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/continuous_pipeline.py
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MALKHUT/malkhut/continuous_pipeline.py
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"""
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Continuous Training Pipeline — runs the full training cycle indefinitely.
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Unlike the bounded pipeline, this one:
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- Runs forever until shutdown signal
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- Logs metrics every N generations
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- Checkpoints state periodically
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- Handles graceful shutdown via signal
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- Adapts strategy pool continuously
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"""
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from __future__ import annotations
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import json
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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, field
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from typing import Any, Dict, List, Optional
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from malkhut.state import FulfilmentPolicyParams
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from malkhut.training.pipeline import TrainingPipeline, PipelineConfig
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from malkhut.training.generator import StrategyGenerator, GeneratorConfig
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from malkhut.training.registry import PolicyRegistry
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from malkhut.training.cma_trainer import ScenarioFactory
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from malkhut.storage.ch_store import MalkhutCHStore
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@dataclass
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class ContinuousConfig:
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"""Configuration for continuous training."""
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checkpoint_interval_s: int = 300 # checkpoint every 5 minutes
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metrics_interval_s: int = 60 # log metrics every minute
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max_generations_per_cycle: int = 10 # generations per training cycle
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max_evals_per_generation: int = 10
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strategy_pool_max: int = 50
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log_path: str = "continuous_training.log"
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class ContinuousTrainingPipeline:
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"""
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Continuous training pipeline that runs indefinitely.
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Cycles through:
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1. Training pipeline (CMA-ES)
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2. Strategy generator (genetic programming)
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3. Planner diversity testing
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4. Metrics logging
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5. Checkpointing
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"""
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def __init__(self, config: Optional[ContinuousConfig] = None) -> None:
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self.config = config or ContinuousConfig()
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self._running = False
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self._shutdown_event = threading.Event() if 'threading' in dir() else None
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# Components
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self._store = MalkhutCHStore()
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self._store.ensure_tables()
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self._registry = PolicyRegistry(store=self._store)
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self._scenario_factory = ScenarioFactory()
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# Metrics
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self._cycle_count = 0
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self._total_evals = 0
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self._total_strategies = 0
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self._best_score = -float("inf")
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self._start_time = time.time()
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self._last_checkpoint = time.time()
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self._last_metrics = time.time()
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def run(self) -> None:
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"""Run the continuous training loop."""
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self._running = True
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print("=" * 70)
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print("MALKHUT CONTINUOUS TRAINING PIPELINE")
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print(f"Config: checkpoint={self.config.checkpoint_interval_s}s, "
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f"metrics={self.config.metrics_interval_s}s, "
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f"generations/cycle={self.config.max_generations_per_cycle}")
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print("=" * 70)
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# Register signal handler for graceful shutdown
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def signal_handler(sig, frame):
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print("\nShutdown signal received...")
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self._running = False
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signal.signal(signal.SIGINT, signal_handler)
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signal.signal(signal.SIGTERM, signal_handler)
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try:
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while self._running:
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self._run_cycle()
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self._cycle_count += 1
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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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# Periodic checkpoint
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if time.time() - self._last_checkpoint >= self.config.checkpoint_interval_s:
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self._checkpoint()
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self._last_checkpoint = time.time()
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except KeyboardInterrupt:
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print("\nInterrupted by user")
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finally:
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self._running = False
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self._log_final_metrics()
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def _run_cycle(self) -> None:
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"""Run one training cycle."""
