From 1e4455755197663bf9cad0e251658c40b5aa1d0a Mon Sep 17 00:00:00 2001 From: OpenSquared Date: Sun, 28 Jun 2026 15:38:19 +0200 Subject: [PATCH] feat:: causal lab --- backend/routers/causal_lab.py | 96 ++++++++++++++++++ backend/routers/cycle_actions.py | 13 +-- backend/services/instrument_service.py | 6 ++ backend/services/market_event_detector.py | 109 ++++++++++++++------- frontend/src/pages/CycleActions.tsx | 102 +++++++++++++------ frontend/src/pages/InstrumentDashboard.tsx | 18 ++-- frontend/src/pages/MarketEvents.tsx | 61 +++++++++++- 7 files changed, 321 insertions(+), 84 deletions(-) diff --git a/backend/routers/causal_lab.py b/backend/routers/causal_lab.py index 09c41c0..c8e68b7 100644 --- a/backend/routers/causal_lab.py +++ b/backend/routers/causal_lab.py @@ -321,6 +321,11 @@ class RecommendRequest(BaseModel): market_event_id: int +class InstantiateRequest(BaseModel): + market_event_id: int + template_id: int + + class AnalyzeRequest(BaseModel): market_event_id: int template_id: int @@ -635,6 +640,97 @@ def recommend_template(body: RecommendRequest): raise HTTPException(500, str(e)) +@router.post("/api/causal-lab/instantiate") +def instantiate_template(body: InstantiateRequest): + """ + GPT-4o-mini suggère les valeurs des nœuds user_input du template + en fonction des spécificités de l'événement (surprise +/-, magnitude, catégorie…). + Retourne { inputs: { node_id: value }, rationale: str } + """ + try: + from services.database import get_conn + conn = get_conn() + _init(conn) + + ev_row = conn.execute("SELECT * FROM market_events WHERE id = ?", (body.market_event_id,)).fetchone() + tmpl_row = conn.execute("SELECT * FROM causal_graph_templates WHERE id = ?", (body.template_id,)).fetchone() + conn.close() + + if not ev_row or not tmpl_row: + raise HTTPException(404, "event ou template introuvable") + + event = dict(ev_row) + template = dict(tmpl_row) + graph_json = json.loads(template.get("graph_json") or "{}") + input_mapping = graph_json.get("input_mapping", {}) + + # Only user_input nodes need to be suggested (auto-sourced nodes are pulled from DB) + user_nodes = { + k: v for k, v in input_mapping.items() + if not v.get("source") or v["source"] == "user_input" + } + + if not user_nodes: + return {"inputs": {}, "rationale": "Aucun nœud manuel — tout est auto-récupéré depuis la BD"} + + prompt = f"""Tu es un analyste financier. Tu dois configurer un graphe causal pour un événement de marché précis. + +Événement : {event.get('name')} +Catégorie : {event.get('category')} / {event.get('sub_type', '')} +Date : {event.get('start_date')} +Description : {(event.get('description') or '')[:400]} +Score impact : {event.get('impact_score', 0.5)} +Valeur réelle : {event.get('actual_value')} | Valeur attendue : {event.get('expected_value')} | Surprise % : {event.get('surprise_pct')} + +Template : {template.get('name')} (catégorie : {template.get('category')}) + +Nœuds à configurer (entrées manuelles) : +{json.dumps(user_nodes, indent=2, ensure_ascii=False)} + +Règles : +- Surprise NÉGATIVE → valeurs négatives pour les nœuds de surprise +- Surprise POSITIVE → valeurs positives +- Utilise les plages "range" si fournies (ex: [-3, 3] pour z-score) +- Magnitude proportionnelle à l'ampleur de la surprise +- Si surprise_pct disponible, utilise-la pour calibrer (ex: -29.1% → nœud_surprise ≈ -2.5) + +Retourne UNIQUEMENT un JSON : {{ "node_id": valeur_numerique, ... }} +Inclure uniquement les nœuds listés ci-dessus.""" + + client = _openai_client() + resp = client.chat.completions.create( + model="gpt-4o-mini", + messages=[{"role": "user", "content": prompt}], + response_format={"type": "json_object"}, + temperature=0.2, + max_tokens=400, + ) + raw = resp.choices[0].message.content or "{}" + suggested = json.loads(raw) + + # Validate: only keep known nodes with numeric values + validated: dict = {} + for k, v in suggested.items(): + if k in user_nodes: + try: + validated[k] = float(v) + except (TypeError, ValueError): + pass + + surprise = event.get("surprise_pct") + rationale = ( + f"Surprise {'+' if (surprise or 0) >= 0 else ''}{surprise:.1f}%" if surprise is not None + else "Calibration basée sur la description de l'événement" + ) + return {"inputs": validated, "rationale": rationale} + + except HTTPException: + raise + except Exception as e: + logger.error(f"[causal_lab] instantiate: {e}") + raise HTTPException(500, str(e)) + + @router.post("/api/causal-lab/analyze") def analyze_event(body: AnalyzeRequest): """ diff --git a/backend/routers/cycle_actions.py b/backend/routers/cycle_actions.py index 91a9471..ee73e30 100644 --- a/backend/routers/cycle_actions.py +++ b/backend/routers/cycle_actions.py @@ -51,12 +51,13 @@ router = APIRouter(prefix="/api/actions", tags=["Cycle Actions"]) class CheckEventsRequest(BaseModel): sources: Optional[List[str]] = None # ['news','eco','technical','reports'] news_impact_min: float = 0.55 - news_lookback_hours: int = 48 eco_z_threshold: float = 1.5 - eco_days: int = 7 technical_lookback_days: int = 7 report_days: int = 7 report_min_importance: int = 3 + # Unified date window for news + eco (replaces eco_days + news_lookback_hours) + date_from: Optional[str] = None # ISO date, e.g. "2026-06-21" + date_to: Optional[str] = None # ISO date, e.g. "2026-06-28" class ActionResult(BaseModel): @@ -151,12 +152,12 @@ def check_market_events(req: CheckEventsRequest): result = check_new_market_events( sources=req.sources, news_impact_min=req.news_impact_min, - news_lookback_hours=req.news_lookback_hours, eco_z_threshold=req.eco_z_threshold, - eco_days=req.eco_days, technical_lookback_days=req.technical_lookback_days, report_days=req.report_days, report_min_importance=req.report_min_importance, + date_from=req.date_from, + date_to=req.date_to, ) except Exception as e: _release("check-market-events") @@ -204,12 +205,12 @@ def check_market_events_background( check_new_market_events( sources=req.sources, news_impact_min=req.news_impact_min, - news_lookback_hours=req.news_lookback_hours, eco_z_threshold=req.eco_z_threshold, - eco_days=req.eco_days, technical_lookback_days=req.technical_lookback_days, report_days=req.report_days, report_min_importance=req.report_min_importance, + date_from=req.date_from, + date_to=req.date_to, ) except Exception as e: logger.error(f"[action/check-market-events/bg] {e}") diff --git a/backend/services/instrument_service.py b/backend/services/instrument_service.py index f3d4b11..5df533e 100644 --- a/backend/services/instrument_service.py +++ b/backend/services/instrument_service.py @@ -528,9 +528,14 @@ def _get_relevant_events( """ try: from services.database import get_conn + from services.causal_graphs import init_tables conn = get_conn() + init_tables(conn) # ensure causal_event_analyses exists rows = conn.execute(""" SELECT e.