from __future__ import annotations from pathlib import Path import json import os import sys ROOT = Path(__file__).resolve().parents[1] if str(ROOT) not in sys.path: sys.path.insert(0, str(ROOT)) from src.services.research_council import CouncilOptimizationService def main(env=None) -> int: env = os.environ if env is None else env instrument_id = int(env.get("RESEARCH_COUNCIL_INSTRUMENT_ID", "1")) timeframe = env.get("RESEARCH_COUNCIL_TIMEFRAME", "1m") model_code = env.get("RESEARCH_COUNCIL_MODEL_CODE", "OSOK_DAILY_RANGE_SWEEP_MSS_FVG_RETRACE") replay_file = str(env.get("RESEARCH_COUNCIL_REPLAY_FILE") or "").strip() or None eval_limit = int(env.get("RESEARCH_COUNCIL_EVAL_LIMIT", "20")) lesson_limit = int(env.get("RESEARCH_COUNCIL_LESSON_LIMIT", "100")) cycle_key = str(env.get("RESEARCH_COUNCIL_OPTIMIZATION_CYCLE_KEY") or "").strip() or None arena_model_codes = [ item.strip() for item in str(env.get("RESEARCH_COUNCIL_ARENA_MODEL_CODES") or "").split(",") if item.strip() ] output_json = _get_bool(env, "RESEARCH_COUNCIL_OUTPUT_JSON", False) payload = CouncilOptimizationService().run_cycle( instrument_id=instrument_id, timeframe=timeframe, model_code=model_code, replay_file=replay_file, eval_limit=eval_limit, lesson_limit=lesson_limit, arena_model_codes=arena_model_codes or None, cycle_key=cycle_key, ) _print_payload(payload, output_json=output_json) return 0 if payload.get("passed") else 2 def _print_payload(payload: dict, *, output_json: bool) -> None: if output_json: print(json.dumps(payload, ensure_ascii=False, sort_keys=True, default=str)) return print(f"Research council optimization cycle {payload.get('status')} for instrument {payload.get('instrument_id')}") print(f"Cycle: {payload.get('cycle_key')}") print(f"Next actions: {', '.join(payload.get('next_actions') or [])}") def _get_bool(env, key: str, default: bool) -> bool: value = env.get(key) if value is None or value == "": return default return str(value).strip().lower() in {"1", "true", "yes", "on"} if __name__ == "__main__": raise SystemExit(main())