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# 1. Training pipeline
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pipeline_config = PipelineConfig(
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max_generations=self.config.max_generations_per_cycle,
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max_evals_per_generation=self.config.max_evals_per_generation,
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max_time_s=300, # 5 minutes per cycle
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auto_promote=True,
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)
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pipeline = TrainingPipeline(
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config=pipeline_config, registry=self._registry,
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log_path=self.config.log_path,
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)
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result = pipeline.run(
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incumbent=_baseline(),
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symbols=("BTCUSDT",),
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)
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self._total_evals += result.total_evals
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# Track improvement
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if result.best_score > self._best_score:
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improvement = result.best_score - self._best_score
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self._best_score = result.best_score
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print(f" Cycle {self._cycle_count}: improvement +{improvement:.2f} (best={self._best_score:.2f})")
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# 2. Strategy generator
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gen_config = GeneratorConfig(
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population_size=10, generations=2, tournament_size=3, elitism_count=1,
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)
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generator = StrategyGenerator(config=gen_config, registry=self._registry)
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scenarios = self._scenario_factory.build_suite(symbols=("BTCUSDT",), steps_per_scenario=3)
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gen_population = generator.evolve(_baseline(), scenarios)
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gen_count = len([g for g in gen_population if g.generation > 0])
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self._total_strategies += gen_count
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for genome in gen_population:
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if genome.generation > 0:
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generator.add_to_pool(genome)
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def _log_metrics(self) -> None:
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"""Log current metrics."""
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elapsed = time.time() - self._start_time
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print(f" [{elapsed:.0f}s] Cycle={self._cycle_count} "
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f"Evals={self._total_evals} Strategies={self._total_strategies} "
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f"Best={self._best_score:.2f}")
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def _checkpoint(self) -> None:
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"""Checkpoint state to disk."""
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checkpoint = {
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"timestamp": time.time(),
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"cycle_count": self._cycle_count,
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"total_evals": self._total_evals,
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"total_strategies": self._total_strategies,
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"best_score": self._best_score,
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"registry_records": self._registry.record_count,
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}
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with open("smoke_checkpoint.json", "w") as f:
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json.dump(checkpoint, f, indent=2)
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def _log_final_metrics(self) -> None:
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"""Log final metrics."""
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duration = time.time() - self._start_time
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print()
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print("=" * 70)
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print("CONTINUOUS TRAINING FINAL METRICS")
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print("=" * 70)
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print(f"Duration: {duration:.1f}s ({duration/60:.1f} min)")
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print(f"Cycles completed: {self._cycle_count}")
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print(f"Total evals: {self._total_evals}")
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print(f"Total strategies: {self._total_strategies}")
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print(f"Best score: {self._best_score:.2f}")
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print(f"Registry records: {self._registry.record_count}")
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print("=" * 70)
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def _baseline() -> FulfilmentPolicyParams:
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return FulfilmentPolicyParams(
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version="baseline", ucb_c=1.414, max_sims=256, max_depth=3,
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rollout_depth=3, root_temperature=0.5, min_root_entropy=0.25,
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quote_offsets_ticks=(0, 1, 2), quote_size_fractions=(0.1, 0.25, 0.5),
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passive_ttl_ms=200, aggressive_ttl_ms=50, maker_edge_min_bps=0.5,
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cross_spread_edge_min_bps=5.0, adverse_toxicity_cancel_threshold=0.5,
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queue_churn_cancel_threshold=0.5, mae_tail_cut_bps=50.0,
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mfe_giveback_cut_fraction=0.5, max_time_in_loss_s=300.0,
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failed_recovery_cut_count=3, recovery_velocity_min_bps_per_s=0.0,
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max_symbol_notional_fraction=0.20, max_single_order_notional_fraction=0.05,
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reduce_when_global_up_fraction=0.30, session_profit_lock_fraction=0.02,
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w_expected_pnl=1.0, w_fill_probability=0.5, w_adverse_selection=2.0,
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w_queue_priority=0.5, w_inventory_risk=1.5, w_tail_loss=5.0,
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w_fee_quality=0.5, w_time_decay=0.3, w_policy_entropy=0.5,
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robust_tail_weight=2.0, toxic_counterparty_weight=3.0,
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low_liquidity_weight=2.0, latency_stress_weight=1.0,
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)
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if __name__ == "__main__":
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import threading
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pipeline = ContinuousTrainingPipeline()
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pipeline.run()
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