*, + (SELECT GROUP_CONCAT(DISTINCT a.instrument) + FROM causal_event_analyses a + WHERE a.market_event_id = e.id) AS analyzed_instruments, (SELECT a.template_id FROM causal_event_analyses a WHERE a.market_event_id = e.id ORDER BY a.id DESC LIMIT 1) AS template_id, (SELECT a.id FROM causal_event_analyses a WHERE a.market_event_id = e.id ORDER BY a.id DESC LIMIT 1) AS analysis_id FROM market_events e @@ -581,6 +586,7 @@ def _get_relevant_events( filtered.append({ "id": ev.get("id"), "template_id": ev.get("template_id"), + "analyzed_instruments": ev.get("analyzed_instruments"), # comma-sep, e.g. "EURUSD,SP500" "date": ev_start, "end_date": ev.get("end_date") or None, "title": ev.get("name") or ev.get("event_name") or "", diff --git a/backend/services/market_event_detector.py b/backend/services/market_event_detector.py index c8aa597..36419d3 100644 --- a/backend/services/market_event_detector.py +++ b/backend/services/market_event_detector.py @@ -187,12 +187,13 @@ Réponds JSON: {{"is_duplicate": true/false, "reason": "courte phrase"}}""" def _check_news(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]: from services.data_fetcher import fetch_geo_news - min_impact = float(desk_cfg.get("min_impact", 0.55)) - lookback_hours = int(desk_cfg.get("lookback_hours", 48)) - max_evaluate = int(desk_cfg.get("max_evaluate", 15)) - dedup_enabled = bool(desk_cfg.get("dedup_enabled", True)) - dedup_days = int(desk_cfg.get("dedup_lookback_days", 2)) - system_prompt = desk_cfg.get("_system_prompt", "") + min_impact = float(desk_cfg.get("min_impact", 0.55)) + max_evaluate = int(desk_cfg.get("max_evaluate", 15)) + dedup_enabled = bool(desk_cfg.get("dedup_enabled", True)) + dedup_days = int(desk_cfg.get("dedup_lookback_days", 2)) + system_prompt = desk_cfg.get("_system_prompt", "") + date_from = desk_cfg.get("date_from") + date_to = desk_cfg.get("date_to") api_key = _get_api_key() if not api_key: @@ -205,14 +206,22 @@ def _check_news(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]: logger.warning(f"[check_events/news] fetch failed: {e}") return [] - cutoff_dt = datetime.utcnow() - timedelta(hours=lookback_hours) + # Build cutoff from date_from; date_to used as upper bound + try: + cutoff_from = datetime.fromisoformat(date_from) if date_from else datetime.utcnow() - timedelta(days=7) + cutoff_to = datetime.fromisoformat(date_to) if date_to else datetime.utcnow() + except Exception: + cutoff_from = datetime.utcnow() - timedelta(days=7) + cutoff_to = datetime.utcnow() + candidates = [] for n in all_news: if (n.get("impact_score") or 0) < min_impact: continue pub_date = _parse_date(n.get("date", "")) try: - if datetime.fromisoformat(pub_date) < cutoff_dt: + pub_dt = datetime.fromisoformat(pub_date) + if pub_dt < cutoff_from or pub_dt > cutoff_to: continue except Exception: pass @@ -329,7 +338,8 @@ FORMAT JSON STRICT: def _check_ff_calendar_surprises( currencies: List[str], min_impact: str, - days: int, + date_from: str, + date_to: str, min_surprise_pct: float, lookback_releases: int, create_evt: bool, @@ -347,7 +357,6 @@ def _check_ff_calendar_surprises( "low": ("high", "medium", "low"), } allowed_impacts = impact_map.get(min_impact, ("high", "medium")) - cutoff = (datetime.utcnow() - timedelta(days=days)).strftime("%Y-%m-%d") try: conn = get_conn() @@ -360,11 +369,12 @@ def _check_ff_calendar_surprises( WHERE currency IN ({ccy_ph}) AND impact IN ({imp_ph}) AND event_date >= ? + AND event_date <= ? AND actual_value IS NOT NULL AND forecast_value IS NOT NULL ORDER BY event_date DESC LIMIT 200""", - (*currencies, *allowed_impacts, cutoff), + (*currencies, *allowed_impacts, date_from, date_to), ).fetchall() conn.close() except Exception as e: @@ -457,28 +467,45 @@ def _check_ff_calendar_surprises( def _check_eco(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]: - from services.database import