953 lines
33 KiB
Python
953 lines
33 KiB
Python
'''Run the focused four-arm daily portfolio-capacity research matrix.
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The expensive point-in-time daily replay and full-universe ranks are cached
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once. Empty-book monthly paths and warm-book weekly seeds are then evaluated
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under cap 10, cap 15, cash-only unbounded, and weekly current-rank top 10.
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'''
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from __future__ import annotations
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import argparse
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import asyncio
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import hashlib
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import json
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import multiprocessing
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import os
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import pickle
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import platform
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import subprocess
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import sys
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from collections import Counter
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from datetime import date, datetime, timezone
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from pathlib import Path
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
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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 scripts.portfolio_capacity_research import ( # noqa: E402
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ANCHOR_YEARS,
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ARM_BY_ID,
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ARMS,
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BOOTSTRAP_REPLICATES,
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BOOTSTRAP_SEED,
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COSTS_PER_SIDE_PCT,
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aggregate_results,
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build_cells,
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build_cohort_manifest,
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median,
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summarize_simulation,
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validate_cohort_manifest,
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)
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from scripts.research_rankings import _live_universe_rank_map # noqa: E402
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CACHE_VERSION = 'portfolio-capacity-candidates-v1-zero-horizon'
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RUNNER_VERSION = 'portfolio-capacity-bracket-v1'
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SPEC_PATH = ROOT / 'docs' / 'research' / 'portfolio-capacity-bracket.md'
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DEFAULT_RUN_ID = 'prod505-capacity-bracket-daily-v1'
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_WORKER_CONTEXT: dict[str, Any] | None = None
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def _parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('snapshot', help='SQLite backtest snapshot')
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parser.add_argument('--run-id', default=DEFAULT_RUN_ID)
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parser.add_argument(
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'--workers',
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default='auto',
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help='Worker count or auto',
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)
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parser.add_argument('--resume', action='store_true')
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parser.add_argument('--validate-only', action='store_true')
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parser.add_argument('--candidate-cache', default=None)
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parser.add_argument('--checkpoint', default=None)
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parser.add_argument('--out', default=None)
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parser.add_argument('--quiet', action='store_true')
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return parser.parse_args()
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def _worker_count(raw: str) -> int:
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if str(raw).lower() == 'auto':
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return max(1, min(6, (multiprocessing.cpu_count() or 2) - 1))
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value = int(raw)
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if value <= 0:
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raise ValueError('--workers must be positive or auto')
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return value
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def _sqlite_url(path: Path) -> str:
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return f'sqlite+aiosqlite:///{path.resolve().as_posix()}'
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def _sha256_file(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open('rb') as handle:
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for chunk in iter(lambda: handle.read(1024 * 1024), b''):
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digest.update(chunk)
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return digest.hexdigest()
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def _json_hash(value: Any) -> str:
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payload = json.dumps(
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value,
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sort_keys=True,
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separators=(',', ':'),
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default=str,
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).encode('utf-8')
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return hashlib.sha256(payload).hexdigest()
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def _atomic_json(path: Path, value: Any) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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temporary = path.with_suffix(path.suffix + '.tmp')
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with temporary.open('w', encoding='utf-8', newline='\n') as handle:
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json.dump(value, handle, indent=2, sort_keys=True, allow_nan=False)
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handle.write('\n')
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temporary.replace(path)
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def _atomic_text(path: Path, value: str) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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temporary = path.with_suffix(path.suffix + '.tmp')
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with temporary.open('w', encoding='utf-8', newline='\n') as handle:
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handle.write(value.rstrip() + '\n')
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temporary.replace(path)
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def _atomic_pickle(path: Path, value: Any) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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temporary = path.with_suffix(path.suffix + '.tmp')
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with temporary.open('wb') as handle:
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pickle.dump(value, handle, protocol=pickle.HIGHEST_PROTOCOL)
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temporary.replace(path)
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def _git_output(*args: str) -> str:
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completed = subprocess.run(
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['git', *args],
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cwd=ROOT,
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check=True,
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capture_output=True,
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text=True,
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)
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return completed.stdout.strip()
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def _assert_clean_worktree() -> None:
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dirty = _git_output('status', '--porcelain')
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if dirty:
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raise SystemExit(
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'Authoritative research refuses a dirty worktree; commit first:\n'
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+ dirty
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)
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def _worker_init(context: dict[str, Any]) -> None:
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global _WORKER_CONTEXT
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os.environ['BACKTEST_SNAPSHOT_OFFLINE'] = '1'
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os.environ['BACKTEST_ALLOW_SPAWN'] = '1'
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from app.services import backtest_service as bt
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context = dict(context)
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context['post_stop_reentry_fn'] = bt._make_gate_reset_reentry_fn(
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context['qualified_candidates'],
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context['prices'],
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cadence='daily',
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ranking_key=context['ranking_key'],
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evaluation_horizon_sessions=0,
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)
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_WORKER_CONTEXT = context
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def _worker_run_cell(cell: dict[str, Any]) -> dict[str, Any]:
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if _WORKER_CONTEXT is None:
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raise RuntimeError('Portfolio-capacity worker was not initialized')
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from app.services import backtest_service as bt
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context = _WORKER_CONTEXT
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arm = ARM_BY_ID[str(cell['arm_id'])]
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measurement_start = date.fromisoformat(str(cell['measurement_start']))
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hard_end = date.fromisoformat(str(cell['hard_end_exclusive']))
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sim = bt._simulate_portfolio(
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context['qualified_candidates'],
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context['prices'],
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context['benchmark_closes'],
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context['exit_policy'],
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context['hold_days'],
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ranking_key=context['ranking_key'],
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max_positions=arm['max_positions'],
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risk_per_trade=context['risk_per_trade'],
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atr_trail_multiplier=context['atr_trail_multiplier'],
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cost_per_side=float(cell['cost_per_side_pct']) / 100.0,
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post_stop_reentry_fn=context['post_stop_reentry_fn'],
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start_date=date.fromisoformat(str(cell['simulation_start'])),
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end_date=hard_end,
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measurement_start_date=measurement_start,
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hard_end_date=hard_end,
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fill_mode=bt.FILL_MODE_CLOSE,
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min_initial_risk_fraction=arm['min_initial_risk_fraction'],
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weekly_top_n_rebalance=bool(arm['weekly_top_n_rebalance']),
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daily_rank_map=(
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context['daily_rank_map']
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if arm['weekly_top_n_rebalance']
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else None
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),
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include_curve=True,
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include_trades=True,
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include_capacity_diagnostics=True,
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)
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if sim is None:
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raise RuntimeError(f'Cell produced no trades: {cell["cell_id"]}')
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return {
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**cell,
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'metrics': summarize_simulation(sim),
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}
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async def _load_snapshot(
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snapshot: Path,
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*,
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quiet: bool,
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) -> dict[str, Any]:
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from app.models.ticker import Ticker
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from app.services import backtest_service as bt
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from app.services.admin_service import get_activation_config
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from app.services.paper_trade_service import get_exit_policy
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from app.services.recommendation_service import get_recommendation_config
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engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True)
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Session = async_sessionmaker(
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engine,
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class_=AsyncSession,
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expire_on_commit=False,
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)
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try:
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async with Session() as db:
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recommendation_config = await get_recommendation_config(db)
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activation = await get_activation_config(db)
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exit_config = await get_exit_policy(db)
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benchmark_closes = await bt._load_benchmark_closes_for_backtest(
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db,
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days=None,
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refresh=False,
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)
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ticker_result = await db.execute(
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select(Ticker).order_by(Ticker.symbol)
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)
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symbols = [
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ticker.symbol for ticker in ticker_result.scalars().all()
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]
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prices: dict[str, tuple] = {}
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for index, symbol in enumerate(symbols, 1):
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columns = await bt._fetch_columns(db, symbol)
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if columns is not None:
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prices[symbol] = columns
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if not quiet and index % 50 == 0:
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print(
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f'loaded prices: {index}/{len(symbols)}',
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flush=True,
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)
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finally:
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await engine.dispose()
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if not prices or not benchmark_closes:
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raise SystemExit('Snapshot has no usable prices or benchmark history')
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strategy = next(
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row
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for row in bt.PORTFOLIO_MONITOR_STRATEGIES
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if row.get('is_production')
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)
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entry_config = bt._entry_variant_config(str(strategy['entry_variant']))
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if entry_config is None:
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raise RuntimeError('Production entry configuration is missing')
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ranking_key = str(
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entry_config.get('ranking_key') or entry_config['percentile_key']
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)
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exit_policy = bt.LIVE_EXIT_MODE_TO_SIM.get(
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str(exit_config.get('mode', 'atr_trailing')),
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'atr_trail3',
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)
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hold_days = int(exit_config.get('hold_days', 30))
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risk_per_trade = float(entry_config['risk_per_trade'])
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atr_trail_multiplier = float(
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exit_config.get('atr_multiplier', bt.ATR_TRAIL_MULTIPLIER)
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)
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threshold = float(
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activation.get('min_momentum_percentile', 80.0)
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)
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expected = {
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'ranking_key': bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY,
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'exit_policy': 'atr_trail3',
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'hold_days': 30,
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'risk_per_trade': 0.01,
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'max_positions': 10,
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'threshold': 80.0,
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}
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realized = {
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'ranking_key': ranking_key,
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'exit_policy': exit_policy,
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'hold_days': hold_days,
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'risk_per_trade': risk_per_trade,
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'max_positions': int(entry_config['max_positions']),
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'threshold': threshold,
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}
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if realized != expected:
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raise SystemExit(
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'Snapshot runtime configuration is not the frozen Phase A control: '
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+ json.dumps({'expected': expected, 'realized': realized}, sort_keys=True)
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)
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return {
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'recommendation_config': recommendation_config,
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'activation': activation,
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'exit_config': exit_config,
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'benchmark_closes': benchmark_closes,
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'prices': prices,
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'symbols': symbols,
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'universe_manifest': {
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'ticker_rows': len(symbols),
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'symbols_with_prices': len(prices),
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'symbols_sha256': _json_hash(sorted(symbols)),
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},
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'ranking_key': ranking_key,
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'exit_policy': exit_policy,
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'hold_days': hold_days,
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'risk_per_trade': risk_per_trade,
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'atr_trail_multiplier': atr_trail_multiplier,
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'threshold': threshold,
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'runtime_config': realized,
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}
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def _build_candidate_cache(
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snapshot_data: dict[str, Any],
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*,
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snapshot: Path,
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snapshot_sha256: str,
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cache_path: Path,
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workers: int,
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quiet: bool,
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) -> dict[str, Any]:
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from app.services import backtest_service as bt
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cache_key = {
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'version': CACHE_VERSION,
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'snapshot': str(snapshot.resolve()),
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'snapshot_sha256': snapshot_sha256,
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'cadence': 'daily',
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'outcome_horizon_sessions': 0,
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'recommendation_config_hash': _json_hash(
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snapshot_data['recommendation_config']
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),
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'activation_hash': _json_hash(snapshot_data['activation']),
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'runtime_config': snapshot_data['runtime_config'],
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'universe_manifest': snapshot_data['universe_manifest'],
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}
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if cache_path.exists():
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with cache_path.open('rb') as handle:
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cached = pickle.load(handle) # noqa: S301 - trusted local cache
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if cached.get('key') == cache_key:
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if not quiet:
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print(f'loaded candidate/rank cache: {cache_path}', flush=True)
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return cached
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if not quiet:
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print(f'candidate cache mismatch; rebuilding: {cache_path}', flush=True)
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replay_rows: list[dict[str, Any]] = []
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prices = snapshot_data['prices']
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replay_args = [
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(
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symbol,
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columns,
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snapshot_data['recommendation_config'],
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snapshot_data['activation'],
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snapshot_data['benchmark_closes'],
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date(1900, 1, 1),
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'daily',
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True,
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True,
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0,
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)
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for symbol, columns in prices.items()
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]
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if workers == 1:
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for index, args in enumerate(replay_args, 1):
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replay_rows.extend(bt._replay_candidates_for_period(*args))
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if not quiet and index % 25 == 0:
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print(
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f'daily replay: {index}/{len(replay_args)} tickers',
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flush=True,
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)
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else:
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context = bt._mp_context() or multiprocessing.get_context('spawn')
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with ProcessPoolExecutor(
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max_workers=workers,
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mp_context=context,
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) as pool:
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futures = [
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pool.submit(bt._replay_candidates_for_period, *args)
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for args in replay_args
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]
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for index, future in enumerate(as_completed(futures), 1):
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replay_rows.extend(future.result())
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if not quiet and index % 25 == 0:
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print(
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f'daily replay: {index}/{len(futures)} tickers',
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flush=True,
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)
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setup_candidates = [
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row for row in replay_rows if not row.get('_rank_only')
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]
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rank_observations = [
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row for row in replay_rows if row.get('_universe_rank_observation')
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]
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daily_rank_map = _live_universe_rank_map(
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rank_observations,
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snapshot_data['benchmark_closes'],
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bt.STRATEGY_RANK_MOMENTUM_WEIGHT,
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)
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qualified: list[dict[str, Any]] = []
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for setup in setup_candidates:
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if setup.get('direction') != 'long':
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continue
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identity = (str(setup['symbol']), str(setup['date']))
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rank = daily_rank_map.get(identity)
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if rank is None:
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continue
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candidate = {
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key: value
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for key, value in setup.items()
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if not key.startswith('_universe_')
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}
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candidate[bt.PRODUCTION_PERCENTILE_KEY] = rank['momentum_percentile']
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candidate[bt.VOL_PERCENTILE_KEY] = rank['volatility_percentile']
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candidate[bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY] = rank['strategy_rank']
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candidate['qualified'] = bt._momentum_qualifies(
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candidate,
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snapshot_data['threshold'],
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)
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if candidate['qualified']:
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qualified.append(candidate)
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qualified.sort(
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key=lambda row: (
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str(row['date']),
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-float(row.get(snapshot_data['ranking_key']) or 0.0),
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str(row['symbol']),
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float(row.get('entry') or 0.0),
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float(row.get('stop') or 0.0),
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float(row.get('target') or 0.0),
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str(row.get('action') or ''),
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)
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)
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rank_dates = sorted({identity[1] for identity in daily_rank_map})
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cached = {
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'key': cache_key,
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'qualified_candidates': qualified,
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'daily_rank_map': daily_rank_map,
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'setup_candidate_count': len(setup_candidates),
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'qualified_long_count': len(qualified),
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'entry_candidates_by_direction': dict(
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Counter(str(row['direction']) for row in setup_candidates)
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),
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'rank_observation_count': len(rank_observations),
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'rank_first_date': rank_dates[0] if rank_dates else None,
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'rank_last_date': rank_dates[-1] if rank_dates else None,
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}
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_atomic_pickle(cache_path, cached)
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if not quiet:
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print(f'wrote candidate/rank cache: {cache_path}', flush=True)
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return cached
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def _checkpoint_state(
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checkpoint_dir: Path,
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fingerprint: str,
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*,
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resume: bool,
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) -> dict[str, dict[str, Any]]:
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manifest_path = checkpoint_dir / 'manifest.json'
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if checkpoint_dir.exists() and not resume:
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existing_cells = list(checkpoint_dir.glob('cell-*.json'))
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if existing_cells:
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raise SystemExit(
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f'Checkpoint cells already exist at {checkpoint_dir}; use --resume '
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'or a new --run-id'
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)
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checkpoint_dir.mkdir(parents=True, exist_ok=True)
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if manifest_path.exists():
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existing = json.loads(manifest_path.read_text(encoding='utf-8'))
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if existing.get('fingerprint') != fingerprint:
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raise SystemExit(
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f'Checkpoint fingerprint mismatch at {checkpoint_dir}'
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)
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else:
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_atomic_json(
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manifest_path,
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{
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'runner_version': RUNNER_VERSION,
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'fingerprint': fingerprint,
|
|
'created_at': datetime.now(timezone.utc).isoformat(),
|
|
},
|
|
)
|
|
|
|
completed: dict[str, dict[str, Any]] = {}
|
|
if resume:
|
|
for path in sorted(checkpoint_dir.glob('cell-*.json')):
|
|
row = json.loads(path.read_text(encoding='utf-8'))
|
|
completed[str(row['cell_id'])] = row
|
|
return completed
|
|
|
|
|
|
def _write_cell_checkpoint(
|
|
checkpoint_dir: Path,
|
|
row: dict[str, Any],
|
|
) -> None:
|
|
name = hashlib.sha256(str(row['cell_id']).encode('utf-8')).hexdigest()
|
|
_atomic_json(checkpoint_dir / f'cell-{name}.json', row)
|
|
|
|
|
|
def _fmt(value: Any, digits: int = 3) -> str:
|
|
if value is None:
|
|
return 'n/a'
|
|
if isinstance(value, float):
|
|
return f'{value:.{digits}f}'
|
|
return str(value)
|
|
|
|
|
|
def _operational_summary(cells: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
|
rows: list[dict[str, Any]] = []
|
|
for arm in ARMS:
|
|
arm_cells = [
|
|
row
|
|
for row in cells
|
|
if row['arm_id'] == arm['id']
|
|
and float(row['cost_per_side_pct']) == 0.1
|
|
]
|
|
weekly = [
|
|
row['metrics']['weekly_rebalance']
|
|
for row in arm_cells
|
|
if row['metrics'].get('weekly_rebalance')
|
|
]
|
|
rows.append({
|
|
'arm_id': arm['id'],
|
|
'paths': len(arm_cells),
|
|
'median_trades': median(
|
|
row['metrics'].get('trades') for row in arm_cells
|
|
),
|
|
'median_blocked_fraction': median(
|
|
row['metrics'].get('blocked_fraction') for row in arm_cells
|
|
),
|
|
'median_avg_positions': median(
|
|
row['metrics'].get('avg_positions') for row in arm_cells
|
|
),
|
|
'peak_positions': max(
|
|
(
|
|
int(row['metrics'].get('peak_positions') or 0)
|
|
for row in arm_cells
|
|
),
|
|
default=0,
|
|
),
|
|
'median_turnover_multiple': median(
|
|
row['metrics'].get('turnover_multiple') for row in arm_cells
|
|
),
|
|
'min_risk_rejections': sum(
|
|
int(row['metrics'].get('skipped_min_initial_risk') or 0)
|
|
for row in arm_cells
|
|
),
|
|
'weekly_zero_entrant_fraction': median(
|
|
item.get('zero_entrant_fraction') for item in weekly
|
|
),
|
|
'weekly_entrant_pool_median': median(
|
|
item.get('entrant_pool_median') for item in weekly
|
|
),
|
|
'weekly_replacements': sum(
|
|
int(item.get('replacements') or 0) for item in weekly
|
|
),
|
|
'weekly_reentries_within_10_sessions': sum(
|
|
int(item.get('reentries_within_10_sessions') or 0)
|
|
for item in weekly
|
|
),
|
|
})
|
|
return rows
|
|
|
|
|
|
def _markdown(report: dict[str, Any]) -> str:
|
|
lines = [
|
|
'# Focused daily portfolio-capacity matrix',
|
|
'',
|
|
f'Generated: {report["generated_at"]}',
|
|
'',
|
|
'## Question',
|
|
'',
|
|
'The current daily Phase A control admitted 472 trades and rejected 519 '
|
|
'qualified opportunities because the ten-slot book was full. This run '
|
|
'brackets the economic cost of that binding constraint; it has no formal '
|
|
'promotion gate.',
|
|
'',
|
|
'> Universe caveat: today\'s production membership is projected backward. '
|
|
'Use paired arm-versus-control differences, not absolute profitability, '
|
|
'for construction conclusions.',
|
|
'',
|
|
'## Paired annual medians',
|
|
'',
|
|
]
|
|
paired = report['analysis']['paired_per_year']
|
|
for protocol in ('empty_book', 'warm_book'):
|
|
lines.extend([
|
|
f'### {protocol.replace("_", " ").title()} — 0.10% per fill',
|
|
'',
|
|
'| Arm | ΔEV net R | 90% context | ΔCalmar | 90% context |',
|
|
'|---|---:|---:|---:|---:|',
|
|
])
|
|
for arm in ARMS:
|
|
row = next(
|
|
item
|
|
for item in paired
|
|
if item['arm_id'] == arm['id']
|
|
and item['protocol'] == protocol
|
|
and float(item['cost_per_side_pct']) == 0.1
|
|
)
|
|
ev = row['headline']['ev_net_r']
|
|
calmar = row['headline']['calmar']
|
|
ev_ci = ev['bootstrap_90']
|
|
calmar_ci = calmar['bootstrap_90']
|
|
lines.append(
|
|
f'| {arm["id"]} | {_fmt(ev["paired_delta_median"])} | '
|
|
f'[{_fmt(ev_ci["p05"])}, {_fmt(ev_ci["p95"])}] | '
|
|
f'{_fmt(calmar["paired_delta_median"])} | '
|
|
f'[{_fmt(calmar_ci["p05"])}, {_fmt(calmar_ci["p95"])}] |'
|
|
)
|
|
lines.append('')
|
|
lines.extend([
|
|
'| Arm | ΔPF | ΔGain-to-Pain | ΔSortino | ΔCAGR pp | ΔMaxDD pp |',
|
|
'|---|---:|---:|---:|---:|---:|',
|
|
])
|
|
for arm in ARMS:
|
|
row = next(
|
|
item
|
|
for item in paired
|
|
if item['arm_id'] == arm['id']
|
|
and item['protocol'] == protocol
|
|
and float(item['cost_per_side_pct']) == 0.1
|
|
)
|
|
headline = row['headline']
|
|
lines.append(
|
|
f'| {arm["id"]} | '
|
|
f'{_fmt(headline["profit_factor"]["paired_delta_median"])} | '
|
|
f'{_fmt(headline["gain_to_pain"]["paired_delta_median"])} | '
|
|
f'{_fmt(headline["sortino"]["paired_delta_median"])} | '
|
|
f'{_fmt(headline["cagr_pct"]["paired_delta_median"])} | '
|
|
f'{_fmt(headline["max_drawdown_pct"]["paired_delta_median"])} |'
|
|
)
|
|
lines.append('')
|
|
|
|
lines.extend([
|
|
'## Warm-seed initialization dispersion',
|
|
'',
|
|
'| Arm | Cost/fill | Median EV IQR ratio | Median Calmar IQR ratio |',
|
|
'|---|---:|---:|---:|',
|
|
])
|
|
for row in report['analysis']['warm_seed_dispersion']:
|
|
lines.append(
|
|
f'| {row["arm_id"]} | {row["cost_per_side_pct"]:.2f}% | '
|
|
f'{_fmt(row["headline"]["ev_net_r"]["median_iqr_ratio"])} | '
|
|
f'{_fmt(row["headline"]["calmar"]["median_iqr_ratio"])} |'
|
|
)
|
|
|
|
lines.extend([
|
|
'',
|
|
'## Capacity and operations — 0.10% per fill',
|
|
'',
|
|
'| Arm | Median trades | Median blocked | Median positions | Peak | '
|
|
'Turnover | Min-risk rejects |',
|
|
'|---|---:|---:|---:|---:|---:|---:|',
|
|
])
|
|
for row in report['operational_summary']:
|
|
blocked = row['median_blocked_fraction']
|
|
blocked_text = (
|
|
f'{float(blocked) * 100.0:.1f}%' if blocked is not None else 'n/a'
|
|
)
|
|
lines.append(
|
|
f'| {row["arm_id"]} | {_fmt(row["median_trades"], 1)} | '
|
|
f'{blocked_text} | {_fmt(row["median_avg_positions"], 2)} | '
|
|
f'{row["peak_positions"]} | '
|
|
f'{_fmt(row["median_turnover_multiple"], 2)} | '
|
|
f'{row["min_risk_rejections"]} |'
|
|
)
|
|
|
|
weekly = next(
|
|
row
|
|
for row in report['operational_summary']
|
|
if row['arm_id'] == 'cap10_weekly_top10'
|
|
)
|
|
lines.extend([
|
|
'',
|
|
'## Weekly-ranking opportunity set',
|
|
'',
|
|
f'- Median fresh entrant pool: '
|
|
f'{_fmt(weekly["weekly_entrant_pool_median"], 1)}.',
|
|
f'- Median zero-entrant fraction: '
|
|
f'{_fmt(weekly["weekly_zero_entrant_fraction"], 3)}.',
|
|
f'- Replacements across reported paths: {weekly["weekly_replacements"]}.',
|
|
f'- Same-symbol re-entries within 10 sessions: '
|
|
f'{weekly["weekly_reentries_within_10_sessions"]}.',
|
|
'',
|
|
'Bootstrap intervals above resample seven annual summaries and are '
|
|
'descriptive context only. They are not gates or independent-population '
|
|
'confidence claims.',
|
|
])
|
|
return '\n'.join(lines)
|
|
|
|
|
|
async def _main() -> None:
|
|
args = _parse_args()
|
|
snapshot = Path(args.snapshot)
|
|
if not snapshot.exists():
|
|
raise SystemExit(f'Snapshot does not exist: {snapshot}')
|
|
if not SPEC_PATH.exists():
|
|
raise SystemExit(f'Frozen specification is missing: {SPEC_PATH}')
|
|
|
|
os.environ['BACKTEST_SNAPSHOT_OFFLINE'] = '1'
|
|
os.environ['BACKTEST_ALLOW_SPAWN'] = '1'
|
|
workers = _worker_count(str(args.workers))
|
|
snapshot_sha256 = _sha256_file(snapshot)
|
|
specification_sha256 = _sha256_file(SPEC_PATH)
|
|
|
|
snapshot_data = await _load_snapshot(snapshot, quiet=bool(args.quiet))
|
|
cache_path = (
|
|
Path(args.candidate_cache)
|
|
if args.candidate_cache
|
|
else ROOT / 'reports' / '.cache' / f'{args.run_id}-candidates.pkl'
|
|
)
|
|
candidate_cache = _build_candidate_cache(
|
|
snapshot_data,
|
|
snapshot=snapshot,
|
|
snapshot_sha256=snapshot_sha256,
|
|
cache_path=cache_path,
|
|
workers=workers,
|
|
quiet=bool(args.quiet),
|
|
)
|
|
|
|
cohort_manifest = build_cohort_manifest(
|
|
snapshot_data['benchmark_closes'].keys()
|
|
)
|
|
cohort_errors = validate_cohort_manifest(cohort_manifest)
|
|
cells = build_cells(cohort_manifest)
|
|
validation_payload = {
|
|
'runner_version': RUNNER_VERSION,
|
|
'snapshot': str(snapshot.resolve()),
|
|
'snapshot_sha256': snapshot_sha256,
|
|
'snapshot_sessions': {
|
|
'count': cohort_manifest['session_count'],
|
|
'first': cohort_manifest['snapshot_first_session'],
|
|
'last': cohort_manifest['snapshot_last_session'],
|
|
},
|
|
'candidate_rank_coverage': {
|
|
'first': candidate_cache['rank_first_date'],
|
|
'last': candidate_cache['rank_last_date'],
|
|
'observations': candidate_cache['rank_observation_count'],
|
|
