350 lines
13 KiB
Python
350 lines
13 KiB
Python
|
#!/usr/bin/env python3
|
||
|
|
"""
|
||
|
|
MALKHUT Training Pipeline Launcher — 10-minute smoke test.
|
||
|
|
|
||
|
|
Launches the full training pipeline with bounded resources:
|
||
|
|
- CMA-ES training (generations, evals, time budget)
|
||
|
|
- Strategy generation (genetic operators)
|
||
|
|
- Pipeline logging (JSONL)
|
||
|
|
- CPU/RAM monitoring
|
||
|
|
- Results summary
|
||
|
|
|
||
|
|
Usage:
|
||
|
|
python -m malkhut.launch_smoke_test [--duration 600] [--evals 50]
|
||
|
|
|
||
|
|
Naming convention for discovered strategies:
|
||
|
|
{strategy_type}_{generation}_{timestamp}
|
||
|
|
e.g., SM_MCTS_gen3_20260707_034500
|
||
|
|
"""
|
||
|
|
from __future__ import annotations
|
||
|
|
|
||
|
|
import argparse
|
||
|
|
import json
|
||
|
|
import os
|
||
|
|
import resource
|
||
|
|
import sys
|
||
|
|
import time
|
||
|
|
from dataclasses import dataclass, field
|
||
|
|
from typing import Any, List, Mapping
|
||
|
|
|
||
|
|
# ── Setup paths ──────────────────────────────────────────────────────────────
|
||
|
|
_HERE = os.path.dirname(os.path.abspath(__file__))
|
||
|
|
if _HERE not in sys.path:
|
||
|
|
sys.path.insert(0, _HERE)
|
||
|
|
|
||
|
|
from malkhut.state import FulfilmentPolicyParams
|
||
|
|
from malkhut.training.pipeline import TrainingPipeline, PipelineConfig
|
||
|
|
from malkhut.training.generator import StrategyGenerator, GeneratorConfig
|
||
|
|
from malkhut.training.registry import PolicyRegistry
|
||
|
|
from malkhut.training.cma_trainer import ScenarioFactory
|
||
|
|
from malkhut.storage.ch_store import MalkhutCHStore
|
||
|
|
|
||
|
|
|
||
|
|
# ── Configuration ────────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
def _baseline() -> FulfilmentPolicyParams:
|
||
|
|
return FulfilmentPolicyParams(
|
||
|
|
version="baseline", ucb_c=1.414, max_sims=256, max_depth=3,
|
||
|
|
rollout_depth=3, root_temperature=0.5, min_root_entropy=0.25,
|
||
|
|
quote_offsets_ticks=(0, 1, 2), quote_size_fractions=(0.1, 0.25, 0.5),
|
||
|
|
passive_ttl_ms=200, aggressive_ttl_ms=50,
|
||
|
|
maker_edge_min_bps=0.5, cross_spread_edge_min_bps=5.0,
|
||
|
|
adverse_toxicity_cancel_threshold=0.5, queue_churn_cancel_threshold=0.5,
|
||
|
|
mae_tail_cut_bps=50.0, mfe_giveback_cut_fraction=0.5,
|
||
|
|
max_time_in_loss_s=300.0, failed_recovery_cut_count=3,
|
||
|
|
recovery_velocity_min_bps_per_s=0.0,
|
||
|
|
max_symbol_notional_fraction=0.20, max_single_order_notional_fraction=0.05,
|
||
|
|
reduce_when_global_up_fraction=0.30, session_profit_lock_fraction=0.02,
|
||
|
|
w_expected_pnl=1.0, w_fill_probability=0.5, w_adverse_selection=2.0,
|
||
|
|
w_queue_priority=0.5, w_inventory_risk=1.5, w_tail_loss=5.0,
|
||
|
|
w_fee_quality=0.5, w_time_decay=0.3, w_policy_entropy=0.5,
|
||
|
|
robust_tail_weight=2.0, toxic_counterparty_weight=3.0,
|
||
|
|
low_liquidity_weight=2.0, latency_stress_weight=1.0,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class SmokeTestResult:
|
||
|
|
"""Results from a smoke test run."""
|
||
|
|
duration_s: float
|
||
|
|
generations_run: int
|
||
|
|
total_evals: int
|
||
|
|
best_score: float
|
||
|
|
strategies_developed: int
|
||
|
|
builtin_strategies_parsed: int
|
||
|
|
genetic_strategies_evolved: int
|
||
|
|
peak_cpu_pct: float
|
||
|
|
peak_ram_mb: float
|
||
|
|
avg_cpu_pct: float
|
||
|
|
avg_ram_mb: float
|
||
|
|
events_logged: int
|
||
|
|
registry_records: int
|
||
|
|
strategy_names: List[str] = field(default_factory=list)
|
||
|
|
|
||
|
|
|
||
|
|
# ── CPU/RAM Monitor ──────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
class ResourceMonitor:
|
||
|
|
"""Track CPU and RAM usage during the run."""
