dennisthiessenandClaude Opus 5
f5d4b516ab
Brings the durable artifacts of research/portfolio-capacity-rebalancing onto main so the rationale for raising the count cap lives with the code that cites it. The matrix runner, the research simulator hooks and the study's unit tests are deliberately left behind; they remain at tag research/portfolio-capacity-final. Corrects conclusions that were reached on EV per trade and are now superseded: the findings doc's decisions 1 (keep cap 10) and 4 (run the risk-floor A/B) are struck through and answered in a new correction section, and the research README and phase-A matrix entries are updated to match. The frozen specification itself is untouched -- its recorded SHA-256 f1e37783 still verifies. effective-risk-floor-ab.md is retained but marked CLOSED/NEGATIVE: the study it proposes is already answered by cap15 vs cash_unbounded (-0.753pp CAGR while EV/trade rises), and its EV-based pass rule would have shipped it. scripts/research_rankings.py replaces a fourth copy of the historical rank-map helper; run_research_matrix, run_execution_recovery_matrix and run_daily_reentry_matrix now share it. The shared version adds a duplicate observation guard and a deterministic symbol tie-break the copies lacked. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
'''Shared production-style historical ranking helpers for research runners.'''
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from __future__ import annotations
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from datetime import date
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def _period_percentiles(
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observations: list[dict], value_key: str
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) -> dict[tuple[str, str], float]:
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'''Rank one deterministic ticker observation per historical period.'''
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by_period: dict[tuple, list[dict]] = {}
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seen: set[tuple[str, str]] = set()
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for row in observations:
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identity = (str(row['symbol']), str(row['date']))
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if identity in seen:
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raise ValueError(f'Duplicate universe rank observation: {identity}')
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seen.add(identity)
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if row.get(value_key) is None:
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continue
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period = tuple(row['ranking_period'])
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by_period.setdefault(period, []).append(row)
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result: dict[tuple[str, str], float] = {}
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for group in by_period.values():
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ordered = sorted(
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group,
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key=lambda row: (float(row[value_key]), str(row['symbol'])),
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)
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denominator = len(ordered) - 1
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for rank, row in enumerate(ordered):
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result[(str(row['symbol']), str(row['date']))] = round(
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rank / denominator * 100.0 if denominator > 0 else 100.0,
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2,
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)
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return result
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def _live_universe_rank_map(
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observations: list[dict],
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benchmark_closes: dict[date, float],
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momentum_weight: float,
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) -> dict[tuple[str, str], dict[str, float | None]]:
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'''Historical equivalent of production compute_activation_ranks.
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Every ticker contributes at most once per session. Residual momentum starts
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only once 252 benchmark closes were point-in-time available; earlier dates
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use the same raw-momentum fallback as production.
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'''
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identities = [(str(row['symbol']), str(row['date'])) for row in observations]
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if len(identities) != len(set(identities)):
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raise ValueError('Universe ranking requires one observation per ticker/date')
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raw_pct = _period_percentiles(observations, 'momentum')
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residual_pct = _period_percentiles(observations, 'residual_momentum')
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vol_pct = _period_percentiles(observations, 'vol_6m')
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benchmark_ords = sorted(value.toordinal() for value in benchmark_closes)
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residual_start_ord = benchmark_ords[251] if len(benchmark_ords) >= 252 else None
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ranks: dict[tuple[str, str], dict[str, float | None]] = {}
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for row in observations:
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identity = (str(row['symbol']), str(row['date']))
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asof_ord = date.fromisoformat(identity[1]).toordinal()
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momentum_pct = (
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residual_pct.get(identity)
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if residual_start_ord is not None and asof_ord >= residual_start_ord
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else raw_pct.get(identity)
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)
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volatility_pct = vol_pct.get(identity)
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strategy_rank = (
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round(
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momentum_pct * momentum_weight
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+ volatility_pct * (1.0 - momentum_weight),
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2,
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)
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if momentum_pct is not None and volatility_pct is not None
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else momentum_pct
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)
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ranks[identity] = {
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'momentum_percentile': momentum_pct,
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'volatility_percentile': volatility_pct,
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'strategy_rank': strategy_rank,
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}
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return ranks
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