feat: cycle

backend/services/auto_cycle.py

@@ -1754,6 +1754,79 @@ Réponds en JSON avec ce schéma EXACT:
     return report
 
 
+def generate_standalone_report() -> Dict[str, Any]:
+    """Cycle Actions — standalone "generate-report" action. _generate_cycle_report()
+    itself is NOT modified (too tightly coupled to run_cycle_once()'s in-memory
+    state to safely change) — instead every one of its parameters is
+    reconstructed from an independent, DB-or-live source:
+      - scored          <- get_last_scores() (config key "last_pattern_scores",
+                            filled by save_pattern_scores() every real cycle)
+      - news/geo_score  <- same live sequence as cycle Step 1
+      - dominant/scenarios/gauges <- same sequence as the update-regime action
+      - commentary      <- _generate_cycle_commentary(), independently callable
+      - wavelet_signals <- compute_and_save_wavelet_signals(), independently callable
+      - portfolio_monitor <- analyze_simulation_portfolio() (+ AI only if alerts)
+      - added_patterns/options_assessment <- cycle-only artifacts, not
+        reconstructible after the fact -> empty/None (report is honest, just
+        thinner on these two fields than a live cycle's report)."""
+    from services.database import get_config, get_pattern_scores, save_cycle_report
+    from services.data_fetcher import fetch_geo_news, get_macro_gauges, score_macro_scenarios
+    from services.geo_analyzer import compute_geo_risk_score
+    from services.ai_analyzer import ai_score_news_batch, ai_score_geo_risk
+
+    run_id = datetime.utcnow().isoformat()
+    ai_key = get_config("openai_api_key") or ""
+
+    last_scores = get_pattern_scores()
+    scored = last_scores.get("scores") or []
+    scoring_run_id = last_scores.get("run_id") or run_id
+
+    news = ai_score_news_batch(fetch_geo_news())
+    algo_geo = compute_geo_risk_score(news)
+    try:
+        geo_obj = ai_score_geo_risk(news, algo_geo, log_meta={"run_id": run_id, "call_type": "geo_risk_score"})
+    except Exception:
+        geo_obj = algo_geo
+    geo_score_val = int(round(geo_obj.get("score") or 0))
+
+    gauges = get_macro_gauges()
+    scenarios = score_macro_scenarios(gauges)
+    dominant = scenarios.get("dominant", "incertain")
+
+    commentary = None
+    try:
+        commentary = _generate_cycle_commentary(scored, dominant, scenarios, geo_score_val, news, gauges)
+    except Exception as e:
+        logger.warning(f"[StandaloneReport] Commentary generation failed: {e}")
+
+    wavelet_signals: List[Dict] = []
+    try:
+        from services.wavelet_signals import compute_and_save_wavelet_signals
+        wavelet_signals = compute_and_save_wavelet_signals(run_id)
+    except Exception as e:
+        logger.warning(f"[StandaloneReport] Wavelet scan failed (non-blocking): {e}")
+
+    portfolio_monitor = None
+    try:
+        from services.portfolio_risk import analyze_simulation_portfolio
+        risk = analyze_simulation_portfolio()
+        if risk.get("alerts"):
+            portfolio_monitor = _run_portfolio_monitor(risk, run_id)
+    except Exception as e:
+        logger.warning(f"[StandaloneReport] Portfolio monitor failed (non-blocking): {e}")
+
+    report = _generate_cycle_report(
+        run_id=run_id, scored=scored, dominant=dominant, scenarios=scenarios,
+        geo_score_val=geo_score_val, news=news, gauges=gauges, ai_key=ai_key,
+        added_patterns=[], scoring_run_id=scoring_run_id,
+        portfolio_monitor=portfolio_monitor, commentary=commentary,
+        options_assessment=None, wavelet_signals=wavelet_signals,
+    )
+    if report:
+        save_cycle_report(run_id, report)
+    return {"run_id": run_id, "report": report}
+
+
 # ── Auto portfolio snapshot ───────────────────────────────────────────────────
 
 def _auto_portfolio_snapshot(ai_key: str) -> None: