Finalize GTL and retire S/R research harness

app/scheduler.py

@@ -35,7 +35,12 @@ from app.providers.fundamentals_chain import build_fundamental_provider_chain
 from app.providers.protocol import SentimentData
 from app.services import fundamental_service, ingestion_service, sentiment_service, settings_store
 from app.services.alert_service import dispatch_alerts
-from app.services.backtest_service import run_and_store as run_backtest_and_store
+from app.services.backtest_service import (
+    BACKTEST_TARGET_MODELS,
+    PRODUCTION_GTL_TARGET_MODEL,
+    run_and_store as run_backtest_and_store,
+    validate_backtest_target_model,
+)
 from app.services.benchmark_service import refresh_benchmark_prices
 from app.services.market_regime_service import update_market_regime
 from app.services.regime_monitor_service import update_regime_monitor
@@ -106,6 +111,7 @@ def _idle_runtime() -> dict[str, object]:
 
 
 _job_runtime: dict[str, dict[str, object]] = {name: _idle_runtime() for name in _JOB_NAMES}
+_next_backtest_target_model = PRODUCTION_GTL_TARGET_MODEL
 
 
 # ---------------------------------------------------------------------------
@@ -113,6 +119,26 @@ _job_runtime: dict[str, dict[str, object]] = {name: _idle_runtime() for name in
 # ---------------------------------------------------------------------------
 
 
+def queue_backtest_target_model(target_model: str | None) -> str:
+    """Select the model for the next manual backtest run only.
+
+    Scheduled runs and subsequent manual runs return to the production GTL.
+    """
+    global _next_backtest_target_model
+    selected = validate_backtest_target_model(
+        target_model or PRODUCTION_GTL_TARGET_MODEL
+    )
+    _next_backtest_target_model = selected
+    return selected
+
+
+def _consume_backtest_target_model() -> str:
+    global _next_backtest_target_model
+    selected = _next_backtest_target_model
+    _next_backtest_target_model = PRODUCTION_GTL_TARGET_MODEL
+    return selected
+
+
 def _log_event(level: int, event: str, **fields: object) -> None:
     """Emit a structured JSON log line: {"event": ..., **fields}."""
     logger.log(level, json.dumps({"event": event, **fields}))
@@ -939,7 +965,13 @@ async def compute_regime_monitor() -> None:
 async def run_backtest_job() -> None:
     """Replay the price-derived engine over history and cache the report."""
     job_name = "backtest"
-    _log_event(logging.INFO, "job_start", job=job_name)
+    target_model = _consume_backtest_target_model()
+    _log_event(
+        logging.INFO,
+        "job_start",
+        job=job_name,
+        target_model=target_model,
+    )
     _runtime_start(job_name)
 
     def _on_progress(done: int, count: int, symbol: str) -> None:
@@ -952,12 +984,20 @@ async def run_backtest_job() -> None:
                 _runtime_finish(job_name, "skipped", processed=0, total=0, message="Disabled")
                 return
 
-            report = await run_backtest_and_store(db, _on_progress)
+            report = await run_backtest_and_store(
+                db,
+                _on_progress,
+                target_model=target_model,
+            )
 
         _runtime_finish(
             job_name, "completed",
             processed=report.get("tickers", 0), total=report.get("tickers", 0),
-            message=f"{report.get('candidates', 0)} setups, {report.get('qualified', 0)} qualified",
+            message=(
+                f"{BACKTEST_TARGET_MODELS[target_model]}: "
+                f"{report.get('candidates', 0)} setups, "
+                f"{report.get('qualified', 0)} qualified"
+            ),
         )
         _log_event(logging.INFO, "job_complete", job=job_name, candidates=report.get("candidates"))
     except Exception as exc: