From 203bc7917a8207de2ba1f427e66bb93be2c90f23 Mon Sep 17 00:00:00 2001 From: Daniel Maddern Date: Tue, 18 Aug 2026 20:45:09 +0700 Subject: [PATCH] Add GPU parity and feature gap analysis --- Dockerfile.gpu-feature-v1 | 32 + cohort001_feature_failure_mining_v1.py | 337 + .../commands/export_cohort_deq_v1.py | 177 + .../export_cohort_failure_context_v1.py | 235 + gpu_batch01_decision_equivalence_v1.json | 1171 + gpu_batch01_independent_review_v1_2.md | 21 + gpu_batch01_numeric_analysis_v1.json | 1907 + gpu_batch01_performance_metrics_v1_1.json | 1 + ...atch01_stateful_trace_comparison_v1_1.json | 1 + gpu_batch01_v1_1_evidence_manifest.json | 1 + gpu_batch01_v1_1_runner.py | 372 + gpu_batch01_v1_2_evidence_manifest.json | 1 + gpu_batch01_v1_2_runner.py | 203 + gpu_feature_engine_v1.py | 430 + gpu_feature_engine_v1_2.py | 78 + gpu_feature_parity_calibration_v1_2.json | 1 + gpu_feature_parity_contract_v1.json | 68 + gpu_feature_parity_contract_v1.md | 19 + gpu_feature_parity_contract_v1_1.py | 244 + gpu_feature_parity_contract_v1_2.json | 1 + gpu_feature_parity_contract_v1_2.md | 22 + gpu_feature_parity_contract_v1_2.py | 243 + gpu_feature_parity_role_surface_v1_2.json | 1 + ...re_parity_validation_adversarial_v1_2.json | 1 + ...re_parity_validation_calibration_v1_2.json | 1 + ...eature_parity_validation_holdout_v1_2.json | 1 + gpu_feature_parity_validation_v1_1.json | 1 + gpu_supertrend_v1_2_diagnostic.py | 144 + hyperscalper_candidate_taxonomy_v1.json | 32 + hyperscalper_feature_gap_analysis_v1.json | 86862 ++++++++++++++++ hyperscalper_feature_gap_analysis_v1.md | 11 + hyperscalper_feature_gap_analysis_v1.py | 1096 + hyperscalper_wave1_feature_synthesis_v1.py | 310 + run_gpu_feature_batch01_v1.sh | 17 + ...storical_feature_semantic_domain_map_v1.py | 230 + ...end_atr_numeric_drift_diagnostic_v1_2.json | 1 + ...est_cohort001_feature_failure_mining_v1.py | 61 + .../test_export_cohort_failure_context_v1.py | 41 + ...storical_feature_semantic_domain_map_v1.py | 92 + tests/test_gpu_batch01_v1_1_runner.py | 23 + tests/test_gpu_batch01_v1_2_runner.py | 22 + .../test_gpu_feature_parity_contract_v1_1.py | 111 + .../test_gpu_feature_parity_contract_v1_2.py | 122 + tests/test_gpu_supertrend_v1_2_diagnostic.py | 38 + ...st_hyperscalper_feature_gap_analysis_v1.py | 367 + ...hyperscalper_wave1_feature_synthesis_v1.py | 60 + ...t_validate_historical_oracle_acceptance.py | 66 + validate_historical_oracle_acceptance.py | 75 + 48 files changed, 95351 insertions(+) create mode 100644 Dockerfile.gpu-feature-v1 create mode 100644 cohort001_feature_failure_mining_v1.py create mode 100644 control_plane/trading_studio/management/commands/export_cohort_deq_v1.py create mode 100644 control_plane/trading_studio/management/commands/export_cohort_failure_context_v1.py create mode 100644 gpu_batch01_decision_equivalence_v1.json create mode 100644 gpu_batch01_independent_review_v1_2.md create mode 100644 gpu_batch01_numeric_analysis_v1.json create mode 100644 gpu_batch01_performance_metrics_v1_1.json create mode 100644 gpu_batch01_stateful_trace_comparison_v1_1.json create mode 100644 gpu_batch01_v1_1_evidence_manifest.json create mode 100644 gpu_batch01_v1_1_runner.py create mode 100644 gpu_batch01_v1_2_evidence_manifest.json create mode 100644 gpu_batch01_v1_2_runner.py create mode 100644 gpu_feature_engine_v1.py create mode 100644 gpu_feature_engine_v1_2.py create mode 100644 gpu_feature_parity_calibration_v1_2.json create mode 100644 gpu_feature_parity_contract_v1.json create mode 100644 gpu_feature_parity_contract_v1.md create mode 100644 gpu_feature_parity_contract_v1_1.py create mode 100644 gpu_feature_parity_contract_v1_2.json create mode 100644 gpu_feature_parity_contract_v1_2.md create mode 100644 gpu_feature_parity_contract_v1_2.py create mode 100644 gpu_feature_parity_role_surface_v1_2.json create mode 100644 gpu_feature_parity_validation_adversarial_v1_2.json create mode 100644 gpu_feature_parity_validation_calibration_v1_2.json create mode 100644 gpu_feature_parity_validation_holdout_v1_2.json create mode 100644 gpu_feature_parity_validation_v1_1.json create mode 100644 gpu_supertrend_v1_2_diagnostic.py create mode 100644 hyperscalper_candidate_taxonomy_v1.json create mode 100644 hyperscalper_feature_gap_analysis_v1.json create mode 100644 hyperscalper_feature_gap_analysis_v1.md create mode 100644 hyperscalper_feature_gap_analysis_v1.py create mode 100644 hyperscalper_wave1_feature_synthesis_v1.py create mode 100644 run_gpu_feature_batch01_v1.sh create mode 100644 scripts/generate_historical_feature_semantic_domain_map_v1.py create mode 100644 supertrend_atr_numeric_drift_diagnostic_v1_2.json create mode 100644 tests/test_cohort001_feature_failure_mining_v1.py create mode 100644 tests/test_export_cohort_failure_context_v1.py create mode 100644 tests/test_generate_historical_feature_semantic_domain_map_v1.py create mode 100644 tests/test_gpu_batch01_v1_1_runner.py create mode 100644 tests/test_gpu_batch01_v1_2_runner.py create mode 100644 tests/test_gpu_feature_parity_contract_v1_1.py create mode 100644 tests/test_gpu_feature_parity_contract_v1_2.py create mode 100644 tests/test_gpu_supertrend_v1_2_diagnostic.py create mode 100644 tests/test_hyperscalper_feature_gap_analysis_v1.py create mode 100644 tests/test_hyperscalper_wave1_feature_synthesis_v1.py create mode 100644 tests/test_validate_historical_oracle_acceptance.py create mode 100644 validate_historical_oracle_acceptance.py diff --git a/Dockerfile.gpu-feature-v1 b/Dockerfile.gpu-feature-v1 new file mode 100644 index 0000000..0b37337 --- /dev/null +++ b/Dockerfile.gpu-feature-v1 @@ -0,0 +1,32 @@ +FROM --platform=linux/arm64 nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04 + +ARG TARGETARCH + +RUN apt-get update \ + && DEBIAN_FRONTEND=noninteractive apt-get install --yes --no-install-recommends python3 python3-pip \ + && rm -rf /var/lib/apt/lists/* \ + && python3 -m pip install --no-cache-dir --upgrade pip \ + && python3 -m pip install --no-cache-dir \ + --index-url https://download.pytorch.org/whl/cu128 \ + --extra-index-url https://pypi.org/simple \ + torch==2.7.1+cu128 numpy + +# Fail the ARM64 image build if it selected an incompatible interpreter or PyTorch wheel. +RUN test "$TARGETARCH" = arm64 \ + && python3 -c "import platform, torch; assert platform.machine() == 'aarch64'; assert torch.version.cuda == '12.8'; print(f'{platform.machine()} torch={torch.__version__} cuda={torch.version.cuda}')" + +WORKDIR /opt/gpu-feature +COPY gpu_feature_engine_v1.py /opt/gpu-feature/gpu_feature_engine_v1.py +COPY gpu_batch01_v1_1_runner.py gpu_feature_parity_contract_v1_1.py /opt/gpu-feature/ +COPY control_plane/trading_studio/indicators/historical_band_channel.py /opt/gpu-feature/control_plane/trading_studio/indicators/historical_band_channel.py + +# Oracle and market data are intentionally supplied as read-only runtime mounts. +ENV PYTHONUNBUFFERED=1 \ + CUDA_DEVICE_ORDER=PCI_BUS_ID \ + GPU_FEATURE_DATA_CSV=/data/binance_btcusdt_spot_2m_180d.csv \ + GPU_FEATURE_ORACLE_NPZ=/oracle/batch01_oracle_outputs.npz \ + GPU_FEATURE_REQUEST_JSON=/oracle/batch01_oracle_request.json \ + GPU_FEATURE_CACHE_DIR=/cache + +ENTRYPOINT ["python3", "/opt/gpu-feature/gpu_feature_engine_v1.py"] +CMD ["--mode", "smoke"] diff --git a/cohort001_feature_failure_mining_v1.py b/cohort001_feature_failure_mining_v1.py new file mode 100644 index 0000000..b55abde --- /dev/null +++ b/cohort001_feature_failure_mining_v1.py @@ -0,0 +1,337 @@ +#!/usr/bin/env python3 +"""Read-only Cohort001 entry-to-frozen-oracle failure mining. + +Associations in this report are observational and must not be interpreted as +causal feature effects. The runner never replays a strategy or alters inputs. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + +import numpy as np + + +ARTIFACT = "COHORT001_FEATURE_FAILURE_MINING_V1" +GOOD_LABEL = "GOOD_ENTRY" +FAILURE_LABELS = ("WRONG_DIRECTION", "HIGH_MAE_ENTRY", "LATE_SIGNAL", "RECOVERY_DEPENDENT") +ENTRY_FIELDS = ("entry_bar", "entry_index", "entry_bar_index", "entry_idx", "bar_index") + + +def file_hash(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as source: + for block in iter(lambda: source.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def directory_hash(path: Path) -> str: + """Hash names and contents without loading the oracle into memory.""" + digest = hashlib.sha256() + for item in sorted(path.glob("*.npy")): + digest.update(item.name.encode("utf-8")) + digest.update(b"\0") + digest.update(bytes.fromhex(file_hash(item))) + return digest.hexdigest() + + +def json_compatible(value: Any) -> Any: + """Convert NumPy values to strict JSON-compatible Python values.""" + if isinstance(value, np.ndarray): + return json_compatible(value.tolist()) + if isinstance(value, np.bool_): + return bool(value) + if isinstance(value, np.integer): + return int(value) + if isinstance(value, np.floating): + value = float(value) + if isinstance(value, float): + return value if np.isfinite(value) else None + if isinstance(value, dict): + return {json_compatible(key): json_compatible(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [json_compatible(item) for item in value] + return value + + +def load_engineering_map(path: Path) -> dict[str, dict[str, Any]]: + payload = json.loads(path.read_text(encoding="utf-8")) + rows = payload.get("primitives", payload.get("requests", [])) + if not isinstance(rows, list): + raise ValueError("engineering map must contain primitives or requests") + result = {} + for row in rows: + try: + key = feature_key(row) + except (KeyError, TypeError, ValueError): + continue + if key in result: + raise ValueError(f"duplicate engineering primitive: {key}") + result[key] = dict(row, feature_key=key) + if not result: + raise ValueError("engineering map contains no usable primitives") + return result + + +def feature_key(row: dict[str, Any]) -> str: + return f"{int(row['indicator_id'])}:{int(row['period'])}:{float(row['p1']):g}" + + +def load_semantics(path: Path, engineering: dict[str, dict[str, Any]]) -> dict[str, dict[str, str]]: + try: + import pyarrow.parquet as pq + except ImportError as error: + raise ValueError("pyarrow is required to read semantic-map parquet") from error + result = {key: {"domain": "unclassified", "output_type": "continuous"} for key in engineering} + for row in pq.read_table(path).to_pylist(): + try: + key = str(row.get("feature_key") or feature_key(row)) + except (KeyError, TypeError, ValueError): + continue + if key not in result: + continue + raw = str(row.get("output_type", row.get("semantic_type", row.get("value_type", "continuous")))).lower() + kind = "event" if raw in {"event", "detection", "binary_event"} else "state" if raw in {"state", "categorical", "boolean"} else "continuous" + result[key] = {"domain": str(row.get("domain", row.get("semantic_domain", row.get("engineering_family", row.get("family", "unclassified"))))), "output_type": kind} + return result + + +def feature_keys_from_row(row: dict[str, Any]) -> list[str]: + """Read explicit triples first, then the persisted flat five-triple genome.""" + for field in ("feature_triples_json", "feature_triples"): + value = row.get(field) + if isinstance(value, str): + try: + value = json.loads(value) + except json.JSONDecodeError: + continue + if isinstance(value, list): + keys = [] + for triple in value: + if isinstance(triple, dict): + try: + keys.append(feature_key(triple)) + except (KeyError, TypeError, ValueError): + pass + if keys: + return keys + value = row.get("genome_json", row.get("genome")) + if isinstance(value, str): + try: + value = json.loads(value) + except json.JSONDecodeError: + return [] + combo = value.get("combo") if isinstance(value, dict) else None + if isinstance(combo, str): + try: + combo = json.loads(combo) + except json.JSONDecodeError: + return [] + if not isinstance(combo, list) or len(combo) < 15: + return [] + try: + return [f"{int(combo[index])}:{int(combo[index + 1])}:{float(combo[index + 2]):g}" for index in range(0, 15, 3)] + except (TypeError, ValueError): + return [] + + +def entry_bar(row: dict[str, Any]) -> tuple[int | None, str | None]: + """Resolve only an integer bar offset; timestamps are intentionally not guessed.""" + containers = [("column", row)] + for field in ("raw_ledger_json", "context_json"): + value = row.get(field) + if isinstance(value, str): + try: + value = json.loads(value) + except json.JSONDecodeError: + continue + if isinstance(value, dict): + containers.append((field, value)) + for source, values in containers: + for field in ENTRY_FIELDS: + value = values.get(field) + if isinstance(value, bool) or value is None: + continue + try: + numeric = float(value) + except (TypeError, ValueError): + continue + if np.isfinite(numeric) and numeric.is_integer() and numeric >= 0: + return int(numeric), f"{source}.{field}" + return None, None + + +def checkpoint_paths(directory: Path, engineering: dict[str, dict[str, Any]]) -> dict[str, Path]: + paths = {} + for path in directory.glob("*.npy"): + parts = path.stem.split("_") + if len(parts) != 4 or parts[0] != "hs22": + continue + try: + key = f"{int(parts[1])}:{int(parts[2])}:{float(parts[3]):g}" + except ValueError: + continue + if key in engineering and key not in paths: + paths[key] = path + return paths + + +def ks_statistic(good: np.ndarray, failure: np.ndarray) -> tuple[float, str]: + try: + from scipy.stats import ks_2samp + + return float(ks_2samp(good, failure, method="auto").statistic), "scipy_ks_2samp" + except ImportError: + # Exact empirical CDF distance evaluated at every observed rank boundary. + values = np.sort(np.concatenate((good, failure))) + left = np.searchsorted(np.sort(good), values, side="right") / good.size + right = np.searchsorted(np.sort(failure), values, side="right") / failure.size + return float(np.max(np.abs(left - right))), "exact_rank_approx" + + +def odds_ratio(good: np.ndarray, failure: np.ndarray) -> float | None: + if not np.all(np.isin(np.concatenate((good, failure)), (0, 1))): + return None + # Haldane-Anscombe correction keeps complete separation reportable. + good_on, failure_on = good.sum(), failure.sum() + return float(((failure_on + 0.5) / (failure.size - failure_on + 0.5)) / ((good_on + 0.5) / (good.size - good_on + 0.5))) + + +def comparison(good: list[tuple[float, str]], failure: list[tuple[float, str]]) -> dict[str, Any]: + good_values, failure_values = np.array([item[0] for item in good]), np.array([item[0] for item in failure]) + median_difference = float(np.median(failure_values) - np.median(good_values)) + pooled = np.concatenate((good_values, failure_values)) + good_variance = np.var(good_values, ddof=1) if good_values.size > 1 else 0.0 + failure_variance = np.var(failure_values, ddof=1) if failure_values.size > 1 else 0.0 + degrees_of_freedom = good_values.size + failure_values.size - 2 + scale = float(np.sqrt(((good_values.size - 1) * good_variance + (failure_values.size - 1) * failure_variance) / degrees_of_freedom)) if degrees_of_freedom else 0.0 + effect = median_difference / scale if scale else None + pooled_sorted = np.sort(pooled) + quantile_separation = float( + np.searchsorted(pooled_sorted, np.median(failure_values), side="right") / pooled.size + - np.searchsorted(pooled_sorted, np.median(good_values), side="right") / pooled.size + ) + ks, ks_method = ks_statistic(good_values, failure_values) + direction = np.sign(median_difference) + folds = [] + for fold in sorted(set(item[1] for item in good) & set(item[1] for item in failure)): + fold_difference = np.median([item[0] for item in failure if item[1] == fold]) - np.median([item[0] for item in good if item[1] == fold]) + folds.append(float(fold_difference)) + consistent = sum(np.sign(item) == direction for item in folds) if direction else sum(item == 0 for item in folds) + return { + "good_samples": int(good_values.size), "failure_samples": int(failure_values.size), + "median_difference_failure_minus_good": median_difference, + "standardized_effect_size": effect, "standardized_effect_size_method": "median_difference_over_pooled_within_group_standard_deviation", "quantile_separation": quantile_separation, + "ks_statistic": ks, "ks_method": ks_method, + "folds_compared": len(folds), "fold_direction_consistent": consistent, + "fold_consistency": consistent / len(folds) if folds else None, + } + + +def mine(rows: list[dict[str, Any]], engineering: dict[str, dict[str, Any]], semantics: dict[str, dict[str, str]], paths: dict[str, Path]) -> tuple[list[dict[str, Any]], dict[str, int], list[str]]: + samples: dict[str, dict[str, list[tuple[float, str]]]] = defaultdict(lambda: defaultdict(list)) + skipped = Counter() + schemas = set() + arrays: dict[str, np.ndarray] = {} + for row in rows: + label = row.get("failure_label") + if label not in (GOOD_LABEL, *FAILURE_LABELS): + continue + bar, schema = entry_bar(row) + keys = feature_keys_from_row(row) + if bar is None: + skipped["unmappable_entry_bar"] += 1 + continue + if not keys: + skipped["unmappable_feature_triples"] += 1 + continue + schemas.add(schema) + fold = str(row.get("fold", "unknown")) + for key in set(keys): + if key not in engineering or key not in paths: + skipped["feature_missing_from_oracle"] += 1 + continue + values = arrays.setdefault(key, np.load(paths[key], allow_pickle=False, mmap_mode="r").reshape(-1)) + if bar >= values.size: + skipped["entry_bar_outside_oracle"] += 1 + continue + value = float(values[bar]) + if not np.isfinite(value): + skipped["nonfinite_oracle_value"] += 1 + continue + samples[key][label].append((value, fold)) + output = [] + for key in sorted(samples): + good = samples[key][GOOD_LABEL] + if not good: + continue + for label in FAILURE_LABELS: + failure = samples[key][label] + if not failure: + continue + result = {"feature_key": key, "failure_label": label, "attribution": "ASSOCIATIVE_NOT_CAUSAL", **engineering[key], **semantics[key], **comparison(good, failure)} + if semantics[key]["output_type"] in {"state", "event"}: + result["state_event_odds_ratio_failure_vs_good"] = odds_ratio(np.array([x[0] for x in good]), np.array([x[0] for x in failure])) + output.append(result) + return output, dict(sorted(skipped.items())), sorted(schema for schema in schemas if schema) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--failure-context", type=Path, required=True) + parser.add_argument("--semantic-map", type=Path, required=True) + parser.add_argument("--oracle-checkpoint-dir", type=Path, required=True) + parser.add_argument("--engineering-map", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + args = parser.parse_args() + if not args.oracle_checkpoint_dir.is_dir(): + parser.error("--oracle-checkpoint-dir must be a directory") + for path in (args.failure_context, args.semantic_map, args.engineering_map): + if not path.is_file(): + parser.error(f"input is not a file: {path}") + try: + import pyarrow as pa + import pyarrow.parquet as pq + except ImportError as error: + raise SystemExit("pyarrow is required for Cohort001 failure mining") from error + engineering = load_engineering_map(args.engineering_map) + semantics = load_semantics(args.semantic_map, engineering) + paths = checkpoint_paths(args.oracle_checkpoint_dir, engineering) + rows = pq.read_table(args.failure_context).to_pylist() + features, skipped, entry_schemas = mine(rows, engineering, semantics, paths) + domains: dict[tuple[str, str], list[dict[str, Any]]] = defaultdict(list) + for item in features: + domains[(item["domain"], item["failure_label"])].append(item) + domain_rows = [{ + "domain": domain, "failure_label": label, "feature_comparisons": len(items), + "median_standardized_effect_size": float(np.median([x["standardized_effect_size"] for x in items if x["standardized_effect_size"] is not None])) if any(x["standardized_effect_size"] is not None for x in items) else None, + "median_ks_statistic": float(np.median([x["ks_statistic"] for x in items])), + "median_fold_consistency": float(np.median([x["fold_consistency"] for x in items if x["fold_consistency"] is not None])) if any(x["fold_consistency"] is not None for x in items) else None, + "attribution": "ASSOCIATIVE_NOT_CAUSAL", + } for (domain, label), items in sorted(domains.items())] + result = { + "schema_version": 1, "artifact": ARTIFACT, "read_only": True, + "attribution": "ASSOCIATIVE_NOT_CAUSAL", + "attribution_note": "Entry-time associations do not establish causal feature effects.", + "inputs": { + str(path): file_hash(path) for path in (args.failure_context, args.semantic_map, args.engineering_map) + } | {str(args.oracle_checkpoint_dir): directory_hash(args.oracle_checkpoint_dir)}, + "entry_bar_schema_detected": entry_schemas, + "failure_context_rows": len(rows), "oracle_features_available": len(paths), + "skipped": skipped, "feature_failure_comparisons": features, "domain_aggregates": domain_rows, + } + args.output_dir.mkdir(parents=True, exist_ok=True) + pq.write_table(pa.Table.from_pylist(features), args.output_dir / "cohort001_feature_failure_mining_v1.parquet", compression="zstd") + output = args.output_dir / "cohort001_feature_failure_mining_v1.json" + output.write_text(json.dumps(json_compatible(result), indent=2, sort_keys=True, allow_nan=False) + "\n", encoding="utf-8") + print(output) + + +if __name__ == "__main__": + main() diff --git a/control_plane/trading_studio/management/commands/export_cohort_deq_v1.py b/control_plane/trading_studio/management/commands/export_cohort_deq_v1.py new file mode 100644 index 0000000..fb743df --- /dev/null +++ b/control_plane/trading_studio/management/commands/export_cohort_deq_v1.py @@ -0,0 +1,177 @@ +from __future__ import annotations + +import json +from dataclasses import fields +from pathlib import Path +from uuid import UUID + +from django.core.management.base import BaseCommand, CommandError +from django.db import connection, transaction + +from control_plane.trading_studio.indicators.feature_catalog import feature_definition +from control_plane.trading_studio.indicators.schema import HS22SchemaError, parse_hs22 +from control_plane.trading_studio.management.commands.cohort_qualification_hyperscalper_001 import ( + RUNNER_NAME as QUALIFICATION_RUNNER, +) +from control_plane.trading_studio.management.commands.materialize_hyperscalper_cohort_001 import ( + DATASET_SHA256, + DATASET_VERSION_ID, +) +from control_plane.trading_studio.management.commands.specimen_hyperscalper_cohort_001 import ( + RUNNER_NAME as SPECIMEN_RUNNER, + SCENARIO_NAMES, +) +from control_plane.trading_studio.models import QualificationReplayRun, TradingCohort +from control_plane.trading_studio.qualification import DEQPrimitives, ReplayMode + + +ALLOWED_RUNNERS = frozenset((SPECIMEN_RUNNER, QUALIFICATION_RUNNER)) + + +class Command(BaseCommand): + help = "Read-only JSON export of completed Cohort001 CANARY_ONLY DEQ ledger data." + + def add_arguments(self, parser): + parser.add_argument("--cohort-id", required=True, type=UUID) + parser.add_argument("--output", required=True, help="JSON output path.") + + def handle(self, *args, **options): + output = Path(options["output"]) + try: + with transaction.atomic(): + # Must be the first statement in this transaction; PostgreSQL rejects writes thereafter. + with connection.cursor() as cursor: + cursor.execute("SET TRANSACTION READ ONLY") + payload = self._payload(options["cohort_id"]) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text( + json.dumps(payload, indent=2, sort_keys=True, ensure_ascii=True) + "\n", + encoding="utf-8", + ) + except (OSError, ValueError, TradingCohort.DoesNotExist) as error: + raise CommandError(str(error)) from error + self.stdout.write(json.dumps({"cohort_id": str(options["cohort_id"]), "output": str(output)})) + + @classmethod + def _payload(cls, cohort_id: UUID) -> dict: + cohort = TradingCohort.objects.select_related("dataset_version").get(pk=cohort_id) + cls._validate_cohort(cohort) + memberships = list( + cohort.memberships.select_related("strategy_version").order_by("ordinal") + ) + if len(memberships) != 20 or [member.ordinal for member in memberships] != list(range(1, 21)): + raise ValueError("Cohort001 must contain exactly 20 ordered memberships.") + + runs = list( + QualificationReplayRun.objects.filter( + strategy_version_id__in=[member.strategy_version_id for member in memberships], + dataset_version_id=cohort.dataset_version_id, + replay_mode=ReplayMode.CANARY_ONLY, + runner_name__in=ALLOWED_RUNNERS, + ) + .prefetch_related("ledger_rows") + .order_by("strategy_version_id", "fold", "scenario_name", "runner_name") + ) + run_index = cls._validated_run_index(memberships, runs) + folds = cohort.policy_snapshot["reconstruction_v1"]["folds"] + + strategies = [] + for member in memberships: + runner = SPECIMEN_RUNNER if member.ordinal == 1 else QUALIFICATION_RUNNER + strategy = { + "ordinal": member.ordinal, + "membership_id": str(member.id), + "strategy_version_id": str(member.strategy_version_id), + "runner_name": runner, + "genome": member.strategy_version.genome, + "feature_triples": cls._feature_triples(member.strategy_version.genome), + "folds": [], + } + for fold in folds: + fold_name = fold["fold"] + scenarios = [] + for scenario_name in SCENARIO_NAMES: + run = run_index[(member.strategy_version_id, fold_name, scenario_name)] + ledger = [row.result for row in sorted(run.ledger_rows.all(), key=lambda row: row.sequence)] + scenarios.append( + { + "qualification_run_id": str(run.id), + "scenario": scenario_name, + "summary": run.summary, + "deq_samples": ledger, + "deq_aggregate": cls._deq_aggregate(ledger), + } + ) + strategy["folds"].append({"fold": fold_name, "scenarios": scenarios}) + strategies.append(strategy) + + return { + "contract": "Cohort001-DEQ-ledger-export-v1", + "cohort_id": str(cohort.id), + "dataset_version_id": str(cohort.dataset_version_id), + "dataset_sha256": cohort.dataset_version.content_hash, + "replay_mode": ReplayMode.CANARY_ONLY, + "allowed_runner_names": sorted(ALLOWED_RUNNERS), + "strategies": strategies, + } + + @staticmethod + def _validate_cohort(cohort) -> None: + reconstruction = cohort.policy_snapshot.get("reconstruction_v1") + if ( + cohort.dataset_version_id != DATASET_VERSION_ID + or cohort.dataset_version.content_hash != DATASET_SHA256 + or not isinstance(reconstruction, dict) + or reconstruction.get("provenance") != "RECONSTRUCTED_FROM_FROZEN_SPEC_V1" + or not isinstance(reconstruction.get("folds"), list) + or len(reconstruction["folds"]) != 4 + ): + raise ValueError("Cohort or dataset is not the validated frozen Cohort001 canary dataset.") + + @classmethod + def _validated_run_index(cls, memberships, runs) -> dict: + member_by_strategy = {member.strategy_version_id: member for member in memberships} + expected = { + (member.strategy_version_id, fold["fold"], scenario) + for member in memberships + for fold in member.cohort.policy_snapshot["reconstruction_v1"]["folds"] + for scenario in SCENARIO_NAMES + } + indexed = {} + for run in runs: + member = member_by_strategy[run.strategy_version_id] + expected_runner = SPECIMEN_RUNNER if member.ordinal == 1 else QUALIFICATION_RUNNER + key = (run.strategy_version_id, run.fold, run.scenario_name) + if run.runner_name != expected_runner or key not in expected or key in indexed: + raise ValueError("Cohort001 contains an unexpected or duplicate CANARY_ONLY replay.") + indexed[key] = run + if set(indexed) != expected: + raise ValueError("Cohort001 DEQ export requires a complete 20 x 4 x 13 replay matrix.") + return indexed + + @staticmethod + def _feature_triples(genome: dict) -> list[dict] | None: + try: + state = parse_hs22(genome.get("combo")) + except (AttributeError, HS22SchemaError, TypeError, ValueError): + return None + return [feature_definition(variant).artifact() for variant in state.variants()] + + @staticmethod + def _deq_aggregate(ledger: list[dict]) -> dict: + result = {"trade_count": len(ledger), "fields": {}} + for field in fields(DEQPrimitives): + values = [row.get("deq", {}).get(field.name) for row in ledger] + available = [value for value in values if value is not None] + aggregate = { + "available_count": len(available), + "null_count": len(values) - len(available), + } + if available and all(isinstance(value, bool) for value in available): + aggregate.update(true_count=sum(available), false_count=len(available) - sum(available)) + elif available: + aggregate.update(min=min(available), max=max(available), mean=sum(available) / len(available)) + else: + aggregate["mean"] = None + result["fields"][field.name] = aggregate + return result diff --git a/control_plane/trading_studio/management/commands/export_cohort_failure_context_v1.py b/control_plane/trading_studio/management/commands/export_cohort_failure_context_v1.py new file mode 100644 index 0000000..f730444 --- /dev/null +++ b/control_plane/trading_studio/management/commands/export_cohort_failure_context_v1.py @@ -0,0 +1,235 @@ +from __future__ import annotations + +import hashlib +import json +from collections import Counter +from pathlib import Path +from uuid import UUID + +from django.core.management.base import BaseCommand, CommandError +from django.db import connection, transaction + +from control_plane.trading_studio.management.commands.export_cohort_deq_v1 import Command as DEQExport + + +CONTRACT = "Cohort001-failure-context-export-v1" +LABEL_VERSION = "v1" +# All signed DEQ values are already expressed in the trade direction. +THRESHOLDS = { + "good_entry_return_1_bars_bps_min": 5.0, + "wrong_direction_return_1_bars_bps_max": -5.0, + "late_signal_return_1_bars_bps_max": 0.0, + "late_signal_return_5_bars_bps_min": 5.0, + "high_mae_entry_bps_max": -25.0, + "recovery_dependent_bars_min": 3, +} +LABEL_PRECEDENCE = ( + "GOOD_SIGNAL_BAD_MONETIZATION", + "RECOVERY_DEPENDENT", + "HIGH_MAE_ENTRY", + "WRONG_DIRECTION", + "LATE_SIGNAL", + "GOOD_ENTRY", + "NO_EDGE", +) + + +class Command(BaseCommand): + help = "Read-only Parquet export of Cohort001 DEQ failure context and analytical labels." + + def add_arguments(self, parser): + parser.add_argument("--cohort-id", required=True, type=UUID) + parser.add_argument( + "--output-dir", + required=True, + help="Directory for cohort_failure_context_v1.parquet and its manifest.", + ) + parser.add_argument("--parquet-output", help="Optional explicit Parquet output path.") + parser.add_argument("--manifest-output", help="Optional explicit manifest output path.") + + def handle(self, *args, **options): + output_dir = Path(options["output_dir"]) + parquet_path = Path(options["parquet_output"] or output_dir / "cohort_failure_context_v1.parquet") + manifest_path = Path( + options["manifest_output"] or output_dir / "cohort_failure_context_v1.manifest.json" + ) + try: + with transaction.atomic(): + self._set_read_only_transaction() + payload = DEQExport._payload(options["cohort_id"]) + rows = self._rows(payload) + self._write_parquet(rows, parquet_path) + manifest = self._manifest(payload, rows, parquet_path) + manifest_path.parent.mkdir(parents=True, exist_ok=True) + manifest_path.write_text( + json.dumps(manifest, indent=2, sort_keys=True, ensure_ascii=True) + "\n", + encoding="utf-8", + ) + except (ImportError, OSError, ValueError) as error: + raise CommandError(str(error)) from error + self.stdout.write( + json.dumps( + { + "cohort_id": str(options["cohort_id"]), + "parquet": str(parquet_path), + "manifest": str(manifest_path), + "database_writes": False, + }, + sort_keys=True, + ) + ) + + @staticmethod + def _set_read_only_transaction() -> None: + """Use the database's explicit read-only mode where it is supported.""" + if connection.vendor == "postgresql": + # This must be the first database statement in the atomic block. + with connection.cursor() as cursor: + cursor.execute("SET TRANSACTION READ ONLY") + + @classmethod + def _rows(cls, payload: dict) -> list[dict]: + rows = [] + for strategy in payload["strategies"]: + for fold in strategy["folds"]: + for scenario in fold["scenarios"]: + rescue = scenario["summary"].get("rescue") + for sequence, ledger in enumerate(scenario["deq_samples"]): + deq = ledger.get("deq", {}) + label, rationale = cls._label(ledger, deq) + rows.append( + { + "cohort_id": payload["cohort_id"], + "dataset_version_id": payload["dataset_version_id"], + "dataset_sha256": payload["dataset_sha256"], + "ordinal": strategy["ordinal"], + "membership_id": strategy["membership_id"], + "strategy_version_id": strategy["strategy_version_id"], + "runner_name": strategy["runner_name"], + "qualification_run_id": scenario["qualification_run_id"], + "fold": fold["fold"], + "scenario": scenario["scenario"], + "ledger_sequence": sequence, + "ledger_identity": ledger.get("identity"), + "direction": cls._direction(ledger), + "signal_bar": ledger.get("signal_bar"), + "entry_bar": ledger.get("entry_bar"), + "entry_at": ledger.get("entry_at"), + "entry_reference_price": ledger.get("entry_reference_price"), + "entry_execution_price": ledger.get("entry_execution_price"), + "net_pnl": ledger.get("net_pnl"), + "gross_pnl": ledger.get("gross_pnl"), + "failure_label": label, + "label_rationale": rationale, + "label_version": LABEL_VERSION, + "return_1_bars_bps": deq.get("return_1_bars_bps"), + "return_5_bars_bps": deq.get("return_5_bars_bps"), + "mfe_bps": deq.get("mfe_bps"), + "mae_bps": deq.get("mae_bps"), + "winner_negative_first": deq.get("winner_negative_first"), + "recovery_bars": deq.get("recovery_bars"), + # JSON columns preserve every raw ledger, genome, context, and rescue field. + "raw_deq_json": cls._canonical(deq), + "raw_ledger_json": cls._canonical(ledger), + "genome_json": cls._canonical(strategy["genome"]), + "feature_triples_json": cls._canonical(strategy["feature_triples"]), + "run_summary_json": cls._canonical(scenario["summary"]), + "rescue_json": cls._canonical(rescue), + "context_json": cls._canonical(ledger.get("context")), + } + ) + return rows + + @staticmethod + def _direction(ledger: dict) -> str | None: + take_profit = ledger.get("take_profit_price") + entry = ledger.get("entry_execution_price") + if take_profit is None or entry is None: + return None + return "LONG" if take_profit > entry else "SHORT" if take_profit < entry else None + + @staticmethod + def _label(ledger: dict, deq: dict) -> tuple[str, str]: + r1 = deq.get("return_1_bars_bps") + r5 = deq.get("return_5_bars_bps") + mae = deq.get("mae_bps") + recovery = deq.get("recovery_bars") + net_pnl = ledger.get("net_pnl") + good_signal = r1 is not None and r1 >= THRESHOLDS["good_entry_return_1_bars_bps_min"] + if good_signal and net_pnl is not None and net_pnl <= 0: + return "GOOD_SIGNAL_BAD_MONETIZATION", "return_1_bars_bps >= 5 and net_pnl <= 0" + if net_pnl is not None and net_pnl > 0 and ( + deq.get("winner_negative_first") is True + or (recovery is not None and recovery >= THRESHOLDS["recovery_dependent_bars_min"]) + ): + return "RECOVERY_DEPENDENT", "profitable after negative-first path or recovery >= 3 bars" + if mae is not None and mae <= THRESHOLDS["high_mae_entry_bps_max"]: + return "HIGH_MAE_ENTRY", "mae_bps <= -25" + if r1 is not None and r1 <= THRESHOLDS["wrong_direction_return_1_bars_bps_max"]: + return "WRONG_DIRECTION", "return_1_bars_bps <= -5" + if ( + r1 is not None + and r5 is not None + and r1 <= THRESHOLDS["late_signal_return_1_bars_bps_max"] + and r5 >= THRESHOLDS["late_signal_return_5_bars_bps_min"] + ): + return "LATE_SIGNAL", "return_1_bars_bps <= 0 and return_5_bars_bps >= 5" + if good_signal: + return "GOOD_ENTRY", "return_1_bars_bps >= 5" + return "NO_EDGE", "no preceding label rule matched" + + @staticmethod + def _canonical(value) -> str: + return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True) + + @classmethod + def _write_parquet(cls, rows: list[dict], path: Path) -> None: + try: + import pyarrow as pa + import pyarrow.parquet as pq + except ImportError as error: + raise ImportError("pyarrow is required to write the Parquet export.") from error + path.parent.mkdir(parents=True, exist_ok=True) + pq.write_table(pa.Table.from_pylist(rows), path, compression="zstd") + + @classmethod + def _manifest(cls, payload: dict, rows: list[dict], parquet_path: Path) -> dict: + labels = Counter(row["failure_label"] for row in rows) + return { + "contract": CONTRACT, + "cohort_id": payload["cohort_id"], + "dataset_version_id": payload["dataset_version_id"], + "dataset_sha256": payload["dataset_sha256"], + "source_contract": payload["contract"], + "allowed_runner_names": payload["allowed_runner_names"], + "row_count": len(rows), + "label_counts": {label: labels.get(label, 0) for label in LABEL_PRECEDENCE}, + "label_version": LABEL_VERSION, + "label_precedence": list(LABEL_PRECEDENCE), + "thresholds": THRESHOLDS, + "parquet": { + "path": str(parquet_path), + "sha256": cls._file_hash(parquet_path), + }, + "provenance": { + "source": "persisted QualificationReplayRun and QualificationReplayLedger records", + "replay_mode": payload["replay_mode"], + "raw_fields": [ + "raw_deq_json", "raw_ledger_json", "genome_json", "feature_triples_json", + "run_summary_json", "rescue_json", "context_json", + ], + }, + "no_write_confirmation": { + "database_writes": False, + "read_only_transaction": True, + "note": "Only the requested Parquet and manifest artifact paths were written.", + }, + } + + @staticmethod + def _file_hash(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as artifact: + for block in iter(lambda: artifact.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() diff --git a/gpu_batch01_decision_equivalence_v1.json b/gpu_batch01_decision_equivalence_v1.json new file mode 100644 index 0000000..2da460b --- /dev/null +++ b/gpu_batch01_decision_equivalence_v1.json @@ -0,0 +1,1171 @@ +{ + "artifact": "GPU_BATCH01_DECISION_EQUIVALENCE_V1", + "price_series": "close", + "passed": true, + "variants": [ + { + "request_id": "batch01_17_14_1.5", + "family": "bollinger", + "valid_count": 129586, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_14_2", + "family": "bollinger", + "valid_count": 129586, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_14_2.5", + "family": "bollinger", + "valid_count": 129586, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_16_1.5", + "family": "bollinger", + "valid_count": 129584, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_16_2", + "family": "bollinger", + "valid_count": 129584, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_16_2.5", + "family": "bollinger", + "valid_count": 129584, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_18_1.5", + "family": "bollinger", + "valid_count": 129582, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_18_2", + "family": "bollinger", + "valid_count": 129582, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_18_2.5", + "family": "bollinger", + "valid_count": 129582, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_20_1.5", + "family": "bollinger", + "valid_count": 129580, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_20_2", + "family": "bollinger", + "valid_count": 129580, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_20_2.5", + "family": "bollinger", + "valid_count": 129580, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_22_1.5", + "family": "bollinger", + "valid_count": 129578, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_22_2", + "family": "bollinger", + "valid_count": 129578, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_22_2.5", + "family": "bollinger", + "valid_count": 129578, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_24_1.5", + "family": "bollinger", + "valid_count": 129576, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_24_2", + "family": "bollinger", + "valid_count": 129576, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_17_24_2.5", + "family": "bollinger", + "valid_count": 129576, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_18_14_1.5", + "family": "bollinger", + "valid_count": 129586, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_18_14_2", + "family": "bollinger", + "valid_count": 129586, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_18_14_2.5", + "family": "bollinger", + "valid_count": 129586, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + "crossings_below_exact": true, + "state_transition_conditions_exact": true + }, + { + "request_id": "batch01_18_16_1.5", + "family": "bollinger", + "valid_count": 129584, + "price_comparison_signs_exact": true, + "price_comparison_sign_mismatches": 0, + "crossings_above_exact": true, + 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b/gpu_batch01_independent_review_v1_2.md @@ -0,0 +1,21 @@ +# GPU Batch01 Independent Review V1.2 + +Decision: `REJECT` + +## Exact Blockers + +1. All 97 features do not pass validation: calibration 94/97, holdout 46/97, and adversarial 67/97. +2. Supertrend ATR limits were frozen as zero by the runner, not at the approved bounded values. The period-10 ATR calibration drift is `7.105427357601002e-15`; the holdout drift is `1.4210854715202004e-14`. +3. Calibration-derived continuous limits do not generalize: holdout fails 48 Bollinger/Keltner requests plus three Supertrend ATR requests; adversarial fails all 30 Keltner requests. +4. V1.2 contains no performance artifact or complete runtime provenance. +5. ATR validation does not require ATR shape and NaN-mask equality before measuring finite errors. + +## Satisfied Checks + +- The contract contains 97 unique requests and was frozen before holdout/adversarial validation. +- Reported finite numeric limits are mechanically enforced. +- Donchian and PSAR outputs pass exact checks on supplied evidence. +- Supertrend bands, predicates, direction, transitions, and final outputs pass exact checks. +- Historical role semantics are honestly marked `UNVERIFIABLE_FROM_RECOVERED_SOURCE` and non-blocking. + +`GPU_FEATURE_PARITY_CONTRACT_V1_2_VALIDATED` must not be issued. diff --git a/gpu_batch01_numeric_analysis_v1.json b/gpu_batch01_numeric_analysis_v1.json new file mode 100644 index 0000000..a92a345 --- /dev/null +++ b/gpu_batch01_numeric_analysis_v1.json @@ -0,0 +1,1907 @@ +{ + "artifact": "GPU_BATCH01_NUMERIC_ANALYSIS_V1", + "oracle_npz_sha256": "e1f2268b634e50eb65b9b5175ea369e52bf0a2039b18049030849948a45fd149", + "data_csv_sha256": 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+{"artifact":"GPU_BATCH01_PERFORMANCE_METRICS_V1_1","families":[{"cold":{"compute_seconds":0.003065667988266796,"device_to_host_seconds":0.0006752620101906359,"host_to_device_seconds":0.00010705000022426248,"max_gpu_memory_bytes":48369152},"cold_values_per_second":760931715.0220335,"family":"bollinger","values":2332764,"variants":36,"warm":{"compute_seconds":0.0030614440329372883,"device_to_host_seconds":0.0006707339780405164,"host_to_device_seconds":7.96440290287137e-05,"max_gpu_memory_bytes":48369152},"warm_values_per_second":761981592.6413786},{"cold":{"compute_seconds":0.00028294604271650314,"device_to_host_seconds":0.0001819260069169104,"host_to_device_seconds":7.823598571121693e-05,"max_gpu_memory_bytes":7779840},"cold_values_per_second":2290153959.316022,"family":"donchian","values":647990,"variants":10,"warm":{"compute_seconds":0.0002682899939827621,"device_to_host_seconds":0.00018216704484075308,"host_to_device_seconds":7.761199958622456e-05,"max_gpu_memory_bytes":7779840},"warm_values_per_second":2415259661.311238},{"cold":{"compute_seconds":30.859290324966423,"device_to_host_seconds":0.0008443809929303825,"host_to_device_seconds":7.618800736963749e-05,"max_gpu_memory_bytes":27707904},"cold_values_per_second":75593.57248448124,"family":"keltner","values":2332764,"variants":36,"warm":{"compute_seconds":30.80078762996709,"device_to_host_seconds":0.0007362319738604128,"host_to_device_seconds":0.00010204297723248601,"max_gpu_memory_bytes":27707904},"warm_values_per_second":75737.1541281749},{"cold":{"compute_seconds":9.649663921969477,"device_to_host_seconds":0.00011956499656662345,"host_to_device_seconds":0.00010370800737291574,"max_gpu_memory_bytes":4148736},"cold_values_per_second":20145.468440347915,"family":"psar","values":194397,"variants":3,"warm":{"compute_seconds":9.652134977979586,"device_to_host_seconds":0.00010578898945823312,"host_to_device_seconds":9.908498032018542e-05,"max_gpu_memory_bytes":4148736},"warm_values_per_second":20140.310971976458},{"cold":{"compute_seconds":34.126689486030955,"device_to_host_seconds":0.00030309800058603287,"host_to_device_seconds":9.794998914003372e-05,"max_gpu_memory_bytes":12444672},"cold_values_per_second":22785.333465125277,"family":"supertrend","values":777588,"variants":12,"warm":{"compute_seconds":34.110405425017234,"device_to_host_seconds":0.0002850039745680988,"host_to_device_seconds":0.00011255801655352116,"max_gpu_memory_bytes":12444672},"warm_values_per_second":22796.211018638372}],"measurement":"CUDA synchronize before each interval; cold is first family invocation, warm is second"} diff --git a/gpu_batch01_stateful_trace_comparison_v1_1.json b/gpu_batch01_stateful_trace_comparison_v1_1.json new file mode 100644 index 0000000..24fe0ee --- /dev/null +++ b/gpu_batch01_stateful_trace_comparison_v1_1.json @@ -0,0 +1 @@ +{"artifact":"GPU_BATCH01_STATEFUL_TRACE_REPORT_V1_1","corpora":[{"artifact":"GPU_BATCH01_STATEFUL_TRACE_COMPARISON_V1_1","corpus":"calibration","passed":false,"records":[{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":false,"family":"supertrend","mismatched_keys":["atr"],"request_id":"batch01_19_10_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":false,"family":"supertrend","mismatched_keys":["atr"],"request_id":"batch01_19_10_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":false,"family":"supertrend","mismatched_keys":["atr"],"request_id":"batch01_19_10_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.01","required_keys":["acceleration","bullish","extreme","output","reversal"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.02","required_keys":["acceleration","bullish","extreme","output","reversal"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.03","required_keys":["acceleration","bullish","extreme","output","reversal"]}]},{"artifact":"GPU_BATCH01_STATEFUL_TRACE_COMPARISON_V1_1","corpus":"holdout","passed":false,"records":[{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":false,"family":"supertrend","mismatched_keys":["atr"],"request_id":"batch01_19_10_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":false,"family":"supertrend","mismatched_keys":["atr"],"request_id":"batch01_19_10_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":false,"family":"supertrend","mismatched_keys":["atr"],"request_id":"batch01_19_10_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.01","required_keys":["acceleration","bullish","extreme","output","reversal"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.02","required_keys":["acceleration","bullish","extreme","output","reversal"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.03","required_keys":["acceleration","bullish","extreme","output","reversal"]}]},{"artifact":"GPU_BATCH01_STATEFUL_TRACE_COMPARISON_V1_1","corpus":"adversarial","passed":true,"records":[{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_8_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_10_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_10_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_10_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_12_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_2","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_3","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"supertrend","mismatched_keys":[],"request_id":"batch01_19_14_4","required_keys":["atr","direction","initialized","lower","output","upper"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.01","required_keys":["acceleration","bullish","extreme","output","reversal"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.02","required_keys":["acceleration","bullish","extreme","output","reversal"]},{"exact":true,"family":"psar","mismatched_keys":[],"request_id":"batch01_28_1_0.03","required_keys":["acceleration","bullish","extreme","output","reversal"]}]}],"passed":false} diff --git a/gpu_batch01_v1_1_evidence_manifest.json b/gpu_batch01_v1_1_evidence_manifest.json new file mode 100644 index 0000000..dbfbf1c --- /dev/null +++ b/gpu_batch01_v1_1_evidence_manifest.json @@ -0,0 +1 @@ +{"artifact":"GPU_BATCH01_V1_1_EVIDENCE_MANIFEST","calibration_gpu_timing":{"compute_seconds":149.54003936197842,"device_to_host_seconds":0.7904343790141866,"host_to_device_seconds":0.1820402479788754,"max_gpu_memory_bytes":114601472},"cuda":"12.8","data_hashes":{"adversarial_csv":"20036fe14201f5e68f932edeb17189e54e875a13f948057cbe06900cadd7dc1c","calibration_csv":"7fb56bb05b0edc2348407765cd693efadf334307c25877dfb52acfe4221e5d00","holdout_csv":"2eef64de206e9ec908b8d7042f58cd4fcf22bfa0b99b856d27246e2432281904","request_json":"7ca0c8578dd81c3ae60898d83e6e73f9bf8889a39a873b451c7cdaf036cbaf24"},"gpu":"NVIDIA GB10","image_env":"artifex/gpu-feature-v1:v1_1","implementation_hashes":{"gpu_batch01_v1_1_runner.py":"c105954199d753aa4f404ca595b1d106e3f2647aad3725fad8b9274b61a881f7","gpu_feature_engine_v1.py":"1d5033b3ae078f9cd5ebe7e51f6ea4bd82d39737229425137370388fb6a205a9","gpu_feature_parity_contract_v1_1.py":"ad78e5f6200393540a45293535937ebfb2769a3474e6f207b867045c42ce477a"},"platform":"Linux-6.17.0-1026-nvidia-aarch64-with-glibc2.35","python":"3.10.12 (main, Jun 22 2026, 18:55:27) [GCC 11.4.0]","role_semantics":{"reason":"Batch01 request records contain no strategy-role assignment; provide --role-usage-artifact generated from historical lineage.","status":"not_reconstructable"},"schema_version":"1.1","source_hashes":{"historical_band_channel.py":"1f019058049ef4717edce14e8a9af11e74ec1f1ab2d12d26a938449017930d91"},"torch":"2.7.1+cu128"} diff --git a/gpu_batch01_v1_1_runner.py b/gpu_batch01_v1_1_runner.py new file mode 100644 index 0000000..86b4ff8 --- /dev/null +++ b/gpu_batch01_v1_1_runner.py @@ -0,0 +1,372 @@ +"""Reproducible GPU Batch01 V1.1 evidence runner for the ARM64 CUDA image. + +The runner deliberately evaluates the CPU historical port and the CUDA engine +separately. CUDA unavailability is an error: GPU results are never replaced by +CPU results. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import os +import platform +import sys +import time +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +from gpu_feature_engine_v1 import evaluate_batch, psar_trace, supertrend_trace +from gpu_feature_parity_contract_v1_1 import ( + calibrate, + canonical_bytes, + compare_stateful_trace, + corpus_manifest, + cpu_psar_trace, + cpu_supertrend_trace, + deterministic_adversarial_ohlcv, + freeze_contract, + historical_cpu_oracle, + nan_gap_semantics_manifest, + validate_frozen_contract, +) + +ARTIFACT_PREFIX = "gpu_batch01_v1_1" + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def write_json(path: Path, payload: Mapping[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(canonical_bytes(dict(payload)) + b"\n") + + +def read_ohlcv(path: Path) -> dict[str, np.ndarray]: + with path.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + required = ("close", "high", "low", "volume") + if not rows or not set(required).issubset(rows[0]): + raise ValueError("CSV must contain non-empty close, high, low, volume columns") + values = { + name: np.asarray([float(row[name]) for row in rows], dtype=np.float64) for name in required + } + if not all(np.isfinite(value).all() for value in values.values()): + raise ValueError( + "historical NaN/gap semantics are undefined; non-finite OHLCV inputs are rejected" + ) + return values + + +def write_ohlcv_csv(path: Path, ohlcv: Mapping[str, np.ndarray]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=["close", "high", "low", "volume"]) + writer.writeheader() + writer.writerows( + {name: float(ohlcv[name][index]) for name in writer.fieldnames} + for index in range(len(ohlcv["close"])) + ) + + +def gpu_outputs( + request: Mapping[str, Any], ohlcv: Mapping[str, np.ndarray], device: torch.device +) -> tuple[dict[str, np.ndarray], dict[str, float]]: + torch.cuda.reset_peak_memory_stats(device) + transfer_start = time.perf_counter() + tensors = tuple( + torch.as_tensor(ohlcv[name], dtype=torch.float64, device=device) + for name in ("close", "high", "low", "volume") + ) + torch.cuda.synchronize(device) + transfer_seconds = time.perf_counter() - transfer_start + compute_start = time.perf_counter() + outputs = evaluate_batch(dict(request), *tensors) + torch.cuda.synchronize(device) + compute_seconds = time.perf_counter() - compute_start + host_start = time.perf_counter() + result = {key: value.detach().cpu().numpy() for key, value in outputs.items()} + torch.cuda.synchronize(device) + return result, { + "host_to_device_seconds": transfer_seconds, + "compute_seconds": compute_seconds, + "device_to_host_seconds": time.perf_counter() - host_start, + "max_gpu_memory_bytes": int(torch.cuda.max_memory_allocated(device)), + } + + +def traces( + requests: list[Mapping[str, Any]], ohlcv: Mapping[str, np.ndarray], device: torch.device +) -> dict[str, Any]: + close, high, low = (ohlcv[name] for name in ("close", "high", "low")) + gpu_values = tuple( + torch.as_tensor(value, dtype=torch.float64, device=device) for value in (close, high, low) + ) + records = [] + for item in requests: + indicator = int(item["indicator_id"]) + if indicator == 19: + expected = cpu_supertrend_trace( + close, high, low, int(item["period"]), float(item["p1"]) + ) + actual = supertrend_trace(*gpu_values, int(item["period"]), float(item["p1"])) + trace_type = "supertrend" + elif indicator == 28: + expected = cpu_psar_trace(close, high, low, float(item["p1"])) + actual = psar_trace(*gpu_values, float(item["p1"])) + trace_type = "psar" + else: + continue + torch.cuda.synchronize(device) + compared = compare_stateful_trace( + expected, + {key: value.detach().cpu().numpy() for key, value in actual.items()}, + trace_type=trace_type, + ) + records.append({"request_id": str(item["request_id"]), "family": trace_type, **compared}) + return { + "artifact": "GPU_BATCH01_STATEFUL_TRACE_COMPARISON_V1_1", + "passed": all(item["exact"] for item in records), + "records": records, + } + + +def performance( + request: Mapping[str, Any], ohlcv: Mapping[str, np.ndarray], device: torch.device +) -> dict[str, Any]: + # Each family is measured through the same CUDA evaluate_batch entry point. + families: dict[str, list[Mapping[str, Any]]] = {} + names = { + 17: "bollinger", + 18: "bollinger", + 19: "supertrend", + 20: "donchian", + 21: "donchian", + 23: "keltner", + 24: "keltner", + 28: "psar", + } + for item in request["requests"]: + families.setdefault(names[int(item["indicator_id"])], []).append(item) + results = [] + for family, items in sorted(families.items()): + family_request = {"requests": items} + _, cold = gpu_outputs(family_request, ohlcv, device) + _, warm = gpu_outputs(family_request, ohlcv, device) + values = len(ohlcv["close"]) * len(items) + results.append( + { + "family": family, + "variants": len(items), + "values": values, + "cold": cold, + "warm": warm, + "cold_values_per_second": values / cold["compute_seconds"] + if cold["compute_seconds"] + else None, + "warm_values_per_second": values / warm["compute_seconds"] + if warm["compute_seconds"] + else None, + } + ) + return { + "artifact": "GPU_BATCH01_PERFORMANCE_METRICS_V1_1", + "measurement": ( + "CUDA synchronize before each interval; cold is first family invocation, warm is second" + ), + "families": results, + } + + +def role_semantics(path: Path | None) -> dict[str, Any]: + if path is None: + return { + "status": "not_reconstructable", + "reason": "Batch01 request records contain no strategy-role assignment; provide " + "--role-usage-artifact generated from historical lineage.", + } + if not path.is_file(): + raise ValueError(f"role usage artifact does not exist: {path}") + return {"status": "source_artifact_recorded", "path": str(path), "sha256": sha256_file(path)} + + +def implementation_hashes() -> dict[str, str]: + root = Path(__file__).resolve().parent + return { + path.name: sha256_file(path) + for path in ( + root / "gpu_batch01_v1_1_runner.py", + root / "gpu_feature_engine_v1.py", + root / "gpu_feature_parity_contract_v1_1.py", + ) + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--calibration-csv", type=Path, required=True) + parser.add_argument("--holdout-csv", type=Path, required=True) + parser.add_argument("--request-json", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--adversarial-length", type=int, default=256) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--role-usage-artifact", type=Path) + parser.add_argument("--image-env", default=os.getenv("GPU_BATCH01_IMAGE_ENV")) + args = parser.parse_args() + if not torch.cuda.is_available(): + raise SystemExit("CUDA GPU is required; no CPU fallback is available") + args.output_dir.mkdir(parents=True, exist_ok=True) + request = json.loads(args.request_json.read_text(encoding="utf-8")) + requests = request.get("requests") + if not isinstance(requests, list) or not requests: + raise ValueError("request JSON must contain a non-empty requests list") + device = torch.device("cuda") + + # Calibration is the only stage allowed to derive and freeze acceptance limits. + calibration_ohlcv = read_ohlcv(args.calibration_csv) + calibration_cpu = historical_cpu_oracle(args.calibration_csv, requests) + calibration_gpu, calibration_timing = gpu_outputs(request, calibration_ohlcv, device) + calibration_manifest = corpus_manifest( + {"calibration": calibration_ohlcv}, + { + "source_csv": str(args.calibration_csv), + "source_sha256": sha256_file(args.calibration_csv), + }, + ) + calibration_evidence = calibrate( + calibration_gpu, + calibration_cpu["outputs"], + requests, + calibration_ohlcv["close"], + calibration_manifest, + ) + contract = freeze_contract(calibration_evidence) + write_json(args.output_dir / "gpu_feature_parity_calibration_v1_1.json", calibration_evidence) + write_json(args.output_dir / "gpu_feature_parity_contract_v1_1.json", contract) + np.savez_compressed( + args.output_dir / f"{ARTIFACT_PREFIX}_calibration_cpu_oracle.npz", + **calibration_cpu["outputs"], + ) + np.savez_compressed( + args.output_dir / f"{ARTIFACT_PREFIX}_calibration_gpu_outputs.npz", **calibration_gpu + ) + + adversarial = deterministic_adversarial_ohlcv(length=args.adversarial_length, seed=args.seed) + adversarial_csv = args.output_dir / f"{ARTIFACT_PREFIX}_adversarial.csv" + write_ohlcv_csv(adversarial_csv, adversarial) + corpora = { + "calibration": calibration_ohlcv, + "holdout": read_ohlcv(args.holdout_csv), + "adversarial": adversarial, + } + corpus = corpus_manifest( + corpora, + { + "adversarial_generator": "deterministic_adversarial_ohlcv", + "seed": args.seed, + "role_semantics": role_semantics(args.role_usage_artifact), + }, + ) + write_json(args.output_dir / "gpu_feature_parity_corpus_manifest_v1_1.json", corpus) + write_json( + args.output_dir / "gpu_feature_parity_nan_gap_semantics_v1_1.json", + nan_gap_semantics_manifest(), + ) + + validations, trace_reports = [], [] + for name in ("calibration", "holdout", "adversarial"): + if name == "calibration": + csv_path, cpu, gpu, timing = ( + args.calibration_csv, + calibration_cpu, + calibration_gpu, + calibration_timing, + ) + else: + csv_path = args.holdout_csv if name == "holdout" else adversarial_csv + cpu = historical_cpu_oracle(csv_path, requests) + gpu, timing = gpu_outputs(request, corpora[name], device) + validation = validate_frozen_contract(contract, gpu, cpu["outputs"], corpora[name]["close"]) + validation.update( + { + "corpus": name, + "cpu_oracle": {key: value for key, value in cpu.items() if key != "outputs"}, + "gpu_timing": timing, + } + ) + validations.append(validation) + trace_reports.append({"corpus": name, **traces(requests, corpora[name], device)}) + if name != "calibration": + np.savez_compressed( + args.output_dir / f"{ARTIFACT_PREFIX}_{name}_cpu_oracle.npz", **cpu["outputs"] + ) + np.savez_compressed( + args.output_dir / f"{ARTIFACT_PREFIX}_{name}_gpu_outputs.npz", **gpu + ) + validation_payload = { + "artifact": "GPU_FEATURE_PARITY_VALIDATION_REPORT_V1_1", + "contract_sha256": hashlib.sha256(canonical_bytes(contract)).hexdigest(), + "passed": all(value["passed"] for value in validations), + "corpora": validations, + } + trace_payload = { + "artifact": "GPU_BATCH01_STATEFUL_TRACE_REPORT_V1_1", + "passed": all(value["passed"] for value in trace_reports), + "corpora": trace_reports, + } + write_json(args.output_dir / "gpu_feature_parity_validation_v1_1.json", validation_payload) + write_json(args.output_dir / "gpu_batch01_stateful_trace_comparison_v1_1.json", trace_payload) + write_json( + args.output_dir / "gpu_batch01_performance_metrics_v1_1.json", + performance(request, corpora["holdout"], device), + ) + root = Path(__file__).resolve().parent + manifest = { + "artifact": "GPU_BATCH01_V1_1_EVIDENCE_MANIFEST", + "schema_version": "1.1", + "python": sys.version, + "platform": platform.platform(), + "torch": torch.__version__, + "cuda": torch.version.cuda, + "gpu": torch.cuda.get_device_properties(device).name, + "image_env": args.image_env, + "source_hashes": { + "historical_band_channel.py": sha256_file( + root / "control_plane/trading_studio/indicators/historical_band_channel.py" + ) + }, + "implementation_hashes": implementation_hashes(), + "data_hashes": { + "request_json": sha256_file(args.request_json), + "calibration_csv": sha256_file(args.calibration_csv), + "holdout_csv": sha256_file(args.holdout_csv), + "adversarial_csv": sha256_file(adversarial_csv), + }, + "calibration_gpu_timing": calibration_timing, + "role_semantics": role_semantics(args.role_usage_artifact), + } + write_json(args.output_dir / "gpu_batch01_v1_1_evidence_manifest.json", manifest) + print( + json.dumps( + { + "output_dir": str(args.output_dir), + "validation_passed": validation_payload["passed"], + "trace_passed": trace_payload["passed"], + }, + allow_nan=False, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/gpu_batch01_v1_2_evidence_manifest.json b/gpu_batch01_v1_2_evidence_manifest.json new file mode 100644 index 0000000..2a1faf7 --- /dev/null +++ b/gpu_batch01_v1_2_evidence_manifest.json @@ -0,0 +1 @@ +{"artifact":"GPU_BATCH01_V1_2_EVIDENCE_MANIFEST","artifacts":["gpu_feature_parity_calibration_v1_2.json","gpu_feature_parity_contract_v1_2.json","gpu_feature_parity_contract_v1_2.md","gpu_feature_parity_validation_calibration_v1_2.json","gpu_feature_parity_validation_holdout_v1_2.json","gpu_feature_parity_validation_adversarial_v1_2.json","gpu_feature_parity_role_surface_v1_2.json","gpu_feature_parity_review_template_v1_2.json"],"atr_limits":{"mae":0.0,"max_absolute_error":0.0},"contract_sha256":"8f8ee3459b54d7674b5f946c8030ca21ad5b2b2691b0446f33616da3917df47f","feature_count":97,"schema_version":"1.2","status":"evidence_generated_not_validated","validation_passed":false} diff --git a/gpu_batch01_v1_2_runner.py b/gpu_batch01_v1_2_runner.py new file mode 100644 index 0000000..381121c --- /dev/null +++ b/gpu_batch01_v1_2_runner.py @@ -0,0 +1,203 @@ +"""GPU Batch01 V1.2 full-family, frozen-contract evidence runner.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import platform +import sys +import time +from pathlib import Path +from typing import Any, Mapping + +import numpy as np +import torch + +from control_plane.trading_studio.indicators.historical_band_channel import evaluate_band_channel +from gpu_batch01_v1_1_runner import read_ohlcv, write_ohlcv_csv +from gpu_feature_engine_v1 import evaluate_batch as evaluate_v1_batch +from gpu_feature_engine_v1_2 import supertrend_trace +from gpu_feature_parity_contract_v1_1 import deterministic_adversarial_ohlcv +from gpu_feature_parity_contract_v1_2 import ( + CALIBRATION_ARTIFACT, + calibrate_output, + calibrate_supertrend, + canonical_bytes, + freeze_contract, + review_template, + role_surface, + validate_all_frozen_contract, +) + +ARTIFACT_PREFIX = "gpu_batch01_v1_2" +ATR_LIMIT_KEYS = ("max_absolute_error", "mae") +DEFAULT_ATR_LIMITS = {"max_absolute_error": 1.5e-14, "mae": 1.5e-14} + + +def write_json(path: Path, payload: Mapping[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(canonical_bytes(dict(payload)) + b"\n") + + +def _sha256_file(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def _atr_limits(atr_limits: Mapping[str, float] | None) -> dict[str, float]: + limits = dict(DEFAULT_ATR_LIMITS if atr_limits is None else atr_limits) + if set(limits) != set(ATR_LIMIT_KEYS) or any(not np.isfinite(value) or value < 0 for value in limits.values()): + raise ValueError("ATR limits must contain finite, non-negative max_absolute_error and mae") + return limits + + +def _cpu_outputs(requests: list[Mapping[str, Any]], ohlcv: Mapping[str, np.ndarray]) -> dict[str, np.ndarray]: + return { + str(item["request_id"]): evaluate_band_channel( + int(item["indicator_id"]), ohlcv["close"], ohlcv["high"], ohlcv["low"], ohlcv["volume"], int(item["period"]), float(item["p1"]) + ) + for item in requests + } + + +def _gpu_outputs(requests: list[Mapping[str, Any]], ohlcv: Mapping[str, np.ndarray], device: torch.device) -> tuple[dict[str, np.ndarray], dict[str, tuple[dict[str, np.ndarray], dict[str, np.ndarray]]]]: + tensors = tuple(torch.as_tensor(ohlcv[name], dtype=torch.float64, device=device) for name in ("close", "high", "low", "volume")) + other = [item for item in requests if int(item["indicator_id"]) != 19] + outputs = evaluate_v1_batch({"requests": other}, *tensors) if other else {} + traces = {} + for item in requests: + if int(item["indicator_id"]) != 19: + continue + key = str(item["request_id"]) + actual = supertrend_trace(*tensors[:3], int(item["period"]), float(item["p1"])) + # The CPU trace is imported lazily to keep historical output generation explicit. + from gpu_feature_parity_contract_v1_2 import cpu_supertrend_trace + expected = cpu_supertrend_trace(ohlcv["close"], ohlcv["high"], ohlcv["low"], int(item["period"]), float(item["p1"])) + traces[key] = (expected, {name: value.detach().cpu().numpy() for name, value in actual.items()}) + outputs[key] = actual["output"] + torch.cuda.synchronize(device) + return {key: value.detach().cpu().numpy() for key, value in outputs.items()}, traces + + +def _gpu_workload(requests: list[Mapping[str, Any]], ohlcv: Mapping[str, np.ndarray], device: torch.device) -> None: + tensors = tuple(torch.as_tensor(ohlcv[name], dtype=torch.float64, device=device) for name in ("close", "high", "low", "volume")) + other = [item for item in requests if int(item["indicator_id"]) != 19] + if other: + evaluate_v1_batch({"requests": other}, *tensors) + for item in requests: + if int(item["indicator_id"]) == 19: + supertrend_trace(*tensors[:3], int(item["period"]), float(item["p1"])) + + +def performance(requests: list[Mapping[str, Any]], ohlcv: Mapping[str, np.ndarray], device: torch.device) -> dict[str, Any]: + """Measure V1.2 GPU evaluation with synchronized intervals.""" + families: dict[str, list[Mapping[str, Any]]] = {} + for item in requests: + family = {17: "bollinger", 18: "bollinger", 19: "supertrend", 20: "donchian", 21: "donchian", 23: "keltner", 24: "keltner", 28: "psar"}[int(item["indicator_id"])] + families.setdefault(family, []).append(item) + results = [] + for family, items in sorted(families.items()): + timings = [] + for _ in range(2): + torch.cuda.synchronize(device) + started = time.perf_counter() + _gpu_workload(items, ohlcv, device) + torch.cuda.synchronize(device) + timings.append(time.perf_counter() - started) + values = len(ohlcv["close"]) * len(items) + results.append({"family": family, "variants": len(items), "values": values, "cold_seconds": timings[0], "warm_seconds": timings[1], "cold_values_per_second": values / timings[0] if timings[0] else None, "warm_values_per_second": values / timings[1] if timings[1] else None}) + return {"artifact": "GPU_BATCH01_PERFORMANCE_METRICS_V1_2", "schema_version": "1.2", "measurement": "CUDA synchronize before and after each interval; cold is the first family invocation and warm is the second", "families": results} + + +def _markdown(contract: Mapping[str, Any], roles: Mapping[str, Any]) -> str: + counts: dict[str, int] = {} + for limit in contract["feature_limits"].values(): + counts[limit["family"]] = counts.get(limit["family"], 0) + 1 + return "\n".join(( + "# GPU Batch01 V1.2 Frozen Validation Contract", "", + "All 97 observed Batch01 outputs are evaluated against `historical_band_channel`.", + "", "## Evidence Classes", "", + "- Donchian and PSAR outputs: exact.", + "- Bollinger and Keltner outputs: bounded by calibration-only limits.", + "- Supertrend final output, bands, direction, transitions, and branch predicates: exact; ATR uses supplied fixed bounds.", + "", "## Frozen Coverage", "", + *[f"- `{family}`: {count}" for family, count in sorted(counts.items())], + "", "## Role Status", "", + "Unrecovered strategy roles are `UNVERIFIABLE_FROM_RECOVERED_SOURCE`; they are recorded but are not parity blockers.", + f"Role records: {len(roles['records'])}.", "", + )) + + +def run(calibration_csv: Path, holdout_csv: Path, request_json: Path, output_dir: Path, *, adversarial_length: int = 256, seed: int = 0, atr_limits: Mapping[str, float] | None = None, device: torch.device | None = None) -> dict[str, Any]: + if device is None: + if not torch.cuda.is_available(): + raise RuntimeError("CUDA GPU is required; no CPU fallback is available") + device = torch.device("cuda") + atr_limits = _atr_limits(atr_limits) + request = json.loads(request_json.read_text(encoding="utf-8")) + requests = request.get("requests") + if not isinstance(requests, list) or len(requests) != 97: + raise ValueError("V1.2 requires the complete 97-request Batch01 manifest") + if len({str(item["request_id"]) for item in requests}) != 97: + raise ValueError("Batch01 request IDs must be unique") + output_dir.mkdir(parents=True, exist_ok=True) + calibration, holdout = read_ohlcv(calibration_csv), read_ohlcv(holdout_csv) + adversarial = deterministic_adversarial_ohlcv(length=adversarial_length, seed=seed) + write_ohlcv_csv(output_dir / f"{ARTIFACT_PREFIX}_adversarial.csv", adversarial) + roles = role_surface(requests) + write_json(output_dir / "gpu_feature_parity_role_surface_v1_2.json", roles) + + # Calibration is the sole point at which continuous-family limits are derived. + calibration_cpu = _cpu_outputs(requests, calibration) + calibration_gpu, calibration_traces = _gpu_outputs(requests, calibration, device) + evidence = {"artifact": CALIBRATION_ARTIFACT, "schema_version": "1.2", "feature_count": 97, "records": [ + calibrate_supertrend(str(item["request_id"]), *calibration_traces[str(item["request_id"])], atr_limits=atr_limits) + if int(item["indicator_id"]) == 19 else calibrate_output(str(item["request_id"]), int(item["indicator_id"]), calibration_cpu[str(item["request_id"])], calibration_gpu[str(item["request_id"])] ) + for item in requests + ]} + contract = freeze_contract(evidence) # Frozen before holdout/adversarial are evaluated. + write_json(output_dir / "gpu_feature_parity_calibration_v1_2.json", evidence) + write_json(output_dir / "gpu_feature_parity_contract_v1_2.json", contract) + (output_dir / "gpu_feature_parity_contract_v1_2.md").write_text(_markdown(contract, roles), encoding="utf-8") + np.savez_compressed(output_dir / f"{ARTIFACT_PREFIX}_calibration_cpu_oracle.npz", **calibration_cpu) + np.savez_compressed(output_dir / f"{ARTIFACT_PREFIX}_calibration_gpu_outputs.npz", **calibration_gpu) + + reports = {} + for name, corpus in (("calibration", calibration), ("holdout", holdout), ("adversarial", adversarial)): + expected = calibration_cpu if name == "calibration" else _cpu_outputs(requests, corpus) + actual, traces = (calibration_gpu, calibration_traces) if name == "calibration" else _gpu_outputs(requests, corpus, device) + report = validate_all_frozen_contract(contract, expected, actual, traces) + report.update({"corpus": name, "feature_count": 97}) + reports[name] = report + write_json(output_dir / f"gpu_feature_parity_validation_{name}_v1_2.json", report) + if name != "calibration": + np.savez_compressed(output_dir / f"{ARTIFACT_PREFIX}_{name}_cpu_oracle.npz", **expected) + np.savez_compressed(output_dir / f"{ARTIFACT_PREFIX}_{name}_gpu_outputs.npz", **actual) + review = review_template() + review["required_review"].extend(["all 97 Batch01 requests are represented", "role status remains UNVERIFIABLE_FROM_RECOVERED_SOURCE where lineage is unrecovered"]) + write_json(output_dir / "gpu_feature_parity_review_template_v1_2.json", review) + write_json(output_dir / "gpu_batch01_performance_metrics_v1_2.json", performance(requests, holdout, device)) + provenance = {"artifact": "GPU_BATCH01_RUNTIME_PROVENANCE_V1_2", "schema_version": "1.2", "python": sys.version, "platform": platform.platform(), "torch": torch.__version__, "cuda": torch.version.cuda, "gpu": torch.cuda.get_device_properties(device).name, "image_env": os.getenv("GPU_BATCH01_IMAGE_ENV"), "source_hashes": {path.name: _sha256_file(path) for path in (Path(__file__), Path(__file__).with_name("gpu_batch01_v1_1_runner.py"), Path(__file__).with_name("gpu_feature_engine_v1.py"), Path(__file__).with_name("gpu_feature_engine_v1_2.py"), Path(__file__).with_name("gpu_feature_parity_contract_v1_1.py"), Path(__file__).with_name("gpu_feature_parity_contract_v1_2.py"), Path(__file__).parent / "control_plane/trading_studio/indicators/historical_band_channel.py")}, "data_hashes": {"request_json": _sha256_file(request_json), "calibration_csv": _sha256_file(calibration_csv), "holdout_csv": _sha256_file(holdout_csv), "adversarial_csv": _sha256_file(output_dir / f"{ARTIFACT_PREFIX}_adversarial.csv")}, "adversarial": {"generator": "deterministic_adversarial_ohlcv", "length": adversarial_length, "seed": seed}} + write_json(output_dir / "gpu_batch01_runtime_provenance_v1_2.json", provenance) + artifacts = ["gpu_feature_parity_calibration_v1_2.json", "gpu_feature_parity_contract_v1_2.json", "gpu_feature_parity_contract_v1_2.md", "gpu_feature_parity_validation_calibration_v1_2.json", "gpu_feature_parity_validation_holdout_v1_2.json", "gpu_feature_parity_validation_adversarial_v1_2.json", "gpu_feature_parity_role_surface_v1_2.json", "gpu_feature_parity_review_template_v1_2.json", "gpu_batch01_performance_metrics_v1_2.json", "gpu_batch01_runtime_provenance_v1_2.json"] + manifest = {"artifact": "GPU_BATCH01_V1_2_EVIDENCE_MANIFEST", "schema_version": "1.2", "status": "evidence_generated_not_validated", "feature_count": 97, "contract_sha256": hashlib.sha256(canonical_bytes(contract)).hexdigest(), "atr_limits": atr_limits, "validation_passed": all(report["passed"] for report in reports.values()), "artifacts": artifacts} + write_json(output_dir / "gpu_batch01_v1_2_evidence_manifest.json", manifest) + return manifest + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--calibration-csv", type=Path, required=True) + parser.add_argument("--holdout-csv", type=Path, required=True) + parser.add_argument("--request-json", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--adversarial-length", type=int, default=256) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--supertrend-atr-max-absolute-error", type=float, default=DEFAULT_ATR_LIMITS["max_absolute_error"]) + parser.add_argument("--supertrend-atr-mae", type=float, default=DEFAULT_ATR_LIMITS["mae"]) + args = parser.parse_args() + print(json.dumps(run(args.calibration_csv, args.holdout_csv, args.request_json, args.output_dir, adversarial_length=args.adversarial_length, seed=args.seed, atr_limits={"max_absolute_error": args.supertrend_atr_max_absolute_error, "mae": args.supertrend_atr_mae}), allow_nan=False)) + + +if __name__ == "__main__": + main() diff --git a/gpu_feature_engine_v1.py b/gpu_feature_engine_v1.py new file mode 100644 index 0000000..aa16bf3 --- /dev/null +++ b/gpu_feature_engine_v1.py @@ -0,0 +1,430 @@ +"""Standalone CUDA Batch01 band/channel prototype. + +This module deliberately has no Artifex imports. It consumes frozen oracle +artifacts and OHLCV CSV files mounted read-only, writes only its output cache, +and records measured parity rather than asserting it. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import os +import time +from pathlib import Path +from typing import Any + +import numpy as np +import torch + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _load_ohlcv(path: Path, device: torch.device) -> tuple[torch.Tensor, ...]: + with path.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + if not rows or not {"close", "high", "low", "volume"}.issubset(rows[0]): + raise ValueError("CSV must contain close, high, low, and volume columns") + return tuple( + torch.as_tensor([float(row[name]) for row in rows], dtype=torch.float64, device=device) + for name in ("close", "high", "low", "volume") + ) + + +def _rolling_windows(values: torch.Tensor, period: int) -> torch.Tensor: + return values.unfold(0, period, 1) + + +def _empty(values: torch.Tensor) -> torch.Tensor: + return torch.full_like(values, float("nan")) + + +def _sma(values: torch.Tensor, period: int) -> torch.Tensor: + out = _empty(values) + if values.numel() < period: + return out + out[period - 1 :] = _rolling_windows(values, period).mean(dim=1) + return out + + +def _ema(values: torch.Tensor, period: int) -> torch.Tensor: + out = _empty(values) + if values.numel() < period: + return out + alpha = 2.0 / (period + 1) + out[period - 1] = values[:period].sum() / period + for index in range(period, values.numel()): + out[index] = alpha * values[index] + (1.0 - alpha) * out[index - 1] + return out + + +def _rma(values: torch.Tensor, period: int) -> torch.Tensor: + out = _empty(values) + valid = torch.nonzero(~torch.isnan(values), as_tuple=False).flatten() + if valid.numel() < period: + return out + start = int(valid[period - 1].item()) + out[start] = values[valid[:period]].sum() / period + alpha = 1.0 / period + for index in range(start + 1, values.numel()): + out[index] = out[index - 1] if torch.isnan(values[index]) else alpha * values[index] + (1.0 - alpha) * out[index - 1] + return out + + +def _true_range(close: torch.Tensor, high: torch.Tensor, low: torch.Tensor) -> torch.Tensor: + out = torch.empty_like(close) + out[0] = high[0] - low[0] + out[1:] = torch.stack((high[1:] - low[1:], (high[1:] - close[:-1]).abs(), (low[1:] - close[:-1]).abs())).amax(dim=0) + return out + + +def _bollinger(close: torch.Tensor, period: int, multipliers: list[float]) -> tuple[torch.Tensor, torch.Tensor]: + upper, lower = _empty(close).repeat(len(multipliers), 1), _empty(close).repeat(len(multipliers), 1) + if close.numel() < period: + return upper, lower + windows = _rolling_windows(close, period) + mean = windows.mean(dim=1) + # Keep reductions on-device and vectorized; sample variance matches the oracle formula. + standard_deviation = ((windows - mean[:, None]).square().sum(dim=1) / (period - 1)).sqrt() + multiplier = torch.tensor(multipliers, dtype=torch.float64, device=close.device) + upper[:, period - 1 :] = mean[None, :] + multiplier[:, None] * standard_deviation[None, :] + lower[:, period - 1 :] = mean[None, :] - multiplier[:, None] * standard_deviation[None, :] + return upper, lower + + +def _donchian(values: torch.Tensor, period: int, upper: bool) -> torch.Tensor: + out = _empty(values) + if values.numel() >= period: + windows = _rolling_windows(values, period) + out[period - 1 :] = windows.amax(dim=1) if upper else windows.amin(dim=1) + return out + + +def _keltner(close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, period: int, multipliers: list[float]) -> tuple[torch.Tensor, torch.Tensor]: + basis, atr = _ema(close, period), _rma(_true_range(close, high, low), period) + upper, lower = _empty(close).repeat(len(multipliers), 1), _empty(close).repeat(len(multipliers), 1) + valid = ~torch.isnan(basis) & ~torch.isnan(atr) + multiplier = torch.tensor(multipliers, dtype=torch.float64, device=close.device) + upper[:, valid] = basis[valid] + multiplier[:, None] * atr[valid] + lower[:, valid] = basis[valid] - multiplier[:, None] * atr[valid] + return upper, lower + + +def _supertrend(close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, period: int, multipliers: list[float]) -> torch.Tensor: + count, length = len(multipliers), close.numel() + out = _empty(close).repeat(count, 1) + atr, upper, lower = _rma(_true_range(close, high, low), period), _empty(close).repeat(count), _empty(close).repeat(count) + multiplier = torch.tensor(multipliers, dtype=torch.float64, device=close.device) + direction = torch.ones(count, dtype=torch.float64, device=close.device) + for index in range(period, length): + midpoint, basic_upper, basic_lower = (high[index] + low[index]) / 2.0, None, None + basic_upper, basic_lower = midpoint + multiplier * atr[index], midpoint - multiplier * atr[index] + if index == period: + upper, lower = basic_upper, basic_lower + out[:, index] = torch.where(close[index] > lower, lower, upper) + direction = torch.where(close[index] > out[:, index], 1.0, -1.0) + continue + upper = torch.where((close[index - 1] <= upper) & ~torch.isnan(upper), torch.minimum(basic_upper, upper), basic_upper) + lower = torch.where((close[index - 1] >= lower) & ~torch.isnan(lower), torch.maximum(basic_lower, lower), basic_lower) + out[:, index] = torch.where(direction == 1.0, torch.where(close[index] >= lower, lower, upper), torch.where(close[index] <= upper, upper, lower)) + direction = torch.where(close[index] > out[:, index], 1.0, -1.0) + return out + + +def _psar(close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, steps: list[float]) -> torch.Tensor: + out = _empty(close).repeat(len(steps), 1) + if close.numel() < 2: + return out + step = torch.tensor(steps, dtype=torch.float64, device=close.device) + bullish = torch.ones(len(steps), dtype=torch.bool, device=close.device) + acceleration = torch.full_like(step, 0.02) + extreme, sar = high[0].repeat(len(steps)), low[0].repeat(len(steps)) + for index in range(1, close.numel()): + sar = sar + acceleration * (extreme - sar) + bull_sar = torch.minimum(sar, low[index - 1]) + bear_sar = torch.maximum(sar, high[index - 1]) + if index >= 2: + bull_sar, bear_sar = torch.minimum(bull_sar, low[index - 2]), torch.maximum(bear_sar, high[index - 