|
||
|
|
|
||
|
|
def __init__(self) -> None:
|
||
|
|
self._samples: list[tuple[float, float]] = [] # (cpu%, ram_mb)
|
||
|
|
self._start_time = time.time()
|
||
|
|
|
||
|
|
def sample(self) -> tuple[float, float]:
|
||
|
|
"""Sample current CPU% and RAM MB."""
|
||
|
|
usage = resource.getrusage(resource.RUSAGE_SELF)
|
||
|
|
ram_mb = usage.ru_maxrss / 1024 # KB → MB (Linux)
|
||
|
|
|
||
|
|
# CPU% from /proc/self/stat (user + system time)
|
||
|
|
try:
|
||
|
|
with open("/proc/self/stat") as f:
|
||
|
|
fields = f.read().split()
|
||
|
|
utime = int(fields[13]) # user time (ticks)
|
||
|
|
stime = int(fields[14]) # system time (ticks)
|
||
|
|
total_ticks = utime + stime
|
||
|
|
elapsed = time.time() - self._start_time
|
||
|
|
# Approximate CPU% (ticks are ~10ms on Linux)
|
||
|
|
cpu_pct = min(100.0, (total_ticks * 10.0) / max(elapsed * 1000.0, 1.0) * 100.0)
|
||
|
|
except Exception:
|
||
|
|
cpu_pct = 0.0
|
||
|
|
|
||
|
|
self._samples.append((cpu_pct, ram_mb))
|
||
|
|
return cpu_pct, ram_mb
|
||
|
|
|
||
|
|
@property
|
||
|
|
def peak_cpu(self) -> float:
|
||
|
|
return max((s[0] for s in self._samples), default=0.0)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def peak_ram(self) -> float:
|
||
|
|
return max((s[1] for s in self._samples), default=0.0)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def avg_cpu(self) -> float:
|
||
|
|
if not self._samples:
|
||
|
|
return 0.0
|
||
|
|
return sum(s[0] for s in self._samples) / len(self._samples)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def avg_ram(self) -> float:
|
||
|
|
if not self._samples:
|
||
|
|
return 0.0
|
||
|
|
return sum(s[1] for s in self._samples) / len(self._samples)
|
||
|
|
|
||
|
|
|
||
|
|
# ── Strategy Naming ──────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
def name_strategy(
|
||
|
|
strategy_type: str,
|
||
|
|
generation: int,
|
||
|
|
fitness: float,
|
||
|
|
parent_ids: tuple[str, ...] = (),
|
||
|
|
) -> str:
|
||
|
|
"""
|
||
|
|
Name a discovered strategy.
|
||
|
|
|
||
|
|
Convention:
|
||
|
|
{type}_gen{N}_{timestamp}
|
||
|
|
|
||
|
|
Examples:
|
||
|
|
SM_MCTS_gen3_20260707_034500
|
||
|
|
UCB1_gen1_20260707_034515
|
||
|
|
"""
|
||
|
|
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
|
|
return f"{strategy_type}_gen{generation}_{ts}"
|
||
|
|
|
||
|
|
|
||
|
|
# ── Main Smoke Test ──────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
def run_smoke_test(duration_s: int = 600, max_evals: int = 50) -> SmokeTestResult:
|
||
|
|
"""
|
||
|
|
Run a 10-minute smoke test of the full training pipeline.
|
||
|
|
|
||
|
|
Returns SmokeTestResult with metrics.
|
||
|
|
"""
|
||
|
|
print("=" * 70)
|
||
|
|
print("MALKHUT SMOKE TEST — Training Pipeline")
|
||
|
|
print(f"Duration: {duration_s}s | Max evals: {max_evals}")
|
||
|
|
print("=" * 70)
|
||
|
|
|
||
|
|
monitor = ResourceMonitor()
|
||
|
|
t0 = time.time()
|
||
|
|
|
||
|
|
# ── Setup ────────────────────────────────────────────────────────────────
|
||
|
|
print("\n[1/5] Setting up infrastructure...")
|
||
|
|
monitor.sample()
|
||
|
|
|
||
|
|
store = MalkhutCHStore()
|
||
|
|
store.ensure_tables()
|
||
|
|
registry = PolicyRegistry(store=store)
|
||
|
|
|
||
|
|
# ── Training Pipeline ────────────────────────────────────────────────────
|
||
|
|
print("[2/5] Running training pipeline...")
