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feat: cycle

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OpenSquared
2026-07-15 14:59:11 +02:00
parent 2d474c9194
commit 16ccc7c2c7
6 changed files with 466 additions and 45 deletions
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279
backend/routers/cycle_actions.py
View File

@@ -1,39 +1,19 @@
"""
Isolated Cycle Actions — callable individually from UI or cron.
Isolated Cycle Actions — callable individually from UI or cron, to decompose
a full auto-cycle into steps runnable/inspectable one at a time.
Decomposition of the full auto_cycle into 8 independent actions:
ACTION 1 — check-market-events ← IMPLEMENTED HERE
Sources: RSS news + FRED eco surprises + MA crossovers + institutional reports
Output : new market_events created in DB
ACTION 2 — refresh-price-data (planned)
Sources: yfinance OHLCV for all watchlist instruments
Output : price_data table updated
ACTION 3 — compute-indicators (planned)
Sources: price_data in DB
Output : ma20/50/100/200 + RSI + BB + ATR stored in instrument_indicators
ACTION 4 — evaluate-impacts (planned)
Sources: market_events without impact scores
Output : GPT-evaluated impact_score + affected_assets stored
ACTION 5 — generate-signals (planned)
Sources: indicators + market_events + portfolio
Output : trading signals for each instrument
ACTION 6 — update-regime (planned)
Sources: signals + macro context
Output : current macro_regime classification
ACTION 7 — snapshot-portfolio (planned)
Sources: portfolio positions + current prices
Output : risk metrics (VaR, PnL, delta) snapshot stored
ACTION 8 — generate-report (planned)
Sources: all of the above
Output : daily cycle report PDF/JSON
ACTION 1 — check-market-events : RSS news + FRED surprises + MA crossovers + institutional reports -> new market_events
ACTION 2 — bootstrap-macro : seed historical macro/geopolitical events (idempotent)
ACTION 3 — bootstrap-eco : seed economic calendar events (idempotent)
ACTION 4 — bootstrap-categories : seed default impact categories (idempotent)
ACTION 5 — bootstrap-ma : historical MA50/100/200 crossover detection -> market_events (idempotent, >100 events skip unless force)
ACTION 6 — refresh-price-data : yfinance OHLCV for the watchlist -> price_data_cache
ACTION 7 — compute-indicators : MA/RSI/BB/ATR per watchlist ticker -> instrument_indicators
ACTION 8 — evaluate-impacts : AI-score market_events not yet in instrument_impacts
ACTION 9 — generate-signals : technical desk signals (check-market-events sources=[technical]) + wavelet scan
ACTION 10 — update-regime : macro gauges -> scenario scoring -> regime history -> cluster reclassification
ACTION 11 — snapshot-portfolio : VaR + PnL snapshot (same functions as /api/var/run-now + /api/var/pnl/run-now)
ACTION 12 — generate-report : best-effort standalone cycle report (see services.auto_cycle.generate_standalone_report)
"""
import logging
from datetime import datetime
@@ -122,14 +102,14 @@ def list_actions():
"implemented": True,
"group": "bootstrap",
},
{"id": "bootstrap-ma", "label": "Bootstrap MA Crossovers", "description": "Detect historical MA50/100/200 crossovers for all instruments.", "status": "idle", "implemented": False, "group": "bootstrap"},
{"id": "refresh-price-data", "label": "Refresh Price Data", "description": "Download fresh OHLCV for all watchlist instruments.", "status": "idle", "implemented": False, "group": "data"},
{"id": "compute-indicators", "label": "Compute Indicators", "description": "Recalculate MA/RSI/BB/ATR for all instruments.", "status": "idle", "implemented": False, "group": "data"},
{"id": "evaluate-impacts", "label": "Evaluate Impacts", "description": "AI-score market_events that lack impact_score.", "status": "idle", "implemented": False, "group": "ai"},
{"id": "generate-signals", "label": "Generate Signals", "description": "Compute trading signals from indicators + events.", "status": "idle", "implemented": False, "group": "ai"},
{"id": "update-regime", "label": "Update Regime", "description": "Re-classify the current macro regime.", "status": "idle", "implemented": False, "group": "ai"},
{"id": "snapshot-portfolio", "label": "Snapshot Portfolio", "description": "Store a risk snapshot (VaR, PnL, delta).", "status": "idle", "implemented": False, "group": "portfolio"},
{"id": "generate-report", "label": "Generate Report", "description": "Build and persist the daily cycle report.", "status": "idle", "implemented": False, "group": "portfolio"},
{"id": "bootstrap-ma", "label": "Bootstrap MA Crossovers", "description": "Detect historical MA50/100/200 crossovers for all instruments.", "status": "running" if _running.get("bootstrap-ma") else "idle", "implemented": True, "group": "bootstrap"},
{"id": "refresh-price-data", "label": "Refresh Price Data", "description": "Download fresh OHLCV for all watchlist instruments.", "status": "running" if _running.get("refresh-price-data") else "idle", "implemented": True, "group": "data"},
