress Crédit", "node_type":"input_event","category":"credit_stress","unit":"pips","display_col":0,"event_category":"credit_stress", "description":"Stress bancaire, spreads → USD safe haven."},
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# ── Layer 0b : politique monétaire (taux directeurs absolus) ─────────────
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# macro_key = id → auto-sync depuis ff_calendar sans mapping manuel
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{"id":"fed_rate","label":"Taux Fed (%)", "node_type":"input_manual","category":"monetary","unit":"%","coefficient_to_pips":-30, "macro_key":"fed_rate", "display_col":1,"description":"Taux directeur Fed en %. Hausse → USD fort → pair ↓. Auto-sync depuis FF Calendar."},
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{"id":"ecb_rate","label":"Taux BCE (%)", "node_type":"input_manual","category":"monetary","unit":"%","coefficient_to_pips":+30, "macro_key":"ecb_rate", "display_col":1,"description":"Taux directeur BCE en %. Hausse → EUR fort → pair ↑. Auto-sync depuis FF Calendar."},
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# ── Layer 0c : inflation ─────────────────────────────────────────────────
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{"id":"us_cpi", "label":"CPI US YoY (%)", "node_type":"input_manual","category":"inflation","unit":"%","coefficient_to_pips":-10, "macro_key":"us_cpi_yoy","display_col":1,"description":"Inflation US YoY. Hausse → anticipations Fed hawkish → USD ↑ → pair ↓."},
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{"id":"eu_cpi", "label":"HICP Eurozone (%)", "node_type":"input_manual","category":"inflation","unit":"%","coefficient_to_pips":+10, "macro_key":"eu_cpi_yoy","display_col":1,"description":"Inflation EU YoY. Hausse → BCE hawkish → EUR ↑ → pair ↑."},
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# ── Layer 0d : croissance ─────────────────────────────────────────────────
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{"id":"us_gdp", "label":"GDP US QoQ (%)", "node_type":"input_manual","category":"growth","unit":"%","coefficient_to_pips":-15, "macro_key":"us_gdp", "display_col":1,"description":"Croissance US trimestrielle. Surperformance → USD ↑ → pair ↓."},
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{"id":"eu_gdp", "label":"GDP Eurozone QoQ (%)","node_type":"input_manual","category":"growth","unit":"%","coefficient_to_pips":+15, "macro_key":"eu_gdp", "display_col":1,"description":"Croissance EU trimestrielle. Surperformance → EUR ↑ → pair ↑."},
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# ── Layer 0e : emploi & activité ─────────────────────────────────────────
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{"id":"us_nfp", "label":"NFP US (K/mois)", "node_type":"input_manual","category":"labor","unit":"K","coefficient_to_pips":-0.05, "macro_key":"us_nfp", "display_col":1,"description":"Emplois non-agricoles US en K. 150K= neutre. Plus → USD ↑ → pair ↓."},
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{"id":"eu_pmi", "label":"PMI EU (écart/50)", "node_type":"input_manual","category":"activity","unit":"pts","coefficient_to_pips":+2.5,"macro_key":"eu_pmi", "display_col":1,"description":"PMI Composite Eurozone MOINS 50 (+4 = PMI=54, expansion → EUR ↑)."},
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{"id":"us_pmi", "label":"PMI US (écart/50)", "node_type":"input_manual","category":"activity","unit":"pts","coefficient_to_pips":-2.5,"macro_key":"us_pmi", "display_col":1,"description":"PMI ISM US MOINS 50 (+3 = PMI=53, expansion → USD ↑ → pair ↓)."},
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# ── Layer 0f : sentiment & risque (pas de macro_key — saisi ou events) ───
|
||||
{"id":"vix", "label":"VIX (niveau)", "node_type":"input_manual","category":"risk","unit":"pts","coefficient_to_pips":-1.5, "display_col":1,"description":"Volatilité equity US. Spike → safe haven USD → pair ↓."},
|
||||
{"id":"us_equity","label":"S&P 500 momentum", "node_type":"input_manual","category":"risk","unit":"score","coefficient_to_pips":+0.3, "display_col":1,"description":"Risk-on US. Hausse → appétit risque → EUR ↑. Score -5/+5."},
