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:

backend/services/database.py

@@ -169,6 +169,23 @@ def init_db():
         "ALTER TABLE wavelet_watchlist_signals ADD COLUMN energy REAL",
         "ALTER TABLE wavelet_watchlist_signals ADD COLUMN ridge_period_days REAL",
         "ALTER TABLE wavelet_watchlist_signals ADD COLUMN params_json TEXT",
+        # Cycle Actions — standalone "refresh-price-data" action, OHLCV cache
+        """CREATE TABLE IF NOT EXISTS price_data_cache (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            ticker TEXT NOT NULL,
+            date TEXT NOT NULL,
+            open REAL, high REAL, low REAL, close REAL, volume REAL,
+            cached_at TEXT DEFAULT (datetime('now')),
+            UNIQUE(ticker, date)
+        )""",
+        # Cycle Actions — standalone "compute-indicators" action, one row per call per ticker
+        """CREATE TABLE IF NOT EXISTS instrument_indicators (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            ticker TEXT NOT NULL,
+            computed_at TEXT DEFAULT (datetime('now')),
+            horizon_days INTEGER,
+            indicators_json TEXT DEFAULT '{}'
+        )""",
     ]:
         try:
             c.execute(_sql)
@@ -183,6 +200,14 @@ def init_db():
         c.execute("CREATE INDEX IF NOT EXISTS idx_atp_session_status ON ai_trade_proposals(session_id, status)")
     except Exception:
         pass
+    try:
+        c.execute("CREATE INDEX IF NOT EXISTS idx_pdc_ticker_date ON price_data_cache(ticker, date DESC)")
+    except Exception:
+        pass
+    try:
+        c.execute("CREATE INDEX IF NOT EXISTS idx_ii_ticker_date ON instrument_indicators(ticker, computed_at DESC)")
+    except Exception:
+        pass
 
     try:
         c.execute("CREATE INDEX IF NOT EXISTS idx_chat_session_date ON ai_chat_messages(session_id, created_at)")
@@ -3441,6 +3466,72 @@ def resolve_ai_trade_proposal(proposal_id: str, status: str, portfolio_id: Optio
     conn.close()
 
 
+# ── Cycle Actions — standalone price data cache + indicator snapshots ──────────
+
+def upsert_price_data(ticker: str, rows: List[Dict[str, Any]]) -> int:
+    """rows: [{date, open, high, low, close, volume}, ...]. Returns rows written."""
+    if not rows:
+        return 0
+    conn = get_conn()
+    n = 0
+    for r in rows:
+        conn.execute(
+            """INSERT INTO price_data_cache (ticker, date, open, high, low, close, volume, cached_at)
+               VALUES (?, ?, ?, ?, ?, ?, ?, datetime('now'))
+               ON CONFLICT(ticker, date) DO UPDATE SET
+                 open=excluded.open, high=excluded.high, low=excluded.low,
+                 close=excluded.close, volume=excluded.volume, cached_at=excluded.cached_at""",
+            (ticker.upper(), r.get("date"), r.get("open"), r.get("high"), r.get("low"), r.get("close"), r.get("volume")),
+        )
+        n += 1
+    conn.commit()
+    conn.close()
+    return n
+
+
+def get_cached_price_data(ticker: str, limit: int = 90) -> List[Dict[str, Any]]:
+    conn = get_conn()
+    rows = conn.execute(
+        "SELECT * FROM price_data_cache WHERE ticker=? ORDER BY date DESC LIMIT ?",
+        (ticker.upper(), limit),
+    ).fetchall()
+    conn.close()
+    return [dict(r) for r in rows]
+
+
+def save_instrument_indicators(ticker: str, horizon_days: int, indicators: Dict[str, Any]) -> None:
+    conn = get_conn()
+    conn.execute(
+        "INSERT INTO instrument_indicators (ticker, horizon_days, indicators_json) VALUES (?, ?, ?)",
+        (ticker.upper(), horizon_days, json.dumps(indicators)),
+    )
+    conn.commit()
+    conn.close()
+
+
+def get_latest_instrument_indicators() -> List[Dict[str, Any]]:
+    """Most recent indicators row per ticker."""
+    conn = get_conn()
+    rows = conn.execute(
+        """SELECT i.* FROM instrument_indicators i
+           INNER JOIN (
+               SELECT ticker, MAX(computed_at) AS max_computed_at
+               FROM instrument_indicators GROUP BY ticker
+           ) latest ON i.ticker = latest.ticker AND i.computed_at = latest.max_computed_at
+           ORDER BY i.ticker"""
+    ).fetchall()
+    conn.close()
+    out = []
+    for r in rows:
+        d = dict(r)
+        try:
+            d["indicators"] = json.loads(d.pop("indicators_json") or "{}")
+        except Exception:
+            d["indicators"] = {}
+        out.append(d)
+    return out
+
+
 # ── System Logs ───────────────────────────────────────────────────────────────
 