2]) + reversal_down, reversal_up = bullish & (low[index] < bull_sar), ~bullish & (high[index] > bear_sar) + candidate = torch.where(bullish, bull_sar, bear_sar) + sar = torch.where(reversal_down | reversal_up, extreme, candidate) + new_bullish = torch.where(reversal_down, False, torch.where(reversal_up, True, bullish)) + new_extreme = torch.where(reversal_down, low[index], torch.where(reversal_up, high[index], extreme)) + rising = new_bullish & ~reversal_up & (high[index] > extreme) + falling = ~new_bullish & ~reversal_down & (low[index] < extreme) + new_extreme = torch.where(rising, high[index], torch.where(falling, low[index], new_extreme)) + acceleration = torch.where(reversal_down | reversal_up, 0.02, torch.where(rising | falling, torch.minimum(acceleration + step, torch.full_like(step, 0.2)), acceleration)) + bullish, extreme = new_bullish, new_extreme + out[:, index] = sar + return out + + +def supertrend_trace(close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, period: int, multiplier: float) -> dict[str, torch.Tensor]: + """Return the GPU state-machine trace for one Supertrend variant. + + The bar loop advances state, while every state operation remains a CUDA + tensor operation; this is not a CPU or per-variant fallback. + """ + output = _supertrend(close, high, low, period, [multiplier])[0] + atr = _rma(_true_range(close, high, low), period) + upper, lower = _empty(close), _empty(close) + direction, initialized = torch.zeros_like(close, dtype=torch.int8), torch.zeros_like(close, dtype=torch.bool) + for index in range(period, close.numel()): + midpoint = (high[index] + low[index]) / 2.0 + basic_upper, basic_lower = midpoint + multiplier * atr[index], midpoint - multiplier * atr[index] + if index == period: + upper[index], lower[index] = basic_upper, basic_lower + else: + upper[index] = torch.minimum(basic_upper, upper[index - 1]) if close[index - 1] <= upper[index - 1] else basic_upper + lower[index] = torch.maximum(basic_lower, lower[index - 1]) if close[index - 1] >= lower[index - 1] else basic_lower + direction[index] = torch.where(close[index] > output[index], 1, -1) + initialized[index] = True + return {"output": output, "atr": atr, "upper": upper, "lower": lower, "direction": direction, "initialized": initialized} + + +def psar_trace(close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, step: float) -> dict[str, torch.Tensor]: + """Return the GPU state-machine trace for one PSAR variant.""" + output = _psar(close, high, low, [step])[0] + bullish = torch.zeros_like(close, dtype=torch.bool) + extreme, acceleration = _empty(close), _empty(close) + reversal = torch.zeros_like(close, dtype=torch.bool) + if close.numel() < 2: + return {"output": output, "bullish": bullish, "extreme": extreme, "acceleration": acceleration, "reversal": reversal} + bull, af, ep, sar = torch.tensor(True, device=close.device), torch.tensor(.02, dtype=torch.float64, device=close.device), high[0], low[0] + for index in range(1, close.numel()): + sar = sar + af * (ep - sar) + candidate = torch.minimum(sar, torch.minimum(low[index - 1], low[index - 2] if index >= 2 else low[index - 1])) if bool(bull) else torch.maximum(sar, torch.maximum(high[index - 1], high[index - 2] if index >= 2 else high[index - 1])) + reverse = (bool(bull) and bool(low[index] < candidate)) or (not bool(bull) and bool(high[index] > candidate)) + if reverse: + bull, sar, ep, af = (torch.tensor(False, device=close.device), ep, low[index], torch.tensor(.02, dtype=torch.float64, device=close.device)) if bool(bull) else (torch.tensor(True, device=close.device), ep, high[index], torch.tensor(.02, dtype=torch.float64, device=close.device)) + else: + sar = candidate + if (bool(bull) and bool(high[index] > ep)) or (not bool(bull) and bool(low[index] < ep)): + ep, af = (high[index], torch.minimum(af + step, torch.tensor(.2, dtype=torch.float64, device=close.device))) if bool(bull) else (low[index], torch.minimum(af + step, torch.tensor(.2, dtype=torch.float64, device=close.device))) + bullish[index], extreme[index], acceleration[index], reversal[index] = bull, ep, af, reverse + return {"output": output, "bullish": bullish, "extreme": extreme, "acceleration": acceleration, "reversal": reversal} + + +def evaluate_batch(request: dict[str, Any], close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, volume: torch.Tensor) -> dict[str, torch.Tensor]: + del volume # Batch01 formulas do not consume volume, but preserve the input contract. + groups: dict[tuple[int, int], list[dict[str, Any]]] = {} + for item in request["requests"]: + groups.setdefault((int(item["indicator_id"]), int(item["period"])), []).append(item) + outputs: dict[str, torch.Tensor] = {} + for (indicator, period), items in groups.items(): + multipliers = [float(item["p1"]) for item in items] + if indicator == 17: + batch = _bollinger(close, period, multipliers)[0] + elif indicator == 18: + batch = _bollinger(close, period, multipliers)[1] + elif indicator == 19: + batch = _supertrend(close, high, low, period, multipliers) + elif indicator == 20: + batch = _donchian(high, period, True).unsqueeze(0) + elif indicator == 21: + batch = _donchian(low, period, False).unsqueeze(0) + elif indicator == 23: + batch = _keltner(close, high, low, period, multipliers)[0] + elif indicator == 24: + batch = _keltner(close, high, low, period, multipliers)[1] + elif indicator == 28: + batch = _psar(close, high, low, multipliers) + else: + raise ValueError(f"unsupported indicator: {indicator}") + for index, item in enumerate(items): + outputs[str(item["request_id"])] = batch[index] + return outputs + + +def _cache_path(cache_dir: Path, data_csv: Path, request_json: Path) -> Path: + key = hashlib.sha256(f"v1:{_sha256(data_csv)}:{_sha256(request_json)}".encode()).hexdigest()[:24] + return cache_dir / f"batch01_gpu_{key}.npz" + + +def _load_or_compute(args: argparse.Namespace, device: torch.device) -> tuple[dict[str, np.ndarray], bool, float]: + cache = _cache_path(args.cache_dir, args.data_csv, args.request_json) + request = json.loads(args.request_json.read_text(encoding="utf-8")) + expected_ids = {item["request_id"] for item in request["requests"]} + if cache.exists(): + with np.load(cache) as saved: + if set(saved.files) == expected_ids: + return {key: saved[key] for key in saved.files}, True, 0.0 + start = time.perf_counter() + outputs = evaluate_batch(request, *_load_ohlcv(args.data_csv, device)) + torch.cuda.synchronize(device) + elapsed = time.perf_counter() - start + host = {key: value.detach().cpu().numpy() for key, value in outputs.items()} + args.cache_dir.mkdir(parents=True, exist_ok=True) + np.savez_compressed(cache, **host) + return host, False, elapsed + + +def smoke(device: torch.device) -> dict[str, Any]: + values = torch.arange(1024, dtype=torch.float64, device=device) + result = (values.square().sum() / values.numel()).item() + torch.cuda.synchronize(device) + return {"passed": bool(np.isfinite(result)), "device": str(device), "torch": torch.__version__, "cuda": torch.version.cuda, "value": result} + + +def _family(indicator_id: int) -> str: + return {17: "bollinger", 18: "bollinger", 19: "supertrend", 20: "donchian", 21: "donchian", 23: "keltner", 24: "keltner", 28: "psar"}[indicator_id] + + +def _ordered_bits(values: np.ndarray) -> np.ndarray: + bits = values.view(np.uint64) + return np.where(bits >> 63 != 0, ~bits, bits | np.uint64(1 << 63)) + + +def _numeric_measurement(actual: np.ndarray, expected: np.ndarray) -> dict[str, Any]: + finite = np.isfinite(actual) & np.isfinite(expected) + difference = np.abs(actual[finite] - expected[finite]) + differing = difference != 0 + relative = difference[np.abs(expected[finite]) != 0] / np.abs(expected[finite][np.abs(expected[finite]) != 0]) + ulps = np.abs(_ordered_bits(actual[finite]).astype(object) - _ordered_bits(expected[finite]).astype(object)) + ulps = np.asarray(ulps, dtype=np.float64) + return { + "finite_compared_count": int(finite.sum()), + "finite_differing_count": int(differing.sum()), + "max_absolute_error": float(difference.max()) if difference.size else 0.0, + "max_relative_error": float(relative.max()) if relative.size else 0.0, + "mae": float(difference.mean()) if difference.size else 0.0, + "max_ulp": int(ulps.max()) if ulps.size else 0, + "p50_ulp": float(np.percentile(ulps, 50)) if ulps.size else 0.0, + "p95_ulp": float(np.percentile(ulps, 95)) if ulps.size else 0.0, + "p99_ulp": float(np.percentile(ulps, 99)) if ulps.size else 0.0, + } + + +def _decision_measurement(close: np.ndarray, actual: np.ndarray, expected: np.ndarray) -> dict[str, Any]: + valid = np.isfinite(close) & np.isfinite(actual) & np.isfinite(expected) + oracle_sign = np.sign(close[valid] - expected[valid]).astype(np.int8) + gpu_sign = np.sign(close[valid] - actual[valid]).astype(np.int8) + sign_exact = bool(np.array_equal(oracle_sign, gpu_sign)) + oracle_above = oracle_sign > 0 + gpu_above = gpu_sign > 0 + oracle_below = oracle_sign < 0 + gpu_below = gpu_sign < 0 + return { + "valid_count": int(valid.sum()), + "price_comparison_signs_exact": sign_exact, + "price_comparison_sign_mismatches": int(np.count_nonzero(oracle_sign != gpu_sign)), + "crossings_above_exact": bool(np.array_equal((~oracle_above[:-1]) & oracle_above[1:], (~gpu_above[:-1]) & gpu_above[1:])), + "crossings_below_exact": bool(np.array_equal((~oracle_below[:-1]) & oracle_below[1:], (~gpu_below[:-1]) & gpu_below[1:])), + "state_transition_conditions_exact": sign_exact + and bool(np.array_equal((~oracle_above[:-1]) & oracle_above[1:], (~gpu_above[:-1]) & gpu_above[1:])) + and bool(np.array_equal((~oracle_below[:-1]) & oracle_below[1:], (~gpu_below[:-1]) & gpu_below[1:])), + } + + +def _band_relationship(actual_upper: np.ndarray, actual_lower: np.ndarray, expected_upper: np.ndarray, expected_lower: np.ndarray) -> dict[str, Any]: + valid = np.isfinite(actual_upper) & np.isfinite(actual_lower) & np.isfinite(expected_upper) & np.isfinite(expected_lower) + oracle_relation = np.sign(expected_upper[valid] - expected_lower[valid]).astype(np.int8) + gpu_relation = np.sign(actual_upper[valid] - actual_lower[valid]).astype(np.int8) + return {"valid_count": int(valid.sum()), "upper_lower_relationship_exact": bool(np.array_equal(oracle_relation, gpu_relation)), "upper_lower_relationship_mismatches": int(np.count_nonzero(oracle_relation != gpu_relation))} + + +def _write_json(path: Path, value: dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, indent=2, allow_nan=False) + "\n", encoding="utf-8") + + +def _contract_markdown(contract: dict[str, Any]) -> str: + lines = ["# GPU Feature Parity Contract V1", "", f"Status: `{contract['status']}`.", "", "The contract requires exact structure and NaN placement, exact decision/state-transition conditions, and per-family measured numeric limits. Numeric drift is never accepted when it changes a decision.", "", "## Family Limits", ""] + for family, limit in contract["family_tolerances"].items(): + lines.append(f"- `{family}`: max abs {limit['max_absolute_error']:.17g}, max rel {limit['max_relative_error']:.17g}, max ULP {limit['max_ulp']}, MAE {limit['mae']:.17g}.") + lines.extend(["", "## Gates", "", "- Shapes, dtypes, and NaN masks must match exactly.", "- Price-comparison signs, crossings above/below, and state-transition conditions must match exactly.", "- Bollinger and Keltner upper/lower relationships must match exactly.", ""]) + return "\n".join(lines) + + +def parity_report(args: argparse.Namespace, device: torch.device) -> dict[str, Any]: + outputs, cache_hit, elapsed = _load_or_compute(args, device) + request = json.loads(args.request_json.read_text(encoding="utf-8")) + close = _load_ohlcv(args.data_csv, torch.device("cpu"))[0].numpy() + failures, variants, decisions = [], [], [] + family_values: dict[str, list[tuple[np.ndarray, np.ndarray]]] = {} + band_pairs: dict[tuple[int, float], dict[int, tuple[np.ndarray, np.ndarray]]] = {} + with np.load(args.oracle_npz) as oracle: + for item in request["requests"]: + key, actual, expected = item["request_id"], outputs[item["request_id"]], oracle[item["request_id"]] + shape_ok, dtype_ok = actual.shape == expected.shape, actual.dtype == expected.dtype + equal = (actual == expected) | (np.isnan(actual) & np.isnan(expected)) if shape_ok else np.array([False]) + mismatches = int(equal.size - equal.sum()) + nan_exact = bool(shape_ok and np.array_equal(np.isnan(actual), np.isnan(expected))) + numeric = _numeric_measurement(actual, expected) if shape_ok else _numeric_measurement(np.array([], dtype=np.float64), np.array([], dtype=np.float64)) + decision = _decision_measurement(close, actual, expected) if shape_ok else {"state_transition_conditions_exact": False} + family = _family(int(item["indicator_id"])) + record = {"request_id": key, "indicator_id": int(item["indicator_id"]), "family": family, "passed": bool(shape_ok and dtype_ok and nan_exact and mismatches == 0), "shape_match": shape_ok, "dtype_match": dtype_ok, "nan_mask_exact": nan_exact, "value_mismatches": mismatches, **numeric} + variants.append(record) + decisions.append({"request_id": key, "family": family, **decision}) + family_values.setdefault(family, []).append((actual, expected)) + if int(item["indicator_id"]) in {17, 18, 23, 24}: + band_pairs.setdefault((int(item["period"]), float(item["p1"])), {})[int(item["indicator_id"])] = (actual, expected) + if not record["passed"]: + failures.append(record) + family_tolerances = {family: _numeric_measurement(np.concatenate([pair[0] for pair in pairs]), np.concatenate([pair[1] for pair in pairs])) for family, pairs in family_values.items()} + relationships = [] + for (period, multiplier), pair in band_pairs.items(): + upper_id, lower_id = (17, 18) if 17 in pair else (23, 24) + actual_upper, expected_upper = pair[upper_id] + actual_lower, expected_lower = pair[lower_id] + relationships.append({"family": "bollinger" if upper_id == 17 else "keltner", "period": period, "p1": multiplier, **_band_relationship(actual_upper, actual_lower, expected_upper, expected_lower)}) + decision_exact = all(item["state_transition_conditions_exact"] for item in decisions) and all(item["upper_lower_relationship_exact"] for item in relationships) + structural_exact = all(item["shape_match"] and item["dtype_match"] and item["nan_mask_exact"] for item in variants) + numeric_analysis = {"artifact": "GPU_BATCH01_NUMERIC_ANALYSIS_V1", "oracle_npz_sha256": _sha256(args.oracle_npz), "data_csv_sha256": _sha256(args.data_csv), "families": family_tolerances, "variants": variants} + decision_report = {"artifact": "GPU_BATCH01_DECISION_EQUIVALENCE_V1", "price_series": "close", "passed": decision_exact, "variants": decisions, "band_relationships": relationships} + contract = {"artifact": "GPU_FEATURE_PARITY_CONTRACT_V1", "schema_version": 1, "status": "accepted" if structural_exact and decision_exact else "rejected", "structural_nan_exact": structural_exact, "state_transition_conditions_exact": decision_exact, "numeric_limits_derived_from_measurement": True, "family_tolerances": family_tolerances, "numeric_errors_may_not_change_decisions": True, "oracle_npz_sha256": _sha256(args.oracle_npz), "data_csv_sha256": _sha256(args.data_csv)} + _write_json(args.output_dir / "gpu_batch01_numeric_analysis_v1.json", numeric_analysis) + _write_json(args.output_dir / "gpu_batch01_decision_equivalence_v1.json", decision_report) + _write_json(args.output_dir / "gpu_feature_parity_contract_v1.json", contract) + (args.output_dir / "gpu_feature_parity_contract_v1.md").write_text(_contract_markdown(contract), encoding="utf-8") + return {"artifact": "BATCH01_GPU_PARITY_REPORT_V1", "prototype": True, "parity_claimed": False, "smoke": smoke(device), "device": str(device), "variant_count": len(variants), "passed": structural_exact and decision_exact, "failure_count": len(failures), "cache_hit": cache_hit, "compute_seconds": elapsed, "artifacts": [str(args.output_dir / name) for name in ("gpu_batch01_numeric_analysis_v1.json", "gpu_batch01_decision_equivalence_v1.json", "gpu_feature_parity_contract_v1.json", "gpu_feature_parity_contract_v1.md")], "failures": failures} + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--mode", choices=("smoke", "parity"), default="smoke") + parser.add_argument( + "--data-csv", + type=Path, + default=Path(os.getenv("GPU_FEATURE_DATA_CSV", "/data/binance_btcusdt_spot_2m_180d.csv")), + ) + parser.add_argument( + "--oracle-npz", + type=Path, + default=Path(os.getenv("GPU_FEATURE_ORACLE_NPZ", "/oracle/batch01_oracle_outputs.npz")), + ) + parser.add_argument( + "--request-json", + type=Path, + default=Path(os.getenv("GPU_FEATURE_REQUEST_JSON", "/oracle/batch01_oracle_request.json")), + ) + parser.add_argument("--cache-dir", type=Path, default=Path(os.getenv("GPU_FEATURE_CACHE_DIR", "/cache"))) + parser.add_argument( + "--output-dir", + type=Path, + default=Path(os.getenv("GPU_FEATURE_OUTPUT_DIR", "/cache")), + help="directory for gpu_batch01_* analysis and gpu_feature_parity_contract_v1 artifacts", + ) + parser.add_argument("--report", type=Path) + args = parser.parse_args() + if not torch.cuda.is_available(): + raise SystemExit("CUDA GPU is required; torch.cuda.is_available() is false") + device = torch.device("cuda") + report = smoke(device) if args.mode == "smoke" else parity_report(args, device) + encoded = json.dumps(report, indent=2, allow_nan=False) + "\n" + if args.report: + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(encoded, encoding="utf-8") + print(encoded, end="") + + +if __name__ == "__main__": + main() diff --git a/gpu_feature_engine_v1_2.py b/gpu_feature_engine_v1_2.py new file mode 100644 index 0000000..c484b25 --- /dev/null +++ b/gpu_feature_engine_v1_2.py @@ -0,0 +1,78 @@ +"""V1.2 GPU feature engine. + +This module intentionally leaves ``gpu_feature_engine_v1`` unchanged. Only +Supertrend uses the V1.2 ordered, on-device RMA seed implementation. +""" +from __future__ import annotations + +from typing import Any + +import torch + +from gpu_feature_engine_v1 import _empty, _true_range, evaluate_batch as _v1_evaluate_batch + + +def rma_ordered_seed(values: torch.Tensor, period: int) -> torch.Tensor: + """RMA with the historical oracle's left-to-right seed association.""" + out = _empty(values) + valid = torch.nonzero(~torch.isnan(values), as_tuple=False).flatten() + if valid.numel() < period: + return out + seed_bar = int(valid[period - 1].item()) + total = torch.zeros((), dtype=values.dtype, device=values.device) + for index in valid[:period]: + total = total + values[index] + out[seed_bar] = total / period + alpha = 1.0 / period + for index in range(seed_bar + 1, values.numel()): + out[index] = out[index - 1] if torch.isnan(values[index]) else alpha * values[index] + (1.0 - alpha) * out[index - 1] + return out + + +def supertrend_trace(close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, period: int, multiplier: float) -> dict[str, torch.Tensor]: + """Return complete V1.2 Supertrend state, including branch predicates.""" + true_range = _true_range(close, high, low) + atr, output, upper, lower = rma_ordered_seed(true_range, period), _empty(close), _empty(close), _empty(close) + basic_upper, basic_lower = _empty(close), _empty(close) + direction = torch.zeros_like(close, dtype=torch.int8) + initialized = torch.zeros_like(close, dtype=torch.bool) + prior_close_le_upper = torch.zeros_like(close, dtype=torch.bool) + prior_close_ge_lower = torch.zeros_like(close, dtype=torch.bool) + active_long = torch.zeros_like(close, dtype=torch.bool) + close_ge_lower = torch.zeros_like(close, dtype=torch.bool) + close_le_upper = torch.zeros_like(close, dtype=torch.bool) + output_uses_lower = torch.zeros_like(close, dtype=torch.bool) + direction_transition = torch.zeros_like(close, dtype=torch.bool) + for index in range(period, close.numel()): + midpoint = (high[index] + low[index]) / 2.0 + basic_upper[index], basic_lower[index] = midpoint + multiplier * atr[index], midpoint - multiplier * atr[index] + if index == period: + upper[index], lower[index] = basic_upper[index], basic_lower[index] + close_ge_lower[index] = close[index] > lower[index] + output_uses_lower[index] = close_ge_lower[index] + output[index] = lower[index] if output_uses_lower[index] else upper[index] + else: + prior_close_le_upper[index] = close[index - 1] <= upper[index - 1] + prior_close_ge_lower[index] = close[index - 1] >= lower[index - 1] + upper[index] = torch.minimum(basic_upper[index], upper[index - 1]) if prior_close_le_upper[index] else basic_upper[index] + lower[index] = torch.maximum(basic_lower[index], lower[index - 1]) if prior_close_ge_lower[index] else basic_lower[index] + active_long[index] = direction[index - 1] == 1 + close_ge_lower[index] = close[index] >= lower[index] + close_le_upper[index] = close[index] <= upper[index] + output_uses_lower[index] = active_long[index] and close_ge_lower[index] or not active_long[index] and not close_le_upper[index] + output[index] = lower[index] if output_uses_lower[index] else upper[index] + direction[index] = torch.where(close[index] > output[index], 1, -1) + initialized[index] = True + if index > period: + direction_transition[index] = direction[index] != direction[index - 1] + return {"output": output, "true_range": true_range, "atr": atr, "basic_upper": basic_upper, "basic_lower": basic_lower, "upper": upper, "lower": lower, "direction": direction, "initialized": initialized, "prior_close_le_upper": prior_close_le_upper, "prior_close_ge_lower": prior_close_ge_lower, "active_long": active_long, "close_ge_lower": close_ge_lower, "close_le_upper": close_le_upper, "output_uses_lower": output_uses_lower, "direction_transition": direction_transition} + + +def evaluate_batch(request: dict[str, Any], close: torch.Tensor, high: torch.Tensor, low: torch.Tensor, volume: torch.Tensor) -> dict[str, torch.Tensor]: + """Evaluate Batch01, replacing only Supertrend requests with V1.2 logic.""" + other = {"requests": [item for item in request["requests"] if int(item["indicator_id"]) != 19]} + outputs = _v1_evaluate_batch(other, close, high, low, volume) if other["requests"] else {} + for item in request["requests"]: + if int(item["indicator_id"]) == 19: + outputs[str(item["request_id"])] = supertrend_trace(close, high, low, int(item["period"]), float(item["p1"]))["output"] + return outputs diff --git a/gpu_feature_parity_calibration_v1_2.json b/gpu_feature_parity_calibration_v1_2.json new file mode 100644 index 0000000..7e22d6e --- /dev/null +++ b/gpu_feature_parity_calibration_v1_2.json @@ -0,0 +1 @@ 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diff --git a/gpu_feature_parity_contract_v1.json b/gpu_feature_parity_contract_v1.json new file mode 100644 index 0000000..4858fa2 --- /dev/null +++ b/gpu_feature_parity_contract_v1.json @@ -0,0 +1,68 @@ +{ + "artifact": "GPU_FEATURE_PARITY_CONTRACT_V1", + "schema_version": 1, + "status": "accepted", + "structural_nan_exact": true, + "state_transition_conditions_exact": true, + "numeric_limits_derived_from_measurement": true, + "family_tolerances": { + "bollinger": { + "finite_compared_count": 4664916, + "finite_differing_count": 4632967, + "max_absolute_error": 1.82626536116004e-09, + "max_relative_error": 2.989992025370508e-14, + "mae": 6.429624976711859e-10, + "max_ulp": 251, + "p50_ulp": 46.0, + "p95_ulp": 202.0, + "p99_ulp": 229.0 + }, + "supertrend": { + "finite_compared_count": 1555056, + "finite_differing_count": 0, + "max_absolute_error": 0.0, + "max_relative_error": 0.0, + "mae": 0.0, + "max_ulp": 0, + "p50_ulp": 0.0, + "p95_ulp": 0.0, + "p99_ulp": 0.0 + }, + "donchian": { + "finite_compared_count": 1295820, + "finite_differing_count": 0, + "max_absolute_error": 0.0, + "max_relative_error": 0.0, + "mae": 0.0, + "max_ulp": 0, + "p50_ulp": 0.0, + "p95_ulp": 0.0, + "p99_ulp": 0.0 + }, + "keltner": { + "finite_compared_count": 4664916, + "finite_differing_count": 222, + "max_absolute_error": 2.9103830456733704e-11, + "max_relative_error": 4.285296050513011e-16, + "mae": 9.919812152288828e-16, + "max_ulp": 2, + "p50_ulp": 0.0, + "p95_ulp": 0.0, + "p99_ulp": 0.0 + }, + "psar": { + "finite_compared_count": 388794, + "finite_differing_count": 0, + "max_absolute_error": 0.0, + "max_relative_error": 0.0, + "mae": 0.0, + "max_ulp": 0, + "p50_ulp": 0.0, + "p95_ulp": 0.0, + "p99_ulp": 0.0 + } + }, + "numeric_errors_may_not_change_decisions": true, + "oracle_npz_sha256": "e1f2268b634e50eb65b9b5175ea369e52bf0a2039b18049030849948a45fd149", + "data_csv_sha256": "7fb56bb05b0edc2348407765cd693efadf334307c25877dfb52acfe4221e5d00" +} diff --git a/gpu_feature_parity_contract_v1.md b/gpu_feature_parity_contract_v1.md new file mode 100644 index 0000000..03fad7f --- /dev/null +++ b/gpu_feature_parity_contract_v1.md @@ -0,0 +1,19 @@ +# GPU Feature Parity Contract V1 + +Status: `accepted`. + +The contract requires exact structure and NaN placement, exact decision/state-transition conditions, and per-family measured numeric limits. Numeric drift is never accepted when it changes a decision. + +## Family Limits + +- `bollinger`: max abs 1.8262653611600399e-09, max rel 2.9899920253705082e-14, max ULP 251, MAE 6.4296249767118589e-10. +- `supertrend`: max abs 0, max rel 0, max ULP 0, MAE 0. +- `donchian`: max abs 0, max rel 0, max ULP 0, MAE 0. +- `keltner`: max abs 2.9103830456733704e-11, max rel 4.2852960505130111e-16, max ULP 2, MAE 9.9198121522888284e-16. +- `psar`: max abs 0, max rel 0, max ULP 0, MAE 0. + +## Gates + +- Shapes, dtypes, and NaN masks must match exactly. +- Price-comparison signs, crossings above/below, and state-transition conditions must match exactly. +- Bollinger and Keltner upper/lower relationships must match exactly. diff --git a/gpu_feature_parity_contract_v1_1.py b/gpu_feature_parity_contract_v1_1.py new file mode 100644 index 0000000..3e1222a --- /dev/null +++ b/gpu_feature_parity_contract_v1_1.py @@ -0,0 +1,244 @@ +"""GPU_FEATURE_PARITY_CONTRACT_V1_1 calibration and frozen-contract validation. + +Calibration is evidence production only. Validation never measures or derives +limits: it accepts a supplied, immutable contract and enforces its limits. +""" +from __future__ import annotations + +import csv +import hashlib +import json +from pathlib import Path +from typing import Any, Mapping + +import numpy as np + +from control_plane.trading_studio.indicators.historical_band_channel import evaluate_band_channel + +ARTIFACT = "GPU_FEATURE_PARITY_CONTRACT_V1_1" +CALIBRATION_ARTIFACT = "GPU_FEATURE_PARITY_CALIBRATION_V1_1" +VALIDATION_ARTIFACT = "GPU_FEATURE_PARITY_VALIDATION_V1_1" +CORPUS_ARTIFACT = "GPU_FEATURE_PARITY_CORPUS_MANIFEST_V1_1" +NAN_GAP_SEMANTICS_ARTIFACT = "GPU_FEATURE_PARITY_NAN_GAP_SEMANTICS_V1_1" +CPU_ORACLE_ARTIFACT = "GPU_FEATURE_PARITY_CPU_ORACLE_V1_1" +FAMILIES = {17: "bollinger", 18: "bollinger", 19: "supertrend", 20: "donchian", 21: "donchian", 23: "keltner", 24: "keltner", 28: "psar"} +SCENARIOS = ( + "warmup", "constant", "nearly_constant_tiny_variance", "high_variance", "alternating", "uptrend", "downtrend", + "flat_breakout", "breakout_flat", "threshold_equality", "threshold_one_ulp_above", "threshold_one_ulp_below", + "repeated_equality", "zero_range", "tiny_range", "abrupt_atr", +) +TRACE_KEYS = { + "supertrend": frozenset(("output", "atr", "upper", "lower", "direction", "initialized")), + "psar": frozenset(("output", "bullish", "extreme", "acceleration", "reversal")), +} + + +class FrozenContractError(ValueError): + pass + + +def canonical_bytes(value: object) -> bytes: + return json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False).encode("utf-8") + + +def sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def array_sha256(values: np.ndarray) -> str: + values = np.ascontiguousarray(values) + return sha256_bytes(canonical_bytes({"dtype": values.dtype.str, "shape": values.shape}) + b"\0" + values.tobytes()) + + +def write_artifact(path: Path, payload: Mapping[str, Any]) -> None: + path.write_bytes(canonical_bytes(dict(payload)) + b"\n") + + +def deterministic_adversarial_ohlcv(*, length: int = 256, seed: int = 0) -> dict[str, np.ndarray]: + """Generate reproducible valid OHLCV covering all declared edge categories.""" + if length < len(SCENARIOS) * 4: + raise ValueError("length must allow every adversarial scenario") + rng = np.random.default_rng(seed) + block = max(4, length // len(SCENARIOS)) + close = np.empty(length, dtype=np.float64) + spread = 0.15 + rng.random(length) * 0.2 + level = 100.0 + for number, scenario in enumerate(SCENARIOS): + start, stop = number * block, length if number == len(SCENARIOS) - 1 else min(length, (number + 1) * block) + count = stop - start + if scenario == "warmup": values = level + np.arange(count) * 0.03 + elif scenario in {"constant", "repeated_equality"}: values = np.full(count, level) + elif scenario == "nearly_constant_tiny_variance": values = level + np.resize(np.array([0.0, np.spacing(level)]), count) + elif scenario == "high_variance": values = level + np.cumsum(rng.normal(0.0, 3.0, count)) + elif scenario == "alternating": values = level + np.resize(np.array([0.75, -0.75]), count) + elif scenario == "uptrend": values = level + np.arange(1, count + 1) * 0.18 + elif scenario == "downtrend": values = level - np.arange(1, count + 1) * 0.18 + elif scenario == "flat_breakout": values = np.full(count, level); values[count // 2 :] += 4.0 + elif scenario == "breakout_flat": values = np.full(count, level + 4.0) + elif scenario == "threshold_equality": values = np.full(count, level) + elif scenario == "threshold_one_ulp_above": values = np.full(count, np.nextafter(level, np.inf)) + elif scenario == "threshold_one_ulp_below": values = np.full(count, np.nextafter(level, -np.inf)) + elif scenario == "zero_range": values = np.full(count, level); spread[start:stop] = 0.0 + elif scenario == "tiny_range": values = np.full(count, level); spread[start:stop] = np.spacing(level) + elif scenario == "abrupt_atr": values = level + np.cumsum(np.resize(np.array([0.01, -0.01]), count)); spread[start:stop] = np.resize(np.array([0.01, 5.0]), count) + close[start:stop] = values + level = values[-1] + high, low = close + spread, close - spread + volume = 1_000.0 + rng.integers(0, 10_000, length).astype(np.float64) + return {"close": close.astype(np.float64), "high": high.astype(np.float64), "low": low.astype(np.float64), "volume": volume} + + +def corpus_manifest(corpora: Mapping[str, Mapping[str, np.ndarray]], provenance: Mapping[str, Any]) -> dict[str, Any]: + entries = [] + for name, ohlcv in sorted(corpora.items()): + if set(ohlcv) != {"close", "high", "low", "volume"}: + raise ValueError("corpus must contain exactly close, high, low, volume") + entries.append({"name": name, "columns": {key: array_sha256(value) for key, value in sorted(ohlcv.items())}, "row_count": int(len(ohlcv["close"]))}) + return {"artifact": CORPUS_ARTIFACT, "schema_version": "1.1", "scenario_categories": list(SCENARIOS), "corpora": entries, "provenance": dict(provenance)} + + +def nan_gap_semantics_manifest() -> dict[str, Any]: + """Record the deliberate non-finite-input policy separately from parity data.""" + return {"artifact": NAN_GAP_SEMANTICS_ARTIFACT, "schema_version": "1.1", "historical_input_policy": "reject_non_finite_ohlcv", "internal_nan_gap_semantics": "undefined", "validation": "input_rejection_before_historical_evaluation"} + + +def _read_csv(path: Path) -> dict[str, np.ndarray]: + with path.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + if not rows or not {"close", "high", "low", "volume"}.issubset(rows[0]): + raise ValueError("CSV must contain non-empty close, high, low, volume columns") + ohlcv = {key: np.asarray([float(row[key]) for row in rows], dtype=np.float64) for key in ("close", "high", "low", "volume")} + if not all(np.isfinite(values).all() for values in ohlcv.values()): + raise ValueError("historical NaN/gap semantics are undefined; non-finite OHLCV inputs are rejected") + return ohlcv + + +def historical_cpu_oracle(csv_path: Path, requests: list[Mapping[str, Any]]) -> dict[str, Any]: + """Evaluate arbitrary CSV inputs through the historical Batch01 source port.""" + ohlcv = _read_csv(csv_path) + outputs: dict[str, np.ndarray] = {} + for request in requests: + identifier = str(request["request_id"]) + indicator = int(request["indicator_id"]) + if indicator not in FAMILIES or identifier in outputs: + raise ValueError("requests must have unique supported request IDs") + outputs[identifier] = evaluate_band_channel(indicator, **ohlcv, period=int(request["period"]), p1=float(request["p1"])) + return {"artifact": CPU_ORACLE_ARTIFACT, "schema_version": "1.1", "input_csv_sha256": sha256_bytes(csv_path.read_bytes()), "source": {"module": "control_plane.trading_studio.indicators.historical_band_channel", "evaluator": "evaluate_band_channel"}, "outputs": outputs, "output_sha256": {name: array_sha256(value) for name, value in outputs.items()}} + + +def _continuity_decisions(close: np.ndarray, values: np.ndarray) -> dict[str, np.ndarray]: + valid = np.isfinite(close) & np.isfinite(values) + state = np.zeros(values.shape, dtype=np.int8) + state[valid] = np.sign(close[valid] - values[valid]).astype(np.int8) + adjacent = valid[1:] & valid[:-1] + transition = np.zeros(values.shape, dtype=np.bool_) + transition[1:] = adjacent & (state[1:] != state[:-1]) + above = np.zeros(values.shape, dtype=np.bool_) + below = np.zeros(values.shape, dtype=np.bool_) + above[1:] = adjacent & (state[:-1] <= 0) & (state[1:] > 0) + below[1:] = adjacent & (state[:-1] >= 0) & (state[1:] < 0) + return {"valid": valid, "state": state, "transitions": transition, "crossings_above": above, "crossings_below": below} + + +def cpu_supertrend_trace(close: np.ndarray, high: np.ndarray, low: np.ndarray, period: int, multiplier: float) -> dict[str, np.ndarray]: + from control_plane.trading_studio.indicators.historical_band_channel import _rma, _true_range + n = len(close); atr = _rma(_true_range(high, low, close), period) + output = np.full(n, np.nan); upper = output.copy(); lower = output.copy(); direction = np.zeros(n, np.int8); initialized = np.zeros(n, bool) + for i in range(period, n): + if np.isnan(atr[i]): continue + basic_upper = (high[i] + low[i]) / 2 + multiplier * atr[i]; basic_lower = (high[i] + low[i]) / 2 - multiplier * atr[i] + if i == period: + upper[i], lower[i] = basic_upper, basic_lower; output[i] = lower[i] if close[i] > lower[i] else upper[i] + else: + upper[i] = min(basic_upper, upper[i - 1]) if close[i - 1] <= upper[i - 1] else basic_upper + lower[i] = max(basic_lower, lower[i - 1]) if close[i - 1] >= lower[i - 1] else basic_lower + output[i] = lower[i] if direction[i - 1] == 1 and close[i] >= lower[i] else upper[i] if direction[i - 1] == 1 else upper[i] if close[i] <= upper[i] else lower[i] + direction[i] = 1 if close[i] > output[i] else -1; initialized[i] = True + return {"output": output, "atr": atr, "upper": upper, "lower": lower, "direction": direction, "initialized": initialized} + + +def cpu_psar_trace(close: np.ndarray, high: np.ndarray, low: np.ndarray, step: float) -> dict[str, np.ndarray]: + n = len(close); output = np.full(n, np.nan); bullish = np.zeros(n, bool); extreme = np.full(n, np.nan); acceleration = np.full(n, np.nan); reversal = np.zeros(n, bool) + if n < 2: return {"output": output, "bullish": bullish, "extreme": extreme, "acceleration": acceleration, "reversal": reversal} + bull, af, ep, sar = True, .02, high[0], low[0] + for i in range(1, n): + sar += af * (ep - sar); sar = min(sar, low[i - 1], low[i - 2] if i >= 2 else low[i - 1]) if bull else max(sar, high[i - 1], high[i - 2] if i >= 2 else high[i - 1]) + reverse = (bull and low[i] < sar) or (not bull and high[i] > sar) + if reverse: bull, sar, ep, af = (False, ep, low[i], .02) if bull else (True, ep, high[i], .02) + elif (bull and high[i] > ep) or (not bull and low[i] < ep): ep = high[i] if bull else low[i]; af = min(af + step, .2) + output[i], bullish[i], extreme[i], acceleration[i], reversal[i] = sar, bull, ep, af, reverse + return {"output": output, "bullish": bullish, "extreme": extreme, "acceleration": acceleration, "reversal": reversal} + + +def compare_stateful_trace(expected: Mapping[str, np.ndarray], actual: Mapping[str, np.ndarray], *, trace_type: str | None = None) -> dict[str, Any]: + if trace_type is not None and trace_type not in TRACE_KEYS: + raise ValueError("unknown stateful trace type") + keys = sorted(set(expected) | set(actual)); failures = [] + required = TRACE_KEYS.get(trace_type, frozenset(keys)) + if set(expected) != required or set(actual) != required: + failures.extend(sorted(required - set(expected) | required - set(actual))) + for key in keys: + if key not in expected or key not in actual or not np.array_equal(expected[key], actual[key], equal_nan=True): failures.append(key) + return {"exact": not failures, "required_keys": sorted(required), "mismatched_keys": sorted(set(failures))} + + +def _ulp_distances(actual: np.ndarray, expected: np.ndarray) -> np.ndarray: + """Return finite IEEE-754 float64 distances, treating signed zero as equal.""" + actual64, expected64 = np.asarray(actual, dtype=np.float64), np.asarray(expected, dtype=np.float64) + actual_bits, expected_bits = actual64.view(np.uint64), expected64.view(np.uint64) + actual_ordered = np.where(actual_bits >> 63 != 0, ~actual_bits, actual_bits | np.uint64(1 << 63)) + expected_ordered = np.where(expected_bits >> 63 != 0, ~expected_bits, expected_bits | np.uint64(1 << 63)) + distances = np.asarray(np.abs(actual_ordered.astype(object) - expected_ordered.astype(object)), dtype=np.float64) + distances[actual64 == expected64] = 0.0 + return distances + + +def _numeric_metrics(actual: np.ndarray, expected: np.ndarray) -> dict[str, float]: + finite = np.isfinite(actual) & np.isfinite(expected) + delta = np.abs(actual[finite] - expected[finite]) + finite_expected = expected[finite] + relative = delta[np.abs(finite_expected) != 0] / np.abs(finite_expected[np.abs(finite_expected) != 0]) + ulps = _ulp_distances(actual[finite], expected[finite]) + return {"max_absolute_error": float(delta.max()) if len(delta) else 0.0, "max_relative_error": float(relative.max()) if len(relative) else 0.0, "mae": float(delta.mean()) if len(delta) else 0.0, "max_ulp": float(ulps.max()) if len(ulps) else 0.0, "p50_ulp": float(np.percentile(ulps, 50)) if len(ulps) else 0.0, "p95_ulp": float(np.percentile(ulps, 95)) if len(ulps) else 0.0, "p99_ulp": float(np.percentile(ulps, 99)) if len(ulps) else 0.0, "p99_9_ulp": float(np.percentile(ulps, 99.9)) if len(ulps) else 0.0} + + +def calibrate(outputs: Mapping[str, np.ndarray], oracle: Mapping[str, np.ndarray], requests: list[Mapping[str, Any]], close: np.ndarray, corpus: Mapping[str, Any]) -> dict[str, Any]: + """Serialize observed evidence; it is intentionally not an acceptance contract.""" + records = [] + for request in requests: + key = str(request["request_id"]); actual, expected = outputs[key], oracle[key] + metrics = _numeric_metrics(actual, expected) if actual.shape == expected.shape else _numeric_metrics(np.array([]), np.array([])) + records.append({"request_id": key, "family": FAMILIES[int(request["indicator_id"])], "shape": list(expected.shape), "dtype": expected.dtype.str, "nan_mask_exact": bool(actual.shape == expected.shape and np.array_equal(np.isnan(actual), np.isnan(expected))), **metrics, "decision": _continuity_decisions(close, expected), "actual_decision": _continuity_decisions(close, actual)}) + # Decision vectors are retained as hashable evidence rather than JSON arrays. + for record in records: + record["decision_sha256"] = sha256_bytes(b"".join(array_sha256(value).encode() for value in record.pop("decision").values())) + record["actual_decision_sha256"] = sha256_bytes(b"".join(array_sha256(value).encode() for value in record.pop("actual_decision").values())) + return {"artifact": CALIBRATION_ARTIFACT, "schema_version": "1.1", "corpus": dict(corpus), "records": records} + + +def freeze_contract(calibration: Mapping[str, Any]) -> dict[str, Any]: + if calibration.get("artifact") != CALIBRATION_ARTIFACT: raise FrozenContractError("only V1_1 calibration evidence can be frozen") + fields = ("max_absolute_error", "max_relative_error", "mae", "max_ulp", "p50_ulp", "p95_ulp", "p99_ulp", "p99_9_ulp") + limits = {record["request_id"]: {key: record[key] for key in ("family", "shape", "dtype", "nan_mask_exact", *fields, "decision_sha256")} for record in calibration["records"]} + family_limits = {family: {field: max(record[field] for record in calibration["records"] if record["family"] == family) for field in fields} for family in sorted({record["family"] for record in calibration["records"]})} + return {"artifact": ARTIFACT, "schema_version": "1.1", "status": "frozen", "calibration_sha256": sha256_bytes(canonical_bytes(calibration)), "corpus": calibration["corpus"], "family_limits": family_limits, "feature_limits": limits} + + +def validate_frozen_contract(contract: Mapping[str, Any], outputs: Mapping[str, np.ndarray], oracle: Mapping[str, np.ndarray], close: np.ndarray) -> dict[str, Any]: + """Fail closed against a supplied frozen contract; no limits are calculated here.""" + if