|
||||
{"id":"eu_fragm","label":"Fragmentation EU", "node_type":"input_manual","category":"political","unit":"score","coefficient_to_pips":-0.5, "display_col":1,"description":"Risque fragmentation zone euro, stress BTP/Bund. Score 0-5 → EUR ↓."},
|
||||
# ── Layer 1 : domaines synthèse ───────────────────────────────────────────
|
||||
{"id":"layer_monetary","label":"▶ Différentiel Monétaire","node_type":"intermediate","category":"intermediate","unit":"pips","display_col":2,
|
||||
"formula":"in_cb + fed_rate + ecb_rate + us_cpi + eu_cpi",
|
||||
"description":"Taux directeurs + inflation → différentiel de politique monétaire Fed/BCE."},
|
||||
{"id":"layer_growth", "label":"▶ Différentiel Croissance","node_type":"intermediate","category":"intermediate","unit":"pips","display_col":2,
|
||||
"formula":"us_gdp + eu_gdp + us_nfp + eu_pmi + us_pmi",
|
||||
"description":"Croissance relative US vs EU. Positif = EU surperform → EUR ↑."},
|
||||
{"id":"layer_risk", "label":"▶ Sentiment & Risque", "node_type":"intermediate","category":"intermediate","unit":"pips","display_col":2,
|
||||
"formula":"in_geo + in_trade + in_credit + vix + us_equity + eu_fragm",
|
||||
"description":"Risque géopolitique, sentiment, appétit risque → impact EUR/USD."},
|
||||
# ── Layer 2 : output ──────────────────────────────────────────────────────
|
||||
{"id":"eurusd","label":"EUR/USD — Impact Net","node_type":"output","category":"output","unit":"pips","display_col":3,
|
||||
"formula":"layer_monetary + layer_growth + layer_risk + layer_positioning",
|
||||
"description":"Pression nette cumulée = Σ(4 domaines). Positif = biais haussier EUR/USD."},
|
||||
{"id":"eurusd","label":"EUR/USD — Biais Net","node_type":"output","category":"output","unit":"pips","display_col":3,
|
||||
"formula":"layer_monetary + layer_growth + layer_risk",
|
||||
"description":"Biais net EUR/USD. Positif = haussier EUR. Divisé en 3 piliers : monétaire, croissance, risque."},
|
||||
]
|
||||
},
|
||||
|
||||
@@ -816,6 +973,22 @@ def init_instrument_model_tables(conn):
|
||||
calibration_json TEXT NOT NULL,
|
||||
updated_at TEXT DEFAULT (datetime('now'))
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS node_forecast_scenarios (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
instrument TEXT NOT NULL,
|
||||
node_id TEXT NOT NULL,
|
||||
label TEXT NOT NULL,
|
||||
horizon TEXT NOT NULL DEFAULT 'mt',
|
||||
target_date TEXT NOT NULL,
|
||||
target_value REAL NOT NULL,
|
||||
confidence REAL NOT NULL DEFAULT 0.7,
|
||||
trajectory TEXT NOT NULL DEFAULT 'linear',
|
||||
absorption_days INTEGER NOT NULL DEFAULT 30,
|
||||
notes TEXT,
|
||||
created_at TEXT DEFAULT (datetime('now'))
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_nfs_inst_node
|
||||
ON node_forecast_scenarios(instrument, node_id);
|
||||
""")
|
||||
conn.commit()
|
||||
|
||||
@@ -1251,14 +1424,18 @@ def simulate_timeline(
|
||||
|
||||
from services.causal_graphs import evaluate_graph
|
||||
|
||||
# ── Macro-guidance : noeuds input_manual avec macro_key → overrides time-varying ──
|
||||
# ── Timelines combinées (ff_calendar actuals + scénarios CT/MT/LT) ────────
|
||||
# Pour chaque nœud input_manual : fusion de la baseline ff_calendar et des
|
||||
# scénarios utilisateur (trajectoire blended par confidence vers chaque waypoint)
|
||||
macro_node_timelines: dict[str, dict[str, float]] = {}
|
||||
for node in graph_def.get("nodes", []):
|
||||
mk = node.get("macro_key")
|
||||
if mk and node.get("node_type") == "input_manual":
|
||||
tl = build_macro_node_timeline(conn, mk, date_from, today)
|
||||
if tl:
|
||||
macro_node_timelines[node["id"]] = tl
|
||||
if node.get("node_type") != "input_manual":
|
||||
continue
|
||||
mk = node.get("macro_key")
|
||||
nid = node["id"]
|
||||
tl = build_node_combined_timeline(conn, inst_upper, nid, mk, date_from, today)
|
||||
if tl:
|
||||
macro_node_timelines[nid] = tl
|
||||
|
||||
has_macro = bool(macro_node_timelines)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user