'qualified_longs': candidate_cache['qualified_long_count'],
|
|
},
|
|
'universe_manifest': snapshot_data['universe_manifest'],
|
|
'empty_cluster_counts': cohort_manifest['empty_cluster_counts'],
|
|
'warm_seed_counts': cohort_manifest['warm_seed_counts'],
|
|
'empty_cluster_count': cohort_manifest['empty_cluster_count'],
|
|
'warm_cluster_count': cohort_manifest['warm_cluster_count'],
|
|
'expected_clusters': list(ANCHOR_YEARS),
|
|
'matrix_cells': len(cells),
|
|
'cache_path': str(cache_path.resolve()),
|
|
'cache_key_hash': _json_hash(candidate_cache['key']),
|
|
'errors': cohort_errors,
|
|
}
|
|
print(json.dumps(validation_payload, indent=2, sort_keys=True), flush=True)
|
|
if cohort_errors:
|
|
raise SystemExit(
|
|
'Cohort validation failed; revise and re-hash the specification'
|
|
)
|
|
if args.validate_only:
|
|
return
|
|
|
|
_assert_clean_worktree()
|
|
git_commit = _git_output('rev-parse', 'HEAD')
|
|
fingerprint_payload = {
|
|
'runner_version': RUNNER_VERSION,
|
|
'git_commit': git_commit,
|
|
'snapshot_sha256': snapshot_sha256,
|
|
'specification_sha256': specification_sha256,
|
|
'candidate_cache_key': candidate_cache['key'],
|
|
'universe_manifest': snapshot_data['universe_manifest'],
|
|
'cohort_manifest': cohort_manifest,
|
|
'arms': list(ARMS),
|
|
'costs_per_side_pct': list(COSTS_PER_SIDE_PCT),
|
|
'bootstrap': {
|
|
'replicates': BOOTSTRAP_REPLICATES,
|
|
'seed': BOOTSTRAP_SEED,
|
|
},
|
|
}
|
|
fingerprint = _json_hash(fingerprint_payload)
|
|
checkpoint_dir = (
|
|
Path(args.checkpoint)
|
|
if args.checkpoint
|
|
else ROOT / 'reports' / '.cache' / f'{args.run_id}-checkpoint'
|
|
)
|
|
completed = _checkpoint_state(
|
|
checkpoint_dir,
|
|
fingerprint,
|
|
resume=bool(args.resume),
|
|
)
|
|
expected_ids = {str(cell['cell_id']) for cell in cells}
|
|
unknown = set(completed) - expected_ids
|
|
if unknown:
|
|
raise SystemExit(
|
|
f'Checkpoint contains {len(unknown)} unknown matrix cells'
|
|
)
|
|
remaining = [
|
|
cell for cell in cells if str(cell['cell_id']) not in completed
|
|
]
|
|
if not args.quiet:
|
|
print(
|
|
f'matrix cells: {len(completed)} resumed, {len(remaining)} remaining',
|
|
flush=True,
|
|
)
|
|
|
|
worker_context = {
|
|
'qualified_candidates': candidate_cache['qualified_candidates'],
|
|
'daily_rank_map': candidate_cache['daily_rank_map'],
|
|
'prices': snapshot_data['prices'],
|
|
'benchmark_closes': snapshot_data['benchmark_closes'],
|
|
'ranking_key': snapshot_data['ranking_key'],
|
|
'exit_policy': snapshot_data['exit_policy'],
|
|
'hold_days': snapshot_data['hold_days'],
|
|
'risk_per_trade': snapshot_data['risk_per_trade'],
|
|
'atr_trail_multiplier': snapshot_data['atr_trail_multiplier'],
|
|
}
|
|
if workers == 1:
|
|
_worker_init(worker_context)
|
|
for index, cell in enumerate(remaining, 1):
|
|
row = _worker_run_cell(cell)
|
|
completed[str(row['cell_id'])] = row
|
|
_write_cell_checkpoint(checkpoint_dir, row)
|
|
if not args.quiet:
|
|
print(
|
|
f'portfolio cells: {len(completed)}/{len(cells)} '
|
|
f'({row["cell_id"]})',
|
|
flush=True,
|
|
)
|
|
elif remaining:
|
|
context = multiprocessing.get_context('spawn')
|
|
with ProcessPoolExecutor(
|
|
max_workers=workers,
|
|
mp_context=context,
|
|
initializer=_worker_init,
|
|
initargs=(worker_context,),
|
|
) as pool:
|
|
futures = {
|
|
pool.submit(_worker_run_cell, cell): str(cell['cell_id'])
|
|
for cell in remaining
|
|
}
|
|
for future in as_completed(futures):
|
|
row = future.result()
|
|
completed[str(row['cell_id'])] = row
|
|
_write_cell_checkpoint(checkpoint_dir, row)
|
|
if not args.quiet:
|
|
print(
|
|
f'portfolio cells: {len(completed)}/{len(cells)} '
|
|
f'({row["cell_id"]})',
|
|
flush=True,
|
|
)
|
|
|
|
missing = expected_ids - set(completed)
|
|
if missing:
|
|
raise RuntimeError(
|
|
f'Incomplete matrix: {len(missing)} cells are missing'
|
|
)
|
|
result_cells = sorted(
|
|
completed.values(),
|
|
key=lambda row: str(row['cell_id']),
|
|
)
|
|
analysis = aggregate_results(result_cells)
|
|
requirements_path = ROOT / 'requirements.txt'
|
|
report: dict[str, Any] = {
|
|
'run_id': args.run_id,
|
|
'status': 'complete',
|
|
'generated_at': datetime.now(timezone.utc).isoformat(),
|
|
'research_question': (
|
|
'Bracket the economic cost of the binding ten-position cap and '
|
|
'test whether weekly current-rank selection beats arrival order.'
|
|
),
|
|
'decision_rule': (
|
|
'No formal promotion gate. Report paired annual medians, warm-seed '
|
|
'EV/Calmar IQR ratios, and simple bootstrap intervals as context.'
|
|
),
|
|
'survivorship_bias_caveat': (
|
|
'The current production universe is projected backward; construction '
|
|
'conclusions rely on paired relative comparisons, not absolute levels.'
|
|
),
|
|
'motivation': {
|
|
'source': 'reports/research-matrix-phase-a.json a0_control full window',
|
|
'trades': 472,
|
|
'skipped_book_full': 519,
|
|
'blocked_fraction': 519 / (519 + 472),
|
|
'stale_claim_corrected': (
|
|
'The older weekly pre-gate-reset claim that cap 10 never bound '
|
|
'does not apply to the current daily configuration.'
|
|
),
|
|
},
|
|
'fingerprint': fingerprint,
|
|
'fingerprint_payload': fingerprint_payload,
|
|
'environment': {
|
|
'python': sys.version,
|
|
'platform': platform.platform(),
|
|
'requirements_sha256': (
|
|
_sha256_file(requirements_path)
|
|
if requirements_path.exists()
|
|
else None
|
|
),
|
|
'command': [sys.executable, *sys.argv],
|
|
},
|
|
'validation': validation_payload,
|
|
'runtime_config': snapshot_data['runtime_config'],
|
|
'universe_manifest': snapshot_data['universe_manifest'],
|
|
'candidate_cache': {
|
|
key: value
|
|
for key, value in candidate_cache.items()
|
|
if key
|
|
not in {
|
|
'qualified_candidates',
|
|
'daily_rank_map',
|
|
}
|
|
},
|
|
'cohort_manifest': cohort_manifest,
|
|
'arms': list(ARMS),
|
|
'costs_per_side_pct': list(COSTS_PER_SIDE_PCT),
|
|
'cell_count': len(result_cells),
|
|
'cells': result_cells,
|
|
'analysis': analysis,
|
|
'operational_summary': _operational_summary(result_cells),
|
|
}
|
|
out_path = (
|
|
Path(args.out)
|
|
if args.out
|
|
else ROOT
|
|
/ 'reports'
|
|
/ f'portfolio-construction-{args.run_id}.json'
|
|
)
|
|
_atomic_json(out_path, report)
|
|
_atomic_text(out_path.with_suffix('.md'), _markdown(report))
|
|
if not args.quiet:
|
|
print(f'wrote {out_path}', flush=True)
|
|
print(f'wrote {out_path.with_suffix(".md")}', flush=True)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
asyncio.run(_main())
|