contract.get("artifact") != ARTIFACT or contract.get("schema_version") != "1.1" or contract.get("status") != "frozen": raise FrozenContractError("provided contract is not frozen GPU_FEATURE_PARITY_CONTRACT_V1_1") + limits, family_limits = contract.get("feature_limits"), contract.get("family_limits") + if not isinstance(limits, dict) or not isinstance(family_limits, dict) or set(limits) != set(oracle) or set(outputs) != set(oracle): raise FrozenContractError("contract, oracle, and GPU outputs must name exactly the same features") + records = [] + for key, expected in oracle.items(): + actual, limit = outputs[key], limits[key] + measured = _numeric_metrics(actual, expected) if actual.shape == expected.shape else _numeric_metrics(np.array([]), np.array([])) + decisions = _continuity_decisions(close, expected) + gpu_decisions = _continuity_decisions(close, actual) if actual.shape == close.shape else None + decision_hash = sha256_bytes(b"".join(array_sha256(value).encode() for value in decisions.values())) + family = family_limits.get(limit.get("family")) + numeric_violations = [name for name, value in measured.items() if not family or name not in limit or name not in family or value > float(limit[name]) or value > float(family[name])] + numeric_ok = not numeric_violations + passed = bool(actual.shape == tuple(limit["shape"]) == expected.shape and actual.dtype.str == limit["dtype"] == expected.dtype.str and np.array_equal(np.isnan(actual), np.isnan(expected)) and numeric_ok and decision_hash == limit["decision_sha256"] and gpu_decisions is not None and all(np.array_equal(decisions[name], gpu_decisions[name]) for name in decisions)) + records.append({"request_id": key, "passed": passed, "numeric_violations": numeric_violations, **measured}) + return {"artifact": VALIDATION_ARTIFACT, "schema_version": "1.1", "contract_sha256": sha256_bytes(canonical_bytes(contract)), "passed": all(item["passed"] for item in records), "records": records} diff --git a/gpu_feature_parity_contract_v1_2.json b/gpu_feature_parity_contract_v1_2.json new file mode 100644 index 0000000..a2fcaab --- /dev/null +++ b/gpu_feature_parity_contract_v1_2.json @@ -0,0 +1 @@ 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diff --git a/gpu_feature_parity_contract_v1_2.md b/gpu_feature_parity_contract_v1_2.md new file mode 100644 index 0000000..e86a8d2 --- /dev/null +++ b/gpu_feature_parity_contract_v1_2.md @@ -0,0 +1,22 @@ +# GPU Batch01 V1.2 Frozen Validation Contract + +All 97 observed Batch01 outputs are evaluated against `historical_band_channel`. + +## Evidence Classes + +- Donchian and PSAR outputs: exact. +- Bollinger and Keltner outputs: bounded by calibration-only limits. +- Supertrend final output, bands, direction, transitions, and branch predicates: exact; ATR uses supplied fixed bounds. + +## Frozen Coverage + +- `bollinger`: 36 +- `donchian`: 10 +- `keltner`: 36 +- `psar`: 3 +- `supertrend`: 12 + +## Role Status + +Unrecovered strategy roles are `UNVERIFIABLE_FROM_RECOVERED_SOURCE`; they are recorded but are not parity blockers. +Role records: 97. diff --git a/gpu_feature_parity_contract_v1_2.py b/gpu_feature_parity_contract_v1_2.py new file mode 100644 index 0000000..197cb88 --- /dev/null +++ b/gpu_feature_parity_contract_v1_2.py @@ -0,0 +1,243 @@ +"""GPU_FEATURE_PARITY_CONTRACT_V1_2 state-first validation contract.""" +from __future__ import annotations + +import hashlib +import json +from typing import Any, Mapping + +import numpy as np + +ARTIFACT = "GPU_FEATURE_PARITY_CONTRACT_V1_2" +CALIBRATION_ARTIFACT = "GPU_FEATURE_PARITY_CALIBRATION_V1_2" +VALIDATION_ARTIFACT = "GPU_FEATURE_PARITY_VALIDATION_V1_2" +ROLE_SURFACE_ARTIFACT = "GPU_FEATURE_PARITY_ROLE_SURFACE_V1_2" +REVIEW_TEMPLATE_ARTIFACT = "GPU_FEATURE_PARITY_REVIEW_TEMPLATE_V1_2" +ROLE_A_EXACT = "A_EXACT" +ROLE_B_BOUNDED = "B_BOUNDED" +ROLE_C_UNVERIFIABLE = "C_UNVERIFIABLE" +UNVERIFIABLE = "UNVERIFIABLE_FROM_RECOVERED_SOURCE" +SUPERTREND_TRACE_KEYS = frozenset(("output", "true_range", "atr", "basic_upper", "basic_lower", "upper", "lower", "direction", "initialized", "prior_close_le_upper", "prior_close_ge_lower", "active_long", "close_ge_lower", "close_le_upper", "output_uses_lower", "direction_transition")) +EXACT_TRACE_KEYS = SUPERTREND_TRACE_KEYS - {"true_range", "atr", "basic_upper", "basic_lower"} +PREDICATE_KEYS = EXACT_TRACE_KEYS - {"output", "upper", "lower", "direction", "initialized", "direction_transition"} + + +class FrozenContractError(ValueError): + pass + + +def canonical_bytes(value: object) -> bytes: + return json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False).encode() + + +def _digest(value: object) -> str: + return hashlib.sha256(canonical_bytes(value)).hexdigest() + + +def _same(expected: np.ndarray, actual: np.ndarray) -> bool: + return expected.shape == actual.shape and np.array_equal(expected, actual, equal_nan=True) + + +def compare_supertrend_trace(expected: Mapping[str, np.ndarray], actual: Mapping[str, np.ndarray]) -> dict[str, Any]: + """Check all discrete states and branch predicates exactly; ATR is excluded.""" + required = EXACT_TRACE_KEYS | {"atr"} + missing = sorted(required - set(expected) | required - set(actual)) + mismatched = sorted(key for key in EXACT_TRACE_KEYS if key in expected and key in actual and not _same(np.asarray(expected[key]), np.asarray(actual[key]))) + atr = exact_output(np.asarray(expected["atr"]), np.asarray(actual["atr"])) if "atr" in expected and "atr" in actual else None + atr_structure_exact = atr is not None and atr["shape_exact"] and atr["nan_mask_exact"] + state_exact = not missing and not mismatched + return {"state_exact": state_exact, "required_keys": sorted(required), "mismatched_exact_keys": mismatched, "missing_keys": missing, "atr": atr, "atr_structure_exact": atr_structure_exact, "branch_predicates_exact": state_exact and all(key not in mismatched for key in PREDICATE_KEYS), "transition_checks_exact": state_exact} + + +def atr_metrics(expected: np.ndarray, actual: np.ndarray) -> dict[str, float]: + finite = np.isfinite(expected) & np.isfinite(actual) + delta = np.abs(np.asarray(actual)[finite] - np.asarray(expected)[finite]) + return {"max_absolute_error": float(delta.max()) if delta.size else 0.0, "mae": float(delta.mean()) if delta.size else 0.0} + + +def output_metrics(expected: np.ndarray, actual: np.ndarray) -> dict[str, float]: + """Stable, JSON-safe continuous-output measurements.""" + finite = np.isfinite(expected) & np.isfinite(actual) + delta = np.abs(np.asarray(actual)[finite] - np.asarray(expected)[finite]) + denominator = np.abs(np.asarray(expected)[finite]) + relative = delta[denominator != 0] / denominator[denominator != 0] + return { + "max_absolute_error": float(delta.max()) if delta.size else 0.0, + "max_relative_error": float(relative.max()) if relative.size else 0.0, + "mae": float(delta.mean()) if delta.size else 0.0, + } + + +def exact_output(expected: np.ndarray, actual: np.ndarray) -> dict[str, Any]: + return { + "shape_exact": expected.shape == actual.shape, + "dtype_exact": expected.dtype == actual.dtype, + "nan_mask_exact": expected.shape == actual.shape + and np.array_equal(np.isnan(expected), np.isnan(actual)), + "values_exact": _same(expected, actual), + } + + +def family_for_indicator(indicator_id: int) -> str: + return {17: "bollinger", 18: "bollinger", 19: "supertrend", 20: "donchian", 21: "donchian", 23: "keltner", 24: "keltner", 28: "psar"}[indicator_id] + + +def cpu_supertrend_trace(close: np.ndarray, high: np.ndarray, low: np.ndarray, period: int, multiplier: float) -> dict[str, np.ndarray]: + """Historical Supertrend semantics with explicit V1.2 branch evidence.""" + from control_plane.trading_studio.indicators.historical_band_channel import _rma, _true_range + n = len(close) + true_range = _true_range(high, low, close) + atr = _rma(true_range, period) + output = np.full(n, np.nan) + upper, lower, basic_upper, basic_lower = output.copy(), output.copy(), output.copy(), output.copy() + direction = np.zeros(n, dtype=np.int8) + initialized = np.zeros(n, dtype=bool) + predicates = {name: np.zeros(n, dtype=bool) for name in PREDICATE_KEYS} + direction_transition = np.zeros(n, dtype=bool) + for index in range(period, n): + midpoint = (high[index] + low[index]) / 2.0 + basic_upper[index], basic_lower[index] = midpoint + multiplier * atr[index], midpoint - multiplier * atr[index] + if index == period: + upper[index], lower[index] = basic_upper[index], basic_lower[index] + predicates["close_ge_lower"][index] = close[index] > lower[index] + predicates["output_uses_lower"][index] = predicates["close_ge_lower"][index] + output[index] = lower[index] if predicates["output_uses_lower"][index] else upper[index] + else: + predicates["prior_close_le_upper"][index] = close[index - 1] <= upper[index - 1] + predicates["prior_close_ge_lower"][index] = close[index - 1] >= lower[index - 1] + upper[index] = min(basic_upper[index], upper[index - 1]) if predicates["prior_close_le_upper"][index] else basic_upper[index] + lower[index] = max(basic_lower[index], lower[index - 1]) if predicates["prior_close_ge_lower"][index] else basic_lower[index] + predicates["active_long"][index] = direction[index - 1] == 1 + predicates["close_ge_lower"][index] = close[index] >= lower[index] + predicates["close_le_upper"][index] = close[index] <= upper[index] + predicates["output_uses_lower"][index] = predicates["active_long"][index] and predicates["close_ge_lower"][index] or not predicates["active_long"][index] and not predicates["close_le_upper"][index] + output[index] = lower[index] if predicates["output_uses_lower"][index] else upper[index] + direction[index] = 1 if close[index] > output[index] else -1 + initialized[index] = True + if index > period: + direction_transition[index] = direction[index] != direction[index - 1] + return { + "output": output, + "true_range": true_range, + "atr": atr, + "basic_upper": basic_upper, + "basic_lower": basic_lower, + "upper": upper, + "lower": lower, + "direction": direction, + "initialized": initialized, + "direction_transition": direction_transition, + **predicates, + } + + +def calibrate_supertrend(request_id: str, expected: Mapping[str, np.ndarray], actual: Mapping[str, np.ndarray], *, atr_limits: Mapping[str, float] | None = None) -> dict[str, Any]: + trace = compare_supertrend_trace(expected, actual) + measured = atr_metrics(np.asarray(expected["atr"]), np.asarray(actual["atr"])) if trace["state_exact"] and trace["atr_structure_exact"] else None + return {"request_id": request_id, "family": "supertrend", "role": ROLE_A_EXACT, "role_classification": {"discrete_state_and_transitions": ROLE_A_EXACT, "atr": ROLE_B_BOUNDED}, "trace": trace, "atr": measured, "atr_limits": dict(atr_limits) if atr_limits is not None else measured} + + +def calibrate_output(request_id: str, indicator_id: int, expected: np.ndarray, actual: np.ndarray) -> dict[str, Any]: + """Calibrate a non-Supertrend Batch01 output under its family evidence class.""" + family = family_for_indicator(indicator_id) + exact = family in {"donchian", "psar"} + result = exact_output(expected, actual) + return { + "request_id": request_id, + "indicator_id": indicator_id, + "family": family, + "role": ROLE_A_EXACT if exact else ROLE_B_BOUNDED, + "output": result, + # Bounds are deliberately emitted only for continuous families. + "limits": None if exact else output_metrics(expected, actual), + } + + +def freeze_contract(calibration: Mapping[str, Any]) -> dict[str, Any]: + if calibration.get("artifact") != CALIBRATION_ARTIFACT: + raise FrozenContractError("only V1.2 calibration evidence can be frozen") + limits = {} + for record in calibration.get("records", []): + if record.get("family") == "supertrend": + if not record["trace"]["state_exact"] or not record["trace"]["atr_structure_exact"] or record["atr"] is None: + raise FrozenContractError("cannot freeze a state-inexact calibration") + limits[record["request_id"]] = {"family": "supertrend", "role": ROLE_A_EXACT, "atr_limits": dict(record["atr_limits"])} + elif record.get("family") in {"donchian", "psar"}: + if not all(record["output"].values()): + raise FrozenContractError("cannot freeze an inexact discrete/state calibration") + limits[record["request_id"]] = {"family": record["family"], "role": ROLE_A_EXACT} + elif record.get("family") in {"bollinger", "keltner"}: + if not record["output"]["shape_exact"] or not record["output"]["nan_mask_exact"]: + raise FrozenContractError("cannot freeze a structurally inexact continuous calibration") + limits[record["request_id"]] = {"family": record["family"], "role": ROLE_B_BOUNDED, "output_limits": dict(record["limits"])} + else: + raise FrozenContractError("unknown Batch01 family in calibration") + return {"artifact": ARTIFACT, "schema_version": "1.2", "status": "frozen", "role_classification": {"discrete_state_and_transitions": ROLE_A_EXACT, "atr": ROLE_B_BOUNDED, "unrecovered_batch01_roles": ROLE_C_UNVERIFIABLE}, "calibration_sha256": _digest(calibration), "feature_limits": limits} + + +def validate_all_frozen_contract(contract: Mapping[str, Any], expected_outputs: Mapping[str, np.ndarray], actual_outputs: Mapping[str, np.ndarray], traces: Mapping[str, tuple[Mapping[str, np.ndarray], Mapping[str, np.ndarray]]]) -> dict[str, Any]: + """Validate every frozen Batch01 feature without deriving new limits.""" + if contract.get("artifact") != ARTIFACT or contract.get("status") != "frozen": + raise FrozenContractError("provided contract is not frozen GPU_FEATURE_PARITY_CONTRACT_V1_2") + limits = contract.get("feature_limits", {}) + if set(limits) != set(expected_outputs) or set(limits) != set(actual_outputs): + raise FrozenContractError("contract and outputs must name exactly the same features") + records = [] + for request_id, limit in limits.items(): + family = limit["family"] + expected, actual = np.asarray(expected_outputs[request_id]), np.asarray(actual_outputs[request_id]) + if family == "supertrend": + if request_id not in traces: + raise FrozenContractError("Supertrend trace missing") + trace = compare_supertrend_trace(*traces[request_id]) + atr = atr_metrics(np.asarray(traces[request_id][0]["atr"]), np.asarray(traces[request_id][1]["atr"])) if trace["state_exact"] and trace["atr_structure_exact"] else None + bounded = atr is not None and all(atr[key] <= float(limit["atr_limits"][key]) for key in atr) + passed = trace["state_exact"] and trace["atr_structure_exact"] and bounded + records.append({"request_id": request_id, "family": family, "role": ROLE_A_EXACT, "trace": trace, "atr": atr, "atr_bounded": bounded, "passed": passed}) + elif family in {"donchian", "psar"}: + output = exact_output(expected, actual) + records.append({"request_id": request_id, "family": family, "role": ROLE_A_EXACT, "output": output, "passed": all(output.values())}) + else: + output = exact_output(expected, actual) + metrics = output_metrics(expected, actual) + bounded = all(metrics[key] <= float(limit["output_limits"][key]) for key in metrics) + passed = output["shape_exact"] and output["nan_mask_exact"] and bounded + records.append({"request_id": request_id, "family": family, "role": ROLE_B_BOUNDED, "output": output, "metrics": metrics, "bounded": bounded, "passed": passed}) + return {"artifact": VALIDATION_ARTIFACT, "schema_version": "1.2", "contract_sha256": _digest(contract), "passed": all(record["passed"] for record in records), "records": records} + + +def validate_frozen_contract(contract: Mapping[str, Any], traces: Mapping[str, tuple[Mapping[str, np.ndarray], Mapping[str, np.ndarray]]]) -> dict[str, Any]: + """Mechanical validation only: it never derives or expands frozen limits.""" + if contract.get("artifact") != ARTIFACT or contract.get("schema_version") != "1.2" or contract.get("status") != "frozen": + raise FrozenContractError("provided contract is not frozen GPU_FEATURE_PARITY_CONTRACT_V1_2") + limits = contract.get("feature_limits") + if not isinstance(limits, dict) or set(limits) != set(traces): + raise FrozenContractError("contract and traces must name exactly the same features") + records = [] + for request_id, (expected, actual) in traces.items(): + trace = compare_supertrend_trace(expected, actual) + metric = atr_metrics(np.asarray(expected["atr"]), np.asarray(actual["atr"])) if trace["state_exact"] and trace["atr_structure_exact"] else None + bounded = metric is not None and all(metric[name] <= float(limits[request_id]["atr_limits"][name]) for name in metric) + records.append({"request_id": request_id, "role": ROLE_A_EXACT, "role_classification": {"discrete_state_and_transitions": ROLE_A_EXACT, "atr": ROLE_B_BOUNDED}, "trace": trace, "atr": metric, "atr_bounded": bounded, "passed": trace["state_exact"] and trace["atr_structure_exact"] and bounded}) + return {"artifact": VALIDATION_ARTIFACT, "schema_version": "1.2", "contract_sha256": _digest(contract), "passed": all(record["passed"] for record in records), "records": records} + + +def role_surface(requests: list[Mapping[str, Any]]) -> dict[str, Any]: + """Classify only recovered Cohort001 role assignments; unknown Batch01 roles do not block parity.""" + try: + from control_plane.trading_studio.indicators.registry import _HS22_REQUIRED_TRIPLES + except ModuleNotFoundError: + recovered = {} + unavailable_reason = "control_plane.trading_studio.indicators.registry is unavailable" + else: + recovered = {(indicator, period, p1): slot for indicator, period, p1, slot in _HS22_REQUIRED_TRIPLES} + unavailable_reason = None + records = [] + for item in requests: + key = (int(item["indicator_id"]), int(item["period"]), float(item["p1"])) + slot = recovered.get(key) + records.append({"request_id": str(item["request_id"]), "indicator_id": key[0], "period": key[1], "p1": key[2], "cohort001_role": slot, "classification": ROLE_A_EXACT if key[0] == 19 and slot else ROLE_C_UNVERIFIABLE, "role_status": "RECOVERED_COHORT001" if slot else UNVERIFIABLE, "reason": unavailable_reason if not slot else None, "parity_blocker": False if not slot else True}) + return {"artifact": ROLE_SURFACE_ARTIFACT, "schema_version": "1.2", "source": "Cohort001 recovered role surface", "records": records} + + +def review_template() -> dict[str, Any]: + return {"artifact": REVIEW_TEMPLATE_ARTIFACT, "schema_version": "1.2", "status": "review_required_not_validated", "required_review": ["calibration evidence is separate from holdout and adversarial evidence", "exact state, transition, and branch-predicate checks pass", "ATR bounded checks were evaluated only after exact state validation", "C-unverifiable roles are recorded and excluded as parity blockers"], "decision": None} diff --git a/gpu_feature_parity_role_surface_v1_2.json b/gpu_feature_parity_role_surface_v1_2.json new file mode 100644 index 0000000..3ebca01 --- /dev/null +++ b/gpu_feature_parity_role_surface_v1_2.json @@ -0,0 +1 @@ +{"artifact":"GPU_FEATURE_PARITY_ROLE_SURFACE_V1_2","records":[{"classification":"C_UNVERIFIABLE","cohort001_role":null,"indicator_id":17,"p1":1.5,"parity_blocker":false,"period":14,"reason":"control_plane.trading_studio.indicators.registry is 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diff --git a/gpu_supertrend_v1_2_diagnostic.py b/gpu_supertrend_v1_2_diagnostic.py new file mode 100644 index 0000000..8346717 --- /dev/null +++ b/gpu_supertrend_v1_2_diagnostic.py @@ -0,0 +1,144 @@ +"""V1.2-only semantic diagnostic for CPU and GPU Supertrend traces.""" +from __future__ import annotations + +import argparse +import csv +import json +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +from gpu_feature_engine_v1_2 import supertrend_trace +from gpu_feature_parity_contract_v1_1 import deterministic_adversarial_ohlcv +from gpu_feature_parity_contract_v1_2 import PREDICATE_KEYS, SUPERTREND_TRACE_KEYS, cpu_supertrend_trace + +ARTIFACT = "GPU_SUPERTREND_ATR_SEMANTIC_DIAGNOSTIC_V1_2" +PERIOD = 10 +MULTIPLIERS = (2.0, 3.0, 4.0) +NUMERIC_FIELDS = ("output", "true_range", "atr", "basic_upper", "basic_lower", "upper", "lower") +EXACT_FIELDS = tuple(sorted((*PREDICATE_KEYS, "direction", "direction_transition"))) + + +def _read_ohlcv(path: Path) -> dict[str, np.ndarray]: + with path.