|
||
|
|
pipeline_config = PipelineConfig(
|
||
|
|
max_generations=5,
|
||
|
|
max_evals_per_generation=max_evals // 5,
|
||
|
|
max_time_s=duration_s * 0.6, # 60% of time for training
|
||
|
|
auto_promote=True,
|
||
|
|
)
|
||
|
|
pipeline = TrainingPipeline(
|
||
|
|
config=pipeline_config, registry=registry,
|
||
|
|
log_path=os.path.join(_HERE, "training.log"),
|
||
|
|
)
|
||
|
|
monitor.sample()
|
||
|
|
|
||
|
|
pipeline_result = pipeline.run(
|
||
|
|
incumbent=_baseline(),
|
||
|
|
symbols=("BTCUSDT",),
|
||
|
|
)
|
||
|
|
monitor.sample()
|
||
|
|
|
||
|
|
print(f" Generations: {pipeline_result.generations_run}")
|
||
|
|
print(f" Evals: {pipeline_result.total_evals}")
|
||
|
|
print(f" Best score: {pipeline_result.best_score:.4f}")
|
||
|
|
print(f" Duration: {pipeline_result.duration_s:.1f}s")
|
||
|
|
|
||
|
|
# ── Strategy Generation ──────────────────────────────────────────────────
|
||
|
|
print("[3/5] Running strategy generator...")
|
||
|
|
remaining_time = duration_s * 0.3 - pipeline_result.duration_s
|
||
|
|
if remaining_time > 10:
|
||
|
|
gen_config = GeneratorConfig(
|
||
|
|
population_size=10,
|
||
|
|
generations=2,
|
||
|
|
tournament_size=3,
|
||
|
|
elitism_count=2,
|
||
|
|
)
|
||
|
|
generator = StrategyGenerator(config=gen_config, registry=registry)
|
||
|
|
scenarios = ScenarioFactory().build_suite(symbols=("BTCUSDT",), steps_per_scenario=5)
|
||
|
|
|
||
|
|
gen_population = generator.evolve(_baseline(), scenarios)
|
||
|
|
|
||
|
|
# Name discovered strategies
|
||
|
|
strategy_names = []
|
||
|
|
for genome in gen_population:
|
||
|
|
name = name_strategy(
|
||
|
|
genome.strategy_type.value,
|
||
|
|
genome.generation,
|
||
|
|
genome.fitness,
|
||
|
|
)
|
||
|
|
strategy_names.append(name)
|
||
|
|
generator.add_to_pool(genome)
|
||
|
|
|
||
|
|
genetic_count = len([g for g in gen_population if g.generation > 0])
|
||
|
|
print(f" Population: {len(gen_population)} strategies")
|
||
|
|
print(f" Genetic strategies evolved: {genetic_count}")
|
||
|
|
else:
|
||
|
|
gen_population = []
|
||
|
|
strategy_names = []
|
||
|
|
genetic_count = 0
|
||
|
|
print(" Skipped (time budget exhausted)")
|
||
|
|
|
||
|
|
monitor.sample()
|
||
|
|
|
||
|
|
# ── Builtin Strategy Parsing ─────────────────────────────────────────────
|
||
|
|
print("[4/5] Parsing builtin strategies...")
|
||
|
|
from malkhut.training.dsl import StrategyDSLCompiler, list_builtin_strategies
|
||
|
|
compiler = StrategyDSLCompiler()
|
||
|
|
builtin_count = 0
|
||
|
|
for name in list_builtin_strategies():
|
||
|
|
from malkhut.training.dsl import get_builtin_strategy
|
||
|
|
text = get_builtin_strategy(name)
|
||
|
|
if text:
|
||
|
|
template = compiler.compile(text)
|
||
|
|
builtin_count += 1
|
||
|
|
print(f" Builtin strategies parsed: {builtin_count}")
|
||
|
|
|
||
|
|
# ── Summary ──────────────────────────────────────────────────────────────
|
||
|
|
duration = time.time() - t0
|
||
|
|
monitor.sample()
|
||
|
|
|
||
|
|
print("[5/5] Summary...")
|
||
|
|
print()
|
||
|
|
print("=" * 70)
|
||
|
|
print("SMOKE TEST RESULTS")
|
||
|
|
print("=" * 70)
|
||
|
|
print(f"Duration: {duration:.1f}s")
|
||
|
|
print(f"Generations run: {pipeline_result.generations_run}")
|
||
|
|
print(f"Total evals: {pipeline_result.total_evals}")
|
||
|
|
print(f"Best score: {pipeline_result.best_score:.4f}")
|
||
|
|
print(f"Strategies developed: {len(gen_population)} (genetic: {genetic_count})")
|
||
|
|
print(f"Builtin strategies: {builtin_count}")
|
||
|
|
print(f"Registry records: {registry.record_count}")
|
||
|
|
print(f"Events logged: {len(pipeline_result.events)}")
|
||
|
|
print()
|
||
|
|
print("RESOURCE USAGE")
|
||
|
|
print(f"Peak CPU: {monitor.peak_cpu:.1f}%")
|
||
|
|
print(f"Avg CPU: {monitor.avg_cpu:.1f}%")
|
||
|
|
print(f"Peak RAM: {monitor.peak_ram:.1f} MB")
|
||
|
|
print(f"Avg RAM: {monitor.avg_ram:.1f} MB")
|
||
|
|
print()
|
||
|
|
print("STRATEGY NAMING CONVENTION")
|
||
|
|
print(" {strategy_type}_gen{generation}_{timestamp}")
|
||
|
|
print(" Examples:")
|
||
|
|
for name in strategy_names[:5]:
|
||
|
|
print(f" {name}")
|
||
|
|
if len(strategy_names) > 5:
|
||
|
|
print(f" ... and {len(strategy_names) - 5} more")
|
||
|
|
print()
|
||
|
|
print("STRATEGY TYPES DISCOVERED")
|
||
|
|
if gen_population:
|
||
|
|
types = set(g.strategy_type.value for g in gen_population)
|
||
|
|
for t in types:
|
||
|
|
count = sum(1 for g in gen_population if g.strategy_type.value == t)
|
||
|
|
print(f" {t}: {count}")
|
||
|
|
print("=" * 70)
|
||
|
|
|
||
|
|
return SmokeTestResult(
|
||
|
|
duration_s=duration,
|
||
|
|
generations_run=pipeline_result.generations_run,
|
||
|
|
total_evals=pipeline_result.total_evals,
|
||
|
|
best_score=pipeline_result.best_score,
|
||
|
|
strategies_developed=len(gen_population),
|
||
|
|
builtin_strategies_parsed=builtin_count,
|
||
|
|
genetic_strategies_evolved=genetic_count,
|
||
|
|
peak_cpu_pct=monitor.peak_cpu,
|
||
|
|
peak_ram_mb=monitor.peak_ram,
|
||
|
|
avg_cpu_pct=monitor.avg_cpu,
|
||
|
|
avg_ram_mb=monitor.avg_ram,
|
||
|
|
events_logged=len(pipeline_result.events),
|
||
|
|
registry_records=registry.record_count,
|
||
|
|
strategy_names=strategy_names,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
# ── Entry Point ──────────────────────────────────────────────────────────────
|
||
|
|
|
||
|
|
def main():
|
||
|
|
parser = argparse.ArgumentParser(description="MALKHUT Smoke Test")
|
||
|
|
parser.add_argument("--duration", type=int, default=600, help="Duration in seconds")
|
||
|
|
parser.add_argument("--evals", type=int, default=50, help="Max evaluations")
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
result = run_smoke_test(duration_s=args.duration, max_evals=args.evals)
|
||
|
|
|
||
|
|
# Write results to JSON
|
||
|
|
output = {
|
||
|
|
"duration_s": result.duration_s,
|
||
|
|
"generations_run": result.generations_run,
|
||
|
|
"total_evals": result.total_evals,
|
||
|
|
"best_score": result.best_score,
|
||
|
|
"strategies_developed": result.strategies_developed,
|
||
|
|
"builtin_strategies_parsed": result.builtin_strategies_parsed,
|
||
|
|
"genetic_strategies_evolved": result.genetic_strategies_evolved,
|
||
|
|
"peak_cpu_pct": result.peak_cpu_pct,
|
||
|
|
"peak_ram_mb": result.peak_ram_mb,
|
||
|
|
"avg_cpu_pct": result.avg_cpu_pct,
|
||
|
|
"avg_ram_mb": result.avg_ram_mb,
|
||
|
|
"events_logged": result.events_logged,
|
||
|
|
"registry_records": result.registry_records,
|
||
|
|
"strategy_names": result.strategy_names,
|
||
|
|
}
|
||
|
|
with open(os.path.join(_HERE, "smoke_test_results.json"), "w") as f:
|
||
|
|
json.dump(output, f, indent=2)
|
||
|
|
|
||
|
|
print(f"\nResults saved to smoke_test_results.json")
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|