Upload alpha_factory/orchestration/pipeline.py

alpha_factory/orchestration/pipeline.py

@@ -22,7 +22,6 @@ from ..personas import (
     gate_alpha,
 )
 from ..schemas import Verdict, BrainMetrics
-from ..local.brain_sim import simulate_alpha_local, sign_sweep_local
 from ..data.brain_groups import get_group_for_expression
 
 console = Console()
@@ -60,8 +59,7 @@ class AlphaPipeline:
             f"[bold green]Alpha Factory v0.2.0[/] -- Batch of {batch_size} candidates\n"
             f"[dim]{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}[/]\n"
             f"Mode: {'PROVEN TEMPLATES' if self.config.use_proven_templates else 'LLM GENERATION'} | "
-            f"BRAIN: {'LIVE' if self.config.enable_brain_client else 'DRY-RUN'} | "
-            f"Local Sim: {'ON' if self.config.enable_local_sim else 'OFF'}",
+            f"BRAIN: {'LIVE' if self.config.enable_brain_client else 'DRY-RUN'}",
             title="Pipeline Start"
         ))
 
@@ -199,14 +197,7 @@ class AlphaPipeline:
                 academic_anchor=None,
             )
             
-            # STEP 7: Local sim (if enabled)
-            local_pass = True
-            if self.config.enable_local_sim:
-                # Proven template mode skips local sim (templates are pre-validated)
-                # But we could add it here for triage
-                pass
-            
-            # STEP 8: BRAIN submission
+            # STEP 7: BRAIN submission
             verdict = await self._submit_or_dryrun(alpha_id, expr, alpha["neutralization"], alpha["decay"])
             
             if verdict == Verdict.PROMOTE:
@@ -299,14 +290,7 @@ class AlphaPipeline:
                 family_id=alpha_id[:8],
             )
 
-            # STEP 7: Local simulation (if enabled)
-            if self.config.enable_local_sim:
-                # Note: Local sim needs price data which requires yfinance
-                # For now, skip and rely on BRAIN for validation
-                # TODO: integrate yfinance data download and local sim
-                pass
-
-            # STEP 8: Crowd Scout — novelty check
+            # STEP 7: Crowd Scout — novelty check
             # Compute a synthetic correlation based on fields/archetype overlap
             max_corr = self._estimate_correlation(expression, existing_hashes)
             crowd_result = await scout_novelty(
@@ -459,4 +443,4 @@ class AlphaPipeline:
 
     def close(self):
         self.store.close()
-        self.winner_memory.close()
+        self.winner_memory.close()