 def log_system_event(

backend/services/ma_analyzer.py

@@ -495,14 +495,15 @@ def _to_event_dict(ev: Dict[str, Any]) -> Dict[str, Any]:
     }
 
 
-async def bootstrap_ma_events() -> Dict[str, Any]:
+async def bootstrap_ma_events(force: bool = False) -> Dict[str, Any]:
     """
     Main entrypoint. Detects MA ruptures, enriches with GPT, saves to DB.
-    Idempotent: skips if DB already has > 100 events.
+    Idempotent by default: skips if DB already has > 100 events. Pass force=True
+    to bypass this guard (same convention as bootstrap_macro_events/bootstrap_eco_events).
     Returns {"detected": N, "saved": N, "skipped": N}.
     """
     existing = count_market_events()
-    if existing > 100:
+    if existing > 100 and not force:
         logger.info(f"[MA] DB already has {existing} events — skipping bootstrap")
         return {"detected": 0, "saved": 0, "skipped": existing}
 

backend/services/price_cache.py

@@ -0,0 +1,41 @@
+"""
+Cycle Actions — standalone "refresh-price-data" action.
+
+Downloads fresh OHLCV for every watchlist instrument and caches it in
+price_data_cache. Deliberately independent from the technical-indicators /
+wavelet code paths (which each do their own live yfinance fetch) — this is an
+inspection/refresh tool for decomposing a cycle, not a shared cache other
+steps depend on, so it can't regress the already-tested live cycle path.
+"""
+import logging
+from typing import Any, Dict
+
+logger = logging.getLogger(__name__)
+
+
+def refresh_watchlist_price_data(period: str = "3mo") -> Dict[str, Any]:
+    from services.database import get_instruments_watchlist, upsert_price_data
+    from services.data_fetcher import get_historical
+
+    tickers = [w["ticker"] for w in get_instruments_watchlist()]
+    per_ticker: Dict[str, int] = {}
+    failed = []
+
+    for ticker in tickers:
+        try:
+            rows = get_historical(ticker, period=period, interval="1d")
+            if not rows:
+                failed.append(ticker)
+                continue
+            n = upsert_price_data(ticker, rows)
+            per_ticker[ticker] = n
+        except Exception as e:
+            logger.warning(f"[PriceCache] Failed to refresh {ticker}: {e}")
+            failed.append(ticker)
+
+    return {
+        "tickers_refreshed": len(per_ticker),
+        "rows_written": sum(per_ticker.values()),
+        "per_ticker": per_ticker,
+        "failed": failed,
+    }

backend/services/technical_indicators.py

@@ -175,3 +175,23 @@ def format_indicators_for_prompt(indicators: dict) -> str:
     if "error" in indicators:
         return ""
     return indicators.get("prompt_block", "")
+
+
+def compute_and_save_indicators(horizon_days: int = 45) -> dict:
+    """Cycle Actions — standalone "compute-indicators" action. compute_indicators()
+    itself is pure (no persistence) — this loops the watchlist and persists each
+    result into instrument_indicators (which nothing else reads from yet; this is
+    an inspection snapshot, not a cache other steps depend on)."""
+    from services.database import get_instruments_watchlist, save_instrument_indicators
+
+    tickers = [w["ticker"] for w in get_instruments_watchlist()]
+    computed = 0
+    failed = []
+    for ticker in tickers:
+        result = compute_indicators(ticker, horizon_days=horizon_days)
+        if "error" in result:
+            failed.append(ticker)
+            continue
+        save_instrument_indicators(ticker, horizon_days, result)
+        computed += 1
+    return {"tickers_computed": computed, "failed": failed, "horizon_days": horizon_days}