{"id": "compute-indicators", "label": "Compute Indicators", "description": "Recalculate MA/RSI/BB/ATR for all instruments.", "status": "running" if _running.get("compute-indicators") else "idle", "implemented": True, "group": "data"},
{"id": "evaluate-impacts", "label": "Evaluate Impacts", "description": "AI-score market_events that lack impact_score.", "status": "running" if _running.get("evaluate-impacts") else "idle", "implemented": True, "group": "ai"},
{"id": "generate-signals", "label": "Generate Signals", "description": "Compute trading signals from indicators + events.", "status": "running" if _running.get("generate-signals") else "idle", "implemented": True, "group": "ai"},
{"id": "update-regime", "label": "Update Regime", "description": "Re-classify the current macro regime.", "status": "running" if _running.get("update-regime") else "idle", "implemented": True, "group": "ai"},
{"id": "snapshot-portfolio", "label": "Snapshot Portfolio", "description": "Store a risk snapshot (VaR, PnL, delta).", "status": "running" if _running.get("snapshot-portfolio") else "idle", "implemented": True, "group": "portfolio"},
{"id": "generate-report", "label": "Generate Report", "description": "Build and persist the daily cycle report.", "status": "running" if _running.get("generate-report") else "idle", "implemented": True, "group": "portfolio"},
]
return {"actions": actions}
@@ -267,3 +247,218 @@ def run_bootstrap_categories(force: bool = Query(False)):
raise HTTPException(500, str(e))
_release("bootstrap-categories")
return {"action": "bootstrap-categories", "status": "success", **result}
# ── POST /api/actions/bootstrap-ma ───────────────────────────────────────────
@router.post("/bootstrap-ma")
async def run_bootstrap_ma(force: bool = Query(False)):
"""Detect historical MA50/100/200 crossovers for the 5 core underlyings.
Idempotent by default (skips if DB already has >100 market_events)."""
_guard("bootstrap-ma")
try:
from services.ma_analyzer import bootstrap_ma_events
result = await bootstrap_ma_events(force=force)
except Exception as e:
_release("bootstrap-ma")
raise HTTPException(500, str(e))
_release("bootstrap-ma")
return {"action": "bootstrap-ma", "status": "success", **result}
# ── POST /api/actions/refresh-price-data ─────────────────────────────────────
@router.post("/refresh-price-data")
def run_refresh_price_data(period: str = Query("3mo")):
"""Download fresh OHLCV for every watchlist instrument into price_data_cache."""
_guard("refresh-price-data")
try:
from services.price_cache import refresh_watchlist_price_data
result = refresh_watchlist_price_data(period=period)
except Exception as e:
_release("refresh-price-data")
raise HTTPException(500, str(e))
_release("refresh-price-data")
return {"action": "refresh-price-data", "status": "success", **result}
# ── POST /api/actions/compute-indicators ─────────────────────────────────────
@router.post("/compute-indicators")
def run_compute_indicators(horizon_days: int = Query(45)):
"""Recalculate RSI/MA/Bollinger/ATR for every watchlist instrument, persisted
into instrument_indicators (calibration matches the horizon, same default
as the auto-cycle's own tech-indicators step)."""
_guard("compute-indicators")
try:
from services.technical_indicators import compute_and_save_indicators
result = compute_and_save_indicators(horizon_days=horizon_days)
except Exception as e:
_release("compute-indicators")
raise HTTPException(500, str(e))
_release("compute-indicators")
return {"action": "compute-indicators", "status": "success", **result}
# ── POST /api/actions/evaluate-impacts ───────────────────────────────────────
@router.post("/evaluate-impacts")
def run_evaluate_impacts(limit: int = Query(20)):
"""AI-score every market_event that has no row yet in instrument_impacts
(i.e. was never evaluated) — same per-event function as the manual
POST /api/impact/evaluate/bulk, with auto-discovered IDs instead of
caller-supplied ones."""
_guard("evaluate-impacts")
try:
from services.database import get_conn
from services.impact_service import evaluate_event_impacts
conn = get_conn()
rows = conn.execute(
"""SELECT id FROM market_events
WHERE id NOT IN (SELECT source_id FROM instrument_impacts WHERE source_type='event')
ORDER BY id DESC LIMIT ?""",
(limit,),
).fetchall()
conn.close()
event_ids = [r["id"] for r in rows]
evaluated = []
failed = []
for eid in event_ids:
try:
evaluate_event_impacts(eid, force=False)
evaluated.append(eid)
except Exception as e:
logger.warning(f"[action/evaluate-impacts] event {eid} failed: {e}")
failed.append(eid)
except Exception as e:
_release("evaluate-impacts")
raise HTTPException(500, str(e))
_release("evaluate-impacts")
return {
"action": "evaluate-impacts", "status": "success",
"candidates_found": len(event_ids), "evaluated": len(evaluated), "failed": failed,
}
# ── POST /api/actions/generate-signals ───────────────────────────────────────
@router.post("/generate-signals")
def run_generate_signals():
"""Technical desk signals (ma_cross/rsi_extreme/bb_squeeze/new_52w_extreme,
via the same path as check-market-events sources=[technical]) + a fresh
wavelet band scan of the watchlist."""
_guard("generate-signals")
try:
from datetime import datetime as _dt
from services.market_event_detector import check_new_market_events
from services.wavelet_signals import compute_and_save_wavelet_signals
technical = check_new_market_events(sources=["technical"])
run_id = _dt.utcnow().isoformat()
wavelet_results = compute_and_save_wavelet_signals(run_id)
except Exception as e:
_release("generate-signals")
raise HTTPException(500, str(e))
_release("generate-signals")
return {
"action": "generate-signals", "status": "success",
"technical_events_created": technical.get("total_created", 0),
"wavelet_signals_computed": len(wavelet_results),
"wavelet_signals_fired": len([w for w in wavelet_results if w.get("signal_kind")]),
}
# ── POST /api/actions/update-regime ──────────────────────────────────────────
@router.post("/update-regime")
def run_update_regime():
"""Re-fetch macro gauges, re-score scenarios, log the regime snapshot, and
re-run cluster reclassification (same sequence as auto_cycle's own step,
reading n_clusters/lookback_days from the configurable cycle_step_config)."""
_guard("update-regime")
try:
from services.data_fetcher import get_macro_gauges, score_macro_scenarios
from services.database import log_macro_regime
from services.database import detect_and_save_regime_clusters
from services.auto_cycle import get_cycle_step_config
gauges = get_macro_gauges()
scenarios = score_macro_scenarios(gauges)
dominant = scenarios.get("dominant", "incertain")
gauges_summary = {
k: {"value": v.get("value"), "change_pct": v.get("change_pct"), "label": v.get("label")}
for k, v in gauges.items()
if v.get("value") is not None or v.get("change_pct") is not None
}
log_macro_regime(dominant, scenarios.get("scores", {}), scenarios.get("reasons", {}), gauges_summary)
regime_cfg = get_cycle_step_config()["regime_clustering"]
cluster_result = None
if regime_cfg.get("enabled", True):
cluster_result = detect_and_save_regime_clusters(
n_clusters=regime_cfg["n_clusters"], days=regime_cfg["lookback_days"],
)
except Exception as e:
_release("update-regime")
raise HTTPException(500, str(e))
_release("update-regime")
return {
"action": "update-regime", "status": "success",
"dominant_regime": dominant, "scenario_scores": scenarios.get("scores", {}),
"cluster_result": cluster_result,
}
# ── POST /api/actions/snapshot-portfolio ─────────────────────────────────────
@router.post("/snapshot-portfolio")
def run_snapshot_portfolio():
"""VaR + PnL snapshot — same functions already exposed individually at
POST /api/var/run-now and POST /api/var/pnl/run-now, combined into one
action for cycle-decomposition purposes."""
_guard("snapshot-portfolio")
try:
from services.var_service import compute_var, save_var_snapshot, save_pnl_snapshot
from services.auto_cycle import get_cycle_step_config
var_cfg = get_cycle_step_config()["var_snapshot"]
var_result = compute_var(
confidence=var_cfg["confidence"], horizon_days=var_cfg["horizon_days"],
lookback_days=var_cfg["lookback_days"], default_iv=var_cfg["default_iv"],
)
var_snapshot_id = None
if "error" not in var_result:
var_snapshot_id = save_var_snapshot(
var_result, confidence=var_cfg["confidence"], horizon_days=var_cfg["horizon_days"],
lookback_days=var_cfg["lookback_days"], default_iv=var_cfg["default_iv"],
)
pnl_snapshot_id = save_pnl_snapshot()
except Exception as e:
_release("snapshot-portfolio")
raise HTTPException(500, str(e))
_release("snapshot-portfolio")
return {
"action": "snapshot-portfolio", "status": "success",
"var_snapshot_id": var_snapshot_id, "pnl_snapshot_id": pnl_snapshot_id,
"var_result": var_result,
}
# ── POST /api/actions/generate-report ────────────────────────────────────────
@router.post("/generate-report")
def run_generate_report():
"""Best-effort standalone cycle report — see
services.auto_cycle.generate_standalone_report() for exactly which pieces
are DB-recovered vs. freshly recomputed vs. necessarily left empty
(artifacts that only exist inside a live run_cycle_once())."""
_guard("generate-report")
try:
from services.auto_cycle import generate_standalone_report
result = generate_standalone_report()
except Exception as e:
_release("generate-report")
raise HTTPException(500, str(e))
_release("generate-report")
return {"action": "generate-report", "status": "success", "run_id": result["run_id"]}

73
backend/services/auto_cycle.py
View File

@@ -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:

91
backend/services/database.py
View File

@@ -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(

7
backend/services/ma_analyzer.py
View File

@@ -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}

41
backend/services/price_cache.py Normal file
View File

@@ -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,
}

20
backend/services/technical_indicators.py
View File

@@ -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}