feat(scripts): smoke run with mock LLM and synthetic OHLCV

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

scripts/smoke_run.py

@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm.llm.client import CompletionResult
+from multi_swarm.orchestrator.run import RunConfig, run_phase1
+
+
+class MockLLMClient:
+    def complete(
+        self, genome: HypothesisAgentGenome, system: str, user: str,
+        max_tokens: int = 2000,
+    ) -> CompletionResult:
+        text = (
+            "```lisp\n"
+            "(strategy"
+            " (when (gt (indicator rsi 14) 70.0) (entry-short))"
+            " (when (lt (indicator rsi 14) 30.0) (entry-long)))\n"
+            "```"
+        )
+        return CompletionResult(
+            text=text, input_tokens=120, output_tokens=60,
+            tier=genome.model_tier, model="mock",
+        )
+
+
+def main() -> None:
+    idx = pd.date_range("2024-01-01", periods=1000, freq="1h", tz="UTC")
+    close = 100 + np.cumsum(np.random.RandomState(0).normal(0.01, 1.0, 1000))
+    ohlcv = pd.DataFrame(
+        {"open": close, "high": close + 0.5, "low": close - 0.5, "close": close, "volume": 1.0},
+        index=idx,
+    )
+    cfg = RunConfig(
+        run_name="smoke",
+        population_size=3,
+        n_generations=1,
+        elite_k=1,
+        tournament_k=2,
+        p_crossover=0.5,
+        seed=0,
+        model_tier=ModelTier.C,
+        db_path=Path("./runs.db"),
+    )
+    run_id = run_phase1(cfg, ohlcv=ohlcv, llm=MockLLMClient())  # type: ignore[arg-type]
+    print(f"Smoke run completed: {run_id}")
+
+
+if __name__ == "__main__":
+    main()