get_recent_economic_surprises, get_conn + from services.database import get_conn z_threshold = float(desk_cfg.get("z_threshold", 1.5)) - days = int(desk_cfg.get("days", 7)) + date_from = desk_cfg.get("date_from") or (datetime.utcnow() - timedelta(days=7)).strftime("%Y-%m-%d") + date_to = desk_cfg.get("date_to") or datetime.utcnow().strftime("%Y-%m-%d") currencies = list(desk_cfg.get("currencies") or ["USD", "EUR", "GBP", "JPY"]) min_impact = str(desk_cfg.get("min_impact", "medium")).lower() create_evt = bool(desk_cfg.get("create_market_event", True)) lookback_releases = int(desk_cfg.get("lookback_releases", 3)) - min_rank = _IMPACT_RANKS.get(min_impact, 2) - # ff_calendar surprise threshold: z_threshold used as rough proxy (×10 → % equivalent) - ff_surprise_min = max(10.0, z_threshold * 10) + min_rank = _IMPACT_RANKS.get(min_impact, 2) + ff_surprise_min = max(10.0, z_threshold * 10) existing = _existing_event_keys() created: List[Dict] = [] - # ── FRED path (USD only) ────────────────────────────────────────────────── + # ── Local economic_events table (replaces FRED API call) ───────────────── if "USD" in currencies: try: - releases = get_recent_economic_surprises(days=days, min_zscore=z_threshold) + conn = get_conn() + rows = conn.execute( + """SELECT * FROM economic_events + WHERE event_date >= ? AND event_date <= ? + AND ABS(COALESCE(surprise_zscore, 0)) >= ? + ORDER BY event_date DESC, ABS(COALESCE(surprise_zscore, 0)) DESC + LIMIT 50""", + (date_from, date_to, z_threshold), + ).fetchall() + conn.close() + releases = [] + for r in rows: + d = dict(r) + try: + d["assets_impacted"] = json.loads(d.get("assets_impacted") or "[]") + except Exception: + d["assets_impacted"] = [] + releases.append(d) except Exception as e: - logger.warning(f"[check_events/eco] FRED query failed: {e}") + logger.warning(f"[check_events/eco] local query failed: {e}") releases = [] for rel in releases: @@ -572,7 +599,8 @@ def _check_eco(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]: created += _check_ff_calendar_surprises( currencies=non_usd, min_impact=min_impact, - days=days, + date_from=date_from, + date_to=date_to, min_surprise_pct=ff_surprise_min, lookback_releases=lookback_releases, create_evt=create_evt, @@ -860,13 +888,14 @@ def _check_fundamental(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]: """Corporate fundamental events: layoffs, M&A, earnings, credit, regulatory.""" from services.data_fetcher import fetch_geo_news - min_impact = float(desk_cfg.get("min_impact", 0.45)) - lookback_hours = int(desk_cfg.get("lookback_hours", 72)) - max_evaluate = int(desk_cfg.get("max_evaluate", 20)) - dedup_enabled = bool(desk_cfg.get("dedup_enabled", True)) - dedup_days = int(desk_cfg.get("dedup_lookback_days", 3)) - system_prompt = desk_cfg.get("_system_prompt", "") - focus_types = desk_cfg.get("focus_types", ["layoffs","earnings","ma","credit","regulatory","guidance"]) + min_impact = float(desk_cfg.get("min_impact", 0.45)) + max_evaluate = int(desk_cfg.get("max_evaluate", 20)) + dedup_enabled = bool(desk_cfg.get("dedup_enabled", True)) + dedup_days = int(desk_cfg.get("dedup_lookback_days", 3)) + system_prompt = desk_cfg.get("_system_prompt", "") + focus_types = desk_cfg.get("focus_types", ["layoffs","earnings","ma","credit","regulatory","guidance"]) + date_from = desk_cfg.get("date_from") or (datetime.utcnow() - timedelta(days=7)).strftime("%Y-%m-%d") + date_to = desk_cfg.get("date_to") or datetime.utcnow().strftime("%Y-%m-%d") api_key = _get_api_key() if not api_key: @@ -878,11 +907,10 @@ def _check_fundamental(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]: logger.warning(f"[check_events/fundamental] fetch failed: {e}") return [] - cutoff_dt = datetime.utcnow() - timedelta(hours=lookback_hours) candidates = [ n for n in all_news if (n.get("impact_score") or 0) >= min_impact - and _parse_date(n.get("date", "")) >= cutoff_dt.strftime("%Y-%m-%d") + and date_from <= _parse_date(n.get("date", "")) <= date_to ][:max_evaluate] if not candidates: @@ -1617,11 +1645,12 @@ def _check_macro_gauges(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]: def check_new_market_events( sources: Optional[List[str]] = None, + # Unified date window — overrides eco_days/news_lookback_hours when provided + date_from: Optional[str] = None, + date_to: Optional[str] = None, # Legacy overrides — used when called without an active desk news_impact_min: float = 0.55, - news_lookback_hours: int = 48, eco_z_threshold: float = 1.5, - eco_days: int = 7, technical_lookback_days: int = 7, report_days: int = 7, report_min_importance: int = 3, @@ -1655,17 +1684,27 @@ def check_new_market_events( cfg["_system_prompt"] = desk.get("system_prompt") or "" return cfg + # Compute default date_from fallback from desk config or 7-day default + _now = datetime.utcnow() + _date_to = date_to or _now.strftime("%Y-%m-%d") + _date_from = date_from or (_now - timedelta(days=7)).strftime("%Y-%m-%d") + news_cfg = _desk_cfg(news_desk, { - "min_impact": news_impact_min, "lookback_hours": news_lookback_hours, + "min_impact": news_impact_min, "max_evaluate": 15, "dedup_enabled": False, "dedup_lookback_days": 2, }) + news_cfg["date_from"] = _date_from + news_cfg["date_to"] = _date_to + fundamental_cfg = _desk_cfg(fundamental_desk, { - "min_impact": 0.45, "lookback_hours": 72, "max_evaluate": 20, + "min_impact": 0.45, "max_evaluate": 20, "dedup_enabled": True, "dedup_lookback_days": 3, }) + fundamental_cfg["date_from"] = _date_from + fundamental_cfg["date_to"] = _date_to + eco_cfg = _desk_cfg(eco_desk, { "z_threshold": eco_z_threshold, - "days": eco_days, "gauge_signals": { "regime_transition": {"enabled": True}, "yield_curve_inversion": {"enabled": True, "threshold": 0.0}, @@ -1674,6 +1713,8 @@ def check_new_market_events( "gold_copper_ratio": {"enabled": True, "fear_threshold": 700, "growth_threshold": 500}, }, }) + eco_cfg["date_from"] = _date_from + eco_cfg["date_to"] = _date_to tech_cfg = _desk_cfg(tech_desk, { "lookback_days": technical_lookback_days, "signals": { diff --git a/frontend/src/pages/CycleActions.tsx b/frontend/src/pages/CycleActions.tsx index 1bf6135..f35d4aa 100644 --- a/frontend/src/pages/CycleActions.tsx +++ b/frontend/src/pages/CycleActions.tsx @@ -122,27 +122,44 @@ function BootstrapAction({ action }: { action: ActionDef }) { interface CheckEventsConfig { sources: string[] news_impact_min: number - news_lookback_hours: number eco_z_threshold: number - eco_days: number technical_lookback_days: number report_days: number report_min_importance: number + date_from: string + date_to: string } -const DEFAULT_CONFIG: CheckEventsConfig = { - sources: ['news', 'eco', 'technical', 'reports'], - news_impact_min: 0.55, - news_lookback_hours: 48, - eco_z_threshold: 1.5, - eco_days: 7, - technical_lookback_days: 7, - report_days: 7, - report_min_importance: 3, +const SCAN_PRESETS = [ + { label: '6h', hours: 6 }, + { label: '12h', hours: 12 }, + { label: '24h', hours: 24 }, + { label: '48h', hours: 48 }, + { label: '72h', hours: 72 }, + { label: '7j', hours: 168 }, +] + +function presetDates(hours: number): { date_from: string; date_to: string } { + const now = new Date() + const from = new Date(now.getTime() - hours * 3_600_000) + const fmt = (d: Date) => d.toISOString().slice(0, 10) + return { date_from: fmt(from), date_to: fmt(now) } +} + +function makeDefaultConfig(): CheckEventsConfig { + return { + sources: ['news', 'eco', 'technical', 'reports'], + news_impact_min: 0.55, + eco_z_threshold: 1.5, + technical_lookback_days: 7, + report_days: 7, + report_min_importance: 3, + ...presetDates(48), + } } function CheckEventsPanel() { - const [config, setConfig] = useState(DEFAULT_CONFIG) + const [config, setConfig] = useState(makeDefaultConfig) const [running, setRunning] = useState(false) const [result, setResult] = useState(null) const [error, setError] = useState(null) @@ -215,14 +232,46 @@ function CheckEventsPanel() { {showConfig && ( -
+
+ + {/* Fenêtre de scan */} +
+
Fenêtre de scan (news + éco)
+
+ {SCAN_PRESETS.map(p => { + const { date_from, date_to } = presetDates(p.hours) + const active = config.date_from === date_from && config.date_to === date_to + return ( + + ) + })} +
+
+ setConfig(c => ({ ...c, date_from: e.target.value }))} + className="bg-dark-900 border border-slate-700 rounded px-2 py-1 text-slate-200 text-xs flex-1" /> + → + setConfig(c => ({ ...c, date_to: e.target.value }))} + className="bg-dark-900 border border-slate-700 rounded px-2 py-1 text-slate-200 text-xs flex-1" /> +
+
+ + {/* Sources */}
Sources actives
{['news', 'eco', 'technical', 'reports'].map(s => ( -
-
+ + {/* Advanced */} +
-
diff --git a/frontend/src/pages/InstrumentDashboard.tsx b/frontend/src/pages/InstrumentDashboard.tsx index c90f639..61281a4 100644 --- a/frontend/src/pages/InstrumentDashboard.tsx +++ b/frontend/src/pages/InstrumentDashboard.tsx @@ -31,6 +31,7 @@ interface LinePoint { time: string; value: number } interface SnapshotEvent { id?: number; template_id?: number | null + analyzed_instruments?: string | null // comma-sep: "EURUSD,SP500" — set when causal analysis exists date: string; end_date: string | null; title: string; level: string category: string; sub_type?: string; description: string; impact_score: number expected_value?: string | null; actual_value?: string | null @@ -686,11 +687,12 @@ function EventTimeline({ return () => obs.disconnect() }, []) - // Only events that have a causal graph linked to this instrument + // Only events that have an instantiated analysis for this instrument + // Uses analyzed_instruments (actual DB analyses) — not template.instruments (theoretical) const linked = events.filter(ev => { - if (!ev.template_id) return false - const tmpl = templates.find(t => t.id === ev.template_id) - return tmpl != null && causalInsts.some(ci => tmpl.instruments.includes(ci)) + if (!ev.analyzed_instruments) return false + const insts = ev.analyzed_instruments.split(',') + return causalInsts.some(ci => insts.includes(ci)) }) if (!priceData.length || !linked.length) { @@ -781,11 +783,11 @@ function EventGraphCards({ }) { const navigate = useNavigate() - // Events that have a causal graph template touching this instrument + // Events that have an instantiated causal analysis for this instrument const linkedEvents = events.filter(ev => { - if (!ev.template_id) return false - const tmpl = templates.find(t => t.id === ev.template_id) - return tmpl != null && causalInsts.some(ci => tmpl.instruments.includes(ci)) + if (!ev.analyzed_instruments) return false + const insts = ev.analyzed_instruments.split(',') + return causalInsts.some(ci => insts.includes(ci)) }) // Unique templates from those linked events