109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
from pathlib import Path
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import json
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import os
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import sys
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from sqlalchemy.exc import SQLAlchemyError
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ROOT = Path(__file__).resolve().parents[1]
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if str(ROOT) not in sys.path:
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sys.path.insert(0, str(ROOT))
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from src.services.bias import BiasService
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from src.services.signals import MINIMAL_SIGNAL_EVIDENCE_KEYS, STRICT_SIGNAL_EVIDENCE_KEYS, SignalService
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def build_signal_evidence_keys_from_env(env=None) -> tuple[str, ...]:
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env = os.environ if env is None else env
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mode = str(env.get("SIGNAL_EVIDENCE_MODE") or "strict").strip().lower()
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if mode == "minimal":
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return MINIMAL_SIGNAL_EVIDENCE_KEYS
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if mode == "strict":
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return STRICT_SIGNAL_EVIDENCE_KEYS
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raise ValueError(f"unsupported_signal_evidence_mode:{mode}")
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def build_signal_timeframe_set_from_env(env=None) -> dict:
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env = os.environ if env is None else env
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bias_tf = str(env.get("AI_ICT_BIAS_TIMEFRAME") or env.get("SIGNAL_BIAS_TIMEFRAME") or "1m")
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setup_tf = str(env.get("AI_ICT_SETUP_TIMEFRAME") or env.get("SIGNAL_SETUP_TIMEFRAME") or bias_tf)
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trigger_tf = str(env.get("AI_ICT_TRIGGER_TIMEFRAME") or env.get("SIGNAL_TRIGGER_TIMEFRAME") or setup_tf)
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return {
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"bias_tf": bias_tf,
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"setup_tf": setup_tf,
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"trigger_tf": trigger_tf,
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}
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def main(env=None) -> int:
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env = os.environ if env is None else env
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output_json = _get_bool(env, "SIGNALS_OUTPUT_JSON", False)
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instrument_id = 1
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timeframe_set = build_signal_timeframe_set_from_env(env=env)
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bias_service = BiasService()
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signal_service = SignalService(required_evidence_keys=build_signal_evidence_keys_from_env(env=env))
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try:
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bias = bias_service.build_bias(
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instrument_id=instrument_id,
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bias_tf=timeframe_set["bias_tf"],
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setup_tf=timeframe_set["setup_tf"],
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trigger_tf=timeframe_set["trigger_tf"],
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)
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bias_id = bias_service.persist_bias(
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instrument_id=instrument_id,
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bias_tf=timeframe_set["bias_tf"],
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setup_tf=timeframe_set["setup_tf"],
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trigger_tf=timeframe_set["trigger_tf"],
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candidate=bias,
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)
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signal = signal_service.build_signal(
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instrument_id=instrument_id,
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setup_tf=timeframe_set["setup_tf"],
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trigger_tf=timeframe_set["trigger_tf"],
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)
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written = signal_service.persist_signal(
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instrument_id=instrument_id,
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setup_tf=timeframe_set["setup_tf"],
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trigger_tf=timeframe_set["trigger_tf"],
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candidate=signal,
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)
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except SQLAlchemyError as exc:
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payload = {
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"passed": False,
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"instrument_id": instrument_id,
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"timeframe": timeframe_set["trigger_tf"],
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"timeframe_set": timeframe_set,
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"error_type": exc.__class__.__name__,
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"error": str(exc),
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}
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if output_json:
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print(json.dumps(payload, default=str, ensure_ascii=False, sort_keys=True))
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else:
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print(f"Database unavailable for signal sample: {exc.__class__.__name__}")
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return 2
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payload = {
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"passed": True,
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"instrument_id": instrument_id,
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"timeframe": timeframe_set["trigger_tf"],
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"timeframe_set": timeframe_set,
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"bias_snapshot_id": bias_id,
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"signal_rows_written": written,
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}
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if output_json:
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print(json.dumps(payload, default=str, ensure_ascii=False, sort_keys=True))
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else:
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print(f'Bias snapshot id: {bias_id}; signal rows written: {written}')
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return 0
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def _get_bool(env, key: str, default: bool) -> bool:
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value = env.get(key)
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if value is None or value == "":
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return default
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return str(value).strip().lower() in {"1", "true", "yes", "on"}
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if __name__ == '__main__':
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raise SystemExit(main())
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