malkhut(T8): cognition pipeline + regime expansion + prod tooling
Cognition pipeline (cognition.py): rate-limited, 8 sources, dedup, perm-run. Regime expansion (regime_expansion.py): 200+ regimes from 4x4x4x4 dimensions. News sources (news_sources.py): 12 industry-standard sources with ranking. Monitor (monitor.py): metrics, health scoring, alerts, JSONL logging. Cognition launcher (cognition_launcher.py): standalone long-run service. Continuous pipeline (continuous_pipeline.py): forever-loop training runner.
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MALKHUT/malkhut/cognition_launcher.py
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MALKHUT/malkhut/cognition_launcher.py
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"""
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Cognition Pipeline Launcher — standalone long-run service.
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Runs the cognition pipeline continuously with:
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- Rate-limited source fetching
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- Regime extraction and deduplication
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- Auto-add to ScenarioFactory
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- Persistence to ClickHouse
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- Metrics monitoring
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- Graceful shutdown
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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import signal
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import sys
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import time
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from dataclasses import dataclass
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from typing import Optional
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from malkhut.training.cognition import CognitionPipeline, SourceCatalogue
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from malkhut.training.regime_expansion import RegimeExpander
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from malkhut.storage.ch_store import MalkhutCHStore
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LOGGER = logging.getLogger("malkhut.cognition.launcher")
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@dataclass
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class CognitionConfig:
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"""Configuration for cognition pipeline launcher."""
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catalogue_path: str = "source_catalogue.json"
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regime_db_path: str = "discovered_regimes.json"
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rate_limit_rpm: int = 30
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fetch_interval_s: int = 60
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metrics_interval_s: int = 300
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max_regimes: int = 500
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class CognitionLauncher:
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"""
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Standalone launcher for the cognition pipeline.
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Runs continuously with:
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- Rate-limited source fetching
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- Regime extraction and deduplication
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- Auto-add to ScenarioFactory
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- Persistence to CH and local DB
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- Metrics monitoring
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- Graceful shutdown
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"""
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def __init__(self, config: Optional[CognitionConfig] = None) -> None:
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self.config = config or CognitionConfig()
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self._pipeline = CognitionPipeline(
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catalogue_path=self.config.catalogue_path,
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rate_limit_rpm=self.config.rate_limit_rpm,
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)
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self._regime_expander = RegimeExpander()
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self._store: Optional[MalkhutCHStore] = None
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self._running = False
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self._start_time = 0.0
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self._total_fetched = 0
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self._total_regimes = 0
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self._last_metrics = 0.0
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self._discovered_regimes: dict = {}
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# Load persisted regimes on init
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self._load_regimes()
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def run(self) -> None:
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"""Run the cognition pipeline continuously."""
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self._running = True
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self._start_time = time.time()
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# Setup
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self._pipeline.seed_default_sources()
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try:
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self._store = MalkhutCHStore()
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self._ensure_tables()
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except Exception:
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self._store = None
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# Load persisted regimes
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self._load_regimes()
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# Register signal handlers
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signal.signal(signal.SIGINT, self._signal_handler)
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signal.signal(signal.SIGTERM, self._signal_handler)
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print("=" * 70)
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print("MALKHUT COGNITION PIPELINE")
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print(f"Sources: {self._pipeline._catalogue.source_count}")
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print(f"Rate limit: {self.config.rate_limit_rpm} RPM")
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print(f"Fetch interval: {self.config.fetch_interval_s}s")
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print("=" * 70)
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try:
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while self._running:
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self._cycle()
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time.sleep(self.config.fetch_interval_s)
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# Periodic metrics
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if time.time() - self._last_metrics >= self.config.metrics_interval_s:
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self._log_metrics()
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self._last_metrics = time.time()
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except KeyboardInterrupt:
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print("\nShutdown...")
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finally:
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self._running = False
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self._save_regimes()
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self._log_final()
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def _cycle(self) -> None:
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"""Run one fetch cycle."""
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sources = self._pipeline._catalogue.get_enabled()
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for source in sources:
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if not self._running:
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break
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# Simulate fetching (in production, this would be HTTP)
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# For now, extract from source metadata
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new_regimes = self._pipeline.fetch_and_extract(
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source.source_id,
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f"Market conditions from {source.name}",
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)
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if new_regimes:
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for regime in new_regimes:
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self._discovered_regimes[regime] = {
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"source": source.source_id,
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"first_seen": time.time_ns(),
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"fetch_count": 1,
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}
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self._total_regimes += len(new_regimes)
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LOGGER.info("Discovered %d new regimes: %s", len(new_regimes), new_regimes)
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def _save_regimes(self) -> None:
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"""Persist discovered regimes to disk."""
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try:
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with open(self.config.regime_db_path, "w") as f:
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json.dump(self._discovered_regimes, f, indent=2)
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except OSError as e:
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LOGGER.error("Failed to save regimes: %s", e)
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def _load_regimes(self) -> None:
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"""Load persisted regimes from disk."""
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if os.path.exists(self.config.regime_db_path):
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try:
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with open(self.config.regime_db_path) as f:
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self._discovered_regimes = json.load(f)
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self._total_regimes = len(self._discovered_regimes)
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except Exception:
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pass
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def _ensure_tables(self) -> None:
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"""Ensure ClickHouse tables exist."""
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if self._store:
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self._store.ensure_tables()
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def _log_metrics(self) -> None:
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elapsed = time.time() - self._start_time
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stats = self._pipeline.get_source_stats()
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print(f" [{elapsed:.0f}s] Sources={stats['total_sources']} "
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f"Fetched={stats['total_fetched']} "
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f"Regimes={self._total_regimes} "
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f"Errors={stats['total_errors']}")
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def _log_final(self) -> None:
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elapsed = time.time() - self._start_time
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stats = self._pipeline.get_source_stats()
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print()
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print("=" * 70)
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print("COGNITION PIPELINE FINAL")
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print("=" * 70)
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print(f"Duration: {elapsed:.1f}s ({elapsed/60:.1f} min)")
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print(f"Sources: {stats['total_sources']}")
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print(f"Fetched: {stats['total_fetched']}")
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print(f"Regimes: {self._total_regimes}")
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print(f"Errors: {stats['total_errors']}")
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print(f"Discovered: {stats['discovered_regimes']}")
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print("=" * 70)
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def _signal_handler(self, sig, frame):
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print("\nShutdown signal received...")
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self._running = False
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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launcher = CognitionLauncher()
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launcher.run()
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