diff --git a/frontend/src/pages/MarketEvents.tsx b/frontend/src/pages/MarketEvents.tsx index b00726c..ea87d90 100644 --- a/frontend/src/pages/MarketEvents.tsx +++ b/frontend/src/pages/MarketEvents.tsx @@ -335,8 +335,11 @@ function EventDetail({ const [selTmpl, setSelTmpl] = useState(0) const [instrument, setInstrument] = useState('EURUSD') const [loadingRec, setLoadingRec] = useState(false) + const [loadingInst, setLoadingInst] = useState(false) const [loadingAn, setLoadingAn] = useState(false) const [recMsg, setRecMsg] = useState(null) + const [instInputs, setInstInputs] = useState>({}) + const [instMsg, setInstMsg] = useState(null) const [anResult, setAnResult] = useState<{ score: number | null; preds: Record } | null>(null) const loadDetail = useCallback(async () => { @@ -409,8 +412,24 @@ function EventDetail({ onClose() } + const instantiate = async (tmplId: number) => { + if (!tmplId) return + setLoadingInst(true); setInstMsg(null); setInstInputs({}) + try { + const r = await fetch('/api/causal-lab/instantiate', { + method: 'POST', headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ market_event_id: detail.id, template_id: tmplId }), + }) + const d = await r.json() + if (!r.ok) throw new Error(d.detail || r.statusText) + setInstInputs(d.inputs || {}) + setInstMsg(d.rationale || null) + } catch (e: any) { setInstMsg(`✗ ${(e as any).message}`) } + finally { setLoadingInst(false) } + } + const recommendTemplate = async () => { - setLoadingRec(true); setRecMsg(null); setAnResult(null) + setLoadingRec(true); setRecMsg(null); setAnResult(null); setInstInputs({}); setInstMsg(null) try { const r = await fetch('/api/causal-lab/recommend', { method: 'POST', headers: { 'Content-Type': 'application/json' }, @@ -422,10 +441,11 @@ function EventDetail({ if (rec?.template_id) { setSelTmpl(rec.template_id) setRecMsg(`✓ ${rec.template_name}${rec.confidence ? ` (${Math.round(rec.confidence * 100)}%)` : ''}`) + await instantiate(rec.template_id) } else { - setRecMsg('Aucun template correspondant — créez-en un dans l\'Editeur') + setRecMsg('Aucun template — créez-en un dans l\'Editeur') } - } catch (e: any) { setRecMsg(`✗ ${e.message}`) } + } catch (e: any) { setRecMsg(`✗ ${(e as any).message}`) } finally { setLoadingRec(false) } } @@ -435,13 +455,13 @@ function EventDetail({ try { const r = await fetch('/api/causal-lab/analyze', { method: 'POST', headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ market_event_id: detail.id, template_id: selTmpl, instrument, inputs: {}, coef_overrides: {} }), + body: JSON.stringify({ market_event_id: detail.id, template_id: selTmpl, instrument, inputs: instInputs, coef_overrides: {} }), }) const d = await r.json() if (!r.ok) throw new Error(d.detail || r.statusText) setAnResult({ score: d.activation?.score ?? null, preds: d.node_values ?? {} }) await loadAnalyses() - } catch (e: any) { setAnResult({ score: null, preds: { error: -1 } }) } + } catch (e: any) { setAnResult({ score: null, preds: {} }) } finally { setLoadingAn(false) } } @@ -738,6 +758,37 @@ function EventDetail({
)} + {/* Instantiation status + inputs preview */} + {selTmpl > 0 && ( +
+
+ + {instMsg && !instMsg.startsWith('✗') && ( + {instMsg} + )} +
+ {Object.keys(instInputs).length > 0 && ( +
+ {Object.entries(instInputs).map(([k, v]) => ( + 0 ? 'text-emerald-400 border-emerald-800/50 bg-emerald-900/20' : v < 0 ? 'text-red-400 border-red-800/50 bg-red-900/20' : 'text-slate-500 border-slate-700/50' + )}> + {k}: {v > 0 ? '+' : ''}{v.toFixed(2)} + + ))} +
+ )} +
+ )} + {/* Instrument + analyze */}