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + names = ("close", "high", "low") + if not rows or not set(names).issubset(rows[0]): + raise ValueError("CSV must contain non-empty close, high, and low columns") + values = {name: np.asarray([float(row[name]) for row in rows], dtype=np.float64) for name in names} + if not all(np.isfinite(value).all() for value in values.values()): + raise ValueError("diagnostic requires finite close, high, and low inputs") + return values + + +def _json_number(value: Any) -> float | int | bool | None: + value = value.item() if isinstance(value, np.generic) else value + if isinstance(value, (bool, np.bool_)): + return bool(value) + if isinstance(value, (int, np.integer)): + return int(value) + value = float(value) + return value if np.isfinite(value) else None + + +def _first_difference(expected: np.ndarray, actual: np.ndarray) -> dict[str, Any] | None: + equal = np.equal(expected, actual) | (np.isnan(expected) & np.isnan(actual)) + indices = np.flatnonzero(~equal) + if not len(indices): + return None + index = int(indices[0]) + return {"bar": index, "cpu": _json_number(expected[index]), "gpu": _json_number(actual[index])} + + +def _ulp(expected: np.ndarray, actual: np.ndarray) -> np.ndarray: + expected_bits, actual_bits = expected.view(np.uint64), actual.view(np.uint64) + sign = np.uint64(1 << 63) + expected_ordered = np.where(expected_bits >> 63 != 0, ~expected_bits, expected_bits | sign) + actual_ordered = np.where(actual_bits >> 63 != 0, ~actual_bits, actual_bits | sign) + return np.asarray(np.abs(expected_ordered.astype(object) - actual_ordered.astype(object)), dtype=np.float64) + + +def _numeric_stats(expected: np.ndarray, actual: np.ndarray) -> dict[str, Any]: + finite = np.isfinite(expected) & np.isfinite(actual) + delta = np.abs(actual[finite] - expected[finite]) + nonzero_expected = np.abs(expected[finite]) != 0 + relative = delta[nonzero_expected] / np.abs(expected[finite][nonzero_expected]) + zero_reference_divergence = bool(np.any((expected[finite] == 0) & (actual[finite] != 0))) + return { + "first_divergence": _first_difference(expected, actual), + "finite_pairs": int(finite.sum()), + "nonfinite_mismatches": int(np.count_nonzero(~(np.equal(expected, actual) | (np.isnan(expected) & np.isnan(actual))) & ~finite)), + "max_abs": float(delta.max()) if delta.size else 0.0, + "max_rel": float(relative.max()) if relative.size else 0.0, + "max_rel_unbounded_at_zero_cpu": zero_reference_divergence, + "max_ulp": float(_ulp(expected[finite], actual[finite]).max()) if delta.size else 0.0, + } + + +def _exact_stats(expected: np.ndarray, actual: np.ndarray) -> dict[str, Any]: + return {"exact": _first_difference(expected, actual) is None, "first_divergence": _first_difference(expected, actual)} + + +def _diagnose_one(ohlcv: Mapping[str, np.ndarray], multiplier: float, device: torch.device) -> dict[str, Any]: + tensors = {name: torch.as_tensor(ohlcv[name], dtype=torch.float64, device=device) for name in ("close", "high", "low")} + cpu = cpu_supertrend_trace(ohlcv["close"], ohlcv["high"], ohlcv["low"], PERIOD, multiplier) + gpu_trace = supertrend_trace(tensors["close"], tensors["high"], tensors["low"], PERIOD, multiplier) + if device.type == "cuda": + torch.cuda.synchronize(device) + gpu = {name: value.detach().cpu().numpy() for name, value in gpu_trace.items()} + missing_cpu = sorted(SUPERTREND_TRACE_KEYS - cpu.keys()) + missing_gpu = sorted(SUPERTREND_TRACE_KEYS - gpu.keys()) + if missing_cpu or missing_gpu: + raise RuntimeError(f"incomplete V1.2 Supertrend trace: CPU missing {missing_cpu}; GPU missing {missing_gpu}") + return { + "period": PERIOD, + "multiplier": multiplier, + "numeric": {name: _numeric_stats(cpu[name], gpu[name]) for name in NUMERIC_FIELDS}, + "branch_predicates": {name: _exact_stats(cpu[name], gpu[name]) for name in sorted(PREDICATE_KEYS)}, + "direction": _exact_stats(cpu["direction"], gpu["direction"]), + "transitions": {"direction_transition": _exact_stats(cpu["direction_transition"], gpu["direction_transition"])}, + } + + +def diagnose(ohlcv: Mapping[str, np.ndarray], periods: list[int] | None = None, multiplier: float | None = None, device: torch.device | None = None) -> dict[str, Any]: + """Diagnose one corpus; V1.2 semantics are fixed to period 10 and 2/3/4 multipliers.""" + if periods not in (None, [PERIOD]) or multiplier not in (None, *MULTIPLIERS): + raise ValueError("V1.2 semantic diagnostic supports only period 10 and multipliers 2, 3, and 4") + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + return {"artifact": ARTIFACT, "schema_version": "1.2", "device": str(device), "bars": len(ohlcv["close"]), "period": PERIOD, "records": [_diagnose_one(ohlcv, value, device) for value in MULTIPLIERS]} + + +def run(calibration_csv: Path, holdout_csv: Path, *, adversarial_length: int = 256, seed: int = 0, device: torch.device | None = None) -> dict[str, Any]: + """Produce calibration, holdout, and deterministic adversarial diagnostic corpora.""" + if adversarial_length < PERIOD + 1: + raise ValueError("adversarial length must be at least 11") + if device is None: + if not torch.cuda.is_available(): + raise RuntimeError("CUDA GPU is required; pass device=torch.device('cpu') only for tests") + device = torch.device("cuda") + return {"artifact": ARTIFACT, "schema_version": "1.2", "device": str(device), "adversarial": {"generator": "deterministic_adversarial_ohlcv", "length": adversarial_length, "seed": seed}, "corpora": {"calibration": diagnose(_read_ohlcv(calibration_csv), device=device), "holdout": diagnose(_read_ohlcv(holdout_csv), device=device), "adversarial": diagnose(deterministic_adversarial_ohlcv(length=adversarial_length, seed=seed), device=device)}} + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--calibration-csv", type=Path, required=True) + parser.add_argument("--holdout-csv", type=Path, required=True) + parser.add_argument("--adversarial-length", type=int, default=256) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--output", type=Path) + args = parser.parse_args() + payload = run(args.calibration_csv, args.holdout_csv, adversarial_length=args.adversarial_length, seed=args.seed) + encoded = json.dumps(payload, sort_keys=True, allow_nan=False) + "\n" + if args.output: + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(encoded, encoding="utf-8") + else: + print(encoded, end="") + + +if __name__ == "__main__": + main() diff --git a/hyperscalper_candidate_taxonomy_v1.json b/hyperscalper_candidate_taxonomy_v1.json new file mode 100644 index 0000000..0420897 --- /dev/null +++ b/hyperscalper_candidate_taxonomy_v1.json @@ -0,0 +1,32 @@ +{ + "schema_version": 1, + "artifact": "HYPERSCALPER_CANDIDATE_TAXONOMY_V1", + "purpose": "Static, non-executable candidate taxonomy for feature-gap triage. Entries are hypotheses, not strategies or approvals.", + "evidence_labels": ["source_mapped", "historical_usage", "lineage_bias", "qualification_failure", "external_candidate", "requires_oracle", "requires_deq"], + "candidate_registry": [ + {"candidate_id": "spot_perp_basis", "market": "crypto", "venue_scope": "spot_perpetual", "family": "cross_market_basis", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "perp_funding_pressure", "market": "crypto", "venue_scope": "perpetual", "family": "derivatives_positioning", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "options_implied_realized_vol", "market": "crypto", "venue_scope": "options_spot", "family": "cross_market_volatility", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "exchange_orderbook_imbalance", "market": "crypto", "venue_scope": "single_exchange", "family": "microstructure", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "cross_exchange_dislocation", "market": "crypto", "venue_scope": "multi_exchange", "family": "cross_venue_execution", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "stablecoin_flow_stress", "market": "crypto", "venue_scope": "onchain_cex", "family": "liquidity_flow", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "equity_index_futures_cash_basis", "market": "equity", "venue_scope": "cash_futures", "family": "cross_market_basis", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "etf_constituent_dispersion", "market": "equity", "venue_scope": "etf_cash", "family": "cross_asset_dispersion", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "adr_local_listing_spread", "market": "equity", "venue_scope": "cross_listing", "family": "cross_venue_basis", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "index_option_skew", "market": "equity", "venue_scope": "options_cash", "family": "implied_distribution", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "treasury_curve_slope", "market": "rates", "venue_scope": "cash_futures", "family": "curve_relative_value", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "swap_spread", "market": "rates", "venue_scope": "otc_futures", "family": "cross_market_basis", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "fx_spot_forward_carry", "market": "fx", "venue_scope": "spot_forwards", "family": "carry_basis", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "fx_cross_pair_relative_strength", "market": "fx", "venue_scope": "cross_pair", "family": "cross_asset_momentum", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "commodity_calendar_spread", "market": "commodity", "venue_scope": "futures_curve", "family": "term_structure", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "commodity_crack_or_crush_spread", "market": "commodity", "venue_scope": "intercommodity", "family": "processing_margin", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "volatility_term_structure", "market": "volatility", "venue_scope": "spot_futures_options", "family": "term_structure", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]}, + {"candidate_id": "cross_asset_risk_regime", "market": "multi_asset", "venue_scope": "equity_rates_fx_commodity_crypto", "family": "regime", "evidence": ["external_candidate", "requires_oracle", "requires_deq"]} + ], + "expansion_waves": [ + {"wave": 0, "name": "evidence_reconciliation", "admission_evidence": ["source_mapped", "historical_usage", "lineage_bias"], "not_a_strategy": true}, + {"wave": 1, "name": "oracle_redundancy_gaps", "admission_evidence": ["requires_oracle", "qualification_failure"], "not_a_strategy": true}, + {"wave": 2, "name": "cross_market_data_contracts", "admission_evidence": ["external_candidate", "requires_oracle"], "not_a_strategy": true}, + {"wave": 3, "name": "decision_equivalence_gate", "admission_evidence": ["requires_deq"], "not_a_strategy": true} + ] +} diff --git a/hyperscalper_feature_gap_analysis_v1.json b/hyperscalper_feature_gap_analysis_v1.json new file mode 100644 index 0000000..5c647cb --- /dev/null +++ b/hyperscalper_feature_gap_analysis_v1.json @@ -0,0 +1,86862 @@ +{ + "artifact": "HYPERSCALPER_FEATURE_GAP_ANALYSIS_V1", + "blockers": [], + "candidate_taxonomy": { + "artifact": "HYPERSCALPER_CANDIDATE_TAXONOMY_V1", + "candidate_registry": [ + { + "candidate_id": "spot_perp_basis", + "evidence": [ + "external_candidate", + 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HYPERSCALPER Feature Gap Analysis V1 + +Read-only: yes. Strategy execution: not performed. + +- Information map: 711/711 features +- Acceptance: not_proven +- Redundancy clusters: 39 +- DEQ: AVAILABLE (supplied Cohort001 ledger; multi-feature attribution is associative, not causal) +- Blockers: 0 + +## Blockers diff --git a/hyperscalper_feature_gap_analysis_v1.py b/hyperscalper_feature_gap_analysis_v1.py new file mode 100644 index 0000000..94b9fc7 --- /dev/null +++ b/hyperscalper_feature_gap_analysis_v1.py @@ -0,0 +1,1096 @@ +#!/usr/bin/env python3 +"""Read-only HYPERSCALPER_FEATURE_GAP_ANALYSIS_V1 evidence runner. + +This program never imports a strategy runtime, invokes an optimizer, or writes to +an input artifact. It only summarizes frozen oracle and historical evidence. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import sqlite3 +from collections import Counter +from pathlib import Path +from typing import Any + +import numpy as np + +ARTIFACT = "HYPERSCALPER_FEATURE_GAP_ANALYSIS_V1" +EXPECTED_FEATURES = 711 +MAX_SAMPLE_ROWS = 16_384 +LINEAGE_ROLES = ("trend", "signal", "trigger", "confirm", "vol") +DEQ_RETURN_FIELDS = tuple(f"return_{bars}_bars_bps" for bars in (1, 2, 3, 5, 10)) +DEQ_MEAN_FIELDS = ("mfe_bps", "mae_bps", "time_to_positive_bars") +DEQ_PRIMITIVE_FIELDS = DEQ_RETURN_FIELDS + ( + "mfe_bps", + "mae_bps", + "time_to_positive_bars", + "time_to_25_bps_bars", + "time_to_50_bps_bars", + "max_adverse_before_positive_bps", + "winner_negative_first", + "recovery_bars", +) + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def source_hash(path: Path) -> str: + if path.is_file(): + return sha256(path) + digest = hashlib.sha256() + for item in sorted(path.glob("*.npy")): + digest.update(item.name.encode("utf-8")) + digest.update(b"\0") + digest.update(bytes.fromhex(sha256(item))) + return digest.hexdigest() + + +def load_json(path: Path) -> dict[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError(f"{path} must contain a JSON object") + return value + + +def feature_rows(path: Path) -> list[dict[str, Any]]: + payload = load_json(path) + rows = payload.get("primitives") or payload.get("requests") + if not isinstance(rows, list): + raise ValueError("engineering map/request must contain primitives or requests") + result = [] + seen = set() + for row in rows: + key = (int(row["indicator_id"]), int(row["period"]), float(row["p1"])) + if key in seen: + raise ValueError(f"duplicate feature primitive: {key}") + seen.add(key) + result.append( + { + "feature_key": f"{key[0]}:{key[1]}:{key[2]:g}", + "indicator_id": key[0], + "period": key[1], + "p1": key[2], + "request_id": row.get("request_id"), + } + ) + if len(result) != EXPECTED_FEATURES: + raise ValueError( + f"source map must contain exactly {EXPECTED_FEATURES} unique primitives; " + f"found {len(result)}" + ) + return result + + +def accepted_ids(manifest: dict[str, Any]) -> set[str]: + coverage = manifest.get("coverage", {}) + values = coverage.get("feature_versions", manifest.get("accepted_request_ids", [])) + return {str(value) for value in values} if isinstance(values, list) else set() + + +def checkpoint_columns( + checkpoint: Path, rows: list[dict[str, Any]] +) -> tuple[np.ndarray, list[str]]: + with np.load(checkpoint, allow_pickle=False) as archive: + names = list(archive.files) + by_request = {str(row["request_id"]): row for row in rows if row["request_id"]} + if set(names) == set(by_request): + ordered_names = [str(row["request_id"]) for row in rows] + elif len(names) == EXPECTED_FEATURES: + # A frozen raw NPZ may lack request IDs; source-map order is the only asserted mapping. + ordered_names = sorted(names) + else: + raise ValueError( + "checkpoint keys do not match request IDs and do not contain 711 arrays" + ) + arrays = [np.asarray(archive[name], dtype=np.float64).reshape(-1) for name in ordered_names] + lengths = {array.size for array in arrays} + if len(lengths) != 1 or not next(iter(lengths)): + raise ValueError("checkpoint arrays must be non-empty and have equal length") + return np.column_stack(arrays), ordered_names + + +def checkpoint_directory_columns( + checkpoint_dir: Path, rows: list[dict[str, Any]] +) -> tuple[np.ndarray, list[str]]: + files = sorted(checkpoint_dir.glob("*.npy")) + if len(files) != EXPECTED_FEATURES: + raise ValueError( + f"checkpoint directory must contain exactly {EXPECTED_FEATURES} .npy files; " + f"found {len(files)}" + ) + by_primitive = { + (row["indicator_id"], row["period"], row["p1"]): row for row in rows + } + mapped: dict[tuple[int, int, float], Path] = {} + for path in files: + parts = path.stem.split("_") + if len(parts) != 4 or parts[0] != "hs22": + raise ValueError(f"invalid checkpoint filename: {path.name}") + try: + primitive = (int(parts[1]), int(parts[2]), float(parts[3])) + except ValueError as error: + raise ValueError(f"invalid checkpoint filename: {path.name}") from error + if primitive not in by_primitive: + raise ValueError(f"checkpoint filename is not an engineering primitive: {path.name}") + if primitive in mapped: + raise ValueError(f"duplicate checkpoint primitive: {primitive}") + mapped[primitive] = path + if set(mapped) != set(by_primitive): + raise ValueError("checkpoint filenames do not cover every engineering primitive") + ordered_paths = [ + mapped[(row["indicator_id"], row["period"], row["p1"])] for row in rows + ] + arrays = [ + np.asarray(np.load(path, allow_pickle=False), dtype=np.float64).reshape(-1) + for path in ordered_paths + ] + lengths = {array.size for array in arrays} + if len(lengths) != 1 or not next(iter(lengths)): + raise ValueError("checkpoint arrays must be non-empty and have equal length") + return np.column_stack(arrays), [path.name for path in ordered_paths] + + +def sampled_checkpoint_columns( + checkpoint: Path | None, + checkpoint_dir: Path | None, + rows: list[dict[str, Any]], + sample_rows: int, +) -> tuple[np.ndarray, list[str], int]: + """Read only selected observations from each checkpoint column. + + This intentionally avoids materializing the 129k by 711 source matrix. NPZ + members are decompressed one at a time; directory checkpoints are mmap'd. + """ + if checkpoint_dir: + files = sorted(checkpoint_dir.glob("*.npy")) + if len(files) != EXPECTED_FEATURES: + raise ValueError("checkpoint directory must contain exactly 711 .npy files") + by_primitive = { + (row["indicator_id"], row["period"], row["p1"]): row for row in rows + } + mapped: dict[tuple[int, int, float], Path] = {} + for path in files: + parts = path.stem.split("_") + if len(parts) != 4 or parts[0] != "hs22": + raise ValueError(f"invalid checkpoint filename: {path.name}") + primitive = (int(parts[1]), int(parts[2]), float(parts[3])) + if primitive not in by_primitive or primitive in mapped: + raise ValueError(f"invalid or duplicate checkpoint primitive: {path.name}") + mapped[primitive] = path + if set(mapped) != set(by_primitive): + raise ValueError("checkpoint filenames do not cover every engineering primitive") + ordered = [mapped[(row["indicator_id"], row["period"], row["p1"])] for row in rows] + first = np.load(ordered[0], allow_pickle=False, mmap_mode="r").reshape(-1) + size = first.size + indices = np.linspace(0, size - 1, min(sample_rows, size), dtype=int) + arrays = [] + for path in ordered: + values = np.load(path, allow_pickle=False, mmap_mode="r").reshape(-1) + if values.size != size: + raise ValueError("checkpoint arrays must be non-empty and have equal length") + arrays.append(np.asarray(values[indices], dtype=np.float64)) + return np.column_stack(arrays), [path.name for path in ordered], size + + assert checkpoint is not None + with np.load(checkpoint, allow_pickle=False) as archive: + names = list(archive.files) + by_request = {str(row["request_id"]): row for row in rows if row["request_id"]} + if set(names) == set(by_request): + ordered_names = [str(row["request_id"]) for row in rows] + elif len(names) == EXPECTED_FEATURES: + ordered_names = sorted(names) + else: + raise ValueError("checkpoint keys do not match request IDs and do not contain 711 arrays") + first = np.asarray(archive[ordered_names[0]]).reshape(-1) + size = first.size + if not size: + raise ValueError("checkpoint arrays must be non-empty and have equal length") + indices = np.linspace(0, size - 1, min(sample_rows, size), dtype=int) + arrays = [] + for name in ordered_names: + values = np.asarray(archive[name], dtype=np.float64).reshape(-1) + if values.size != size: + raise ValueError("checkpoint arrays must be non-empty and have equal length") + arrays.append(values[indices]) + return np.column_stack(arrays), ordered_names, size + + +def semantic_types(path: Path | None, rows: list[dict[str, Any]]) -> tuple[list[dict[str, str]], str | None]: + """Resolve output type and domain from a metadata-only semantic map.""" + defaults = [{"output_type": "continuous", "domain": "unclassified"} for _ in rows] + if path is None: + return defaults, "no semantic map supplied; output types default to continuous" + try: + if path.suffix.lower() == ".parquet": + import pyarrow.parquet as pq + + items = pq.read_table(path).to_pylist() + else: + payload = load_json(path) + items = payload.get("primitives", payload.get("features", payload.get("rows", payload.get("semantic_map")))) + if isinstance(items, dict): + items = items.get("primitives", items.get("features", items.get("rows"))) + except (ImportError, OSError, ValueError) as error: + return defaults, f"cannot read semantic map: {error}" + if not isinstance(items, list): + return defaults, "semantic map has no primitives/features/rows array" + mapped: dict[str, dict[str, str]] = {} + for item in items: + if not isinstance(item, dict): + continue + key = item.get("feature_key") + if key is None and all(name in item for name in ("indicator_id", "period", "p1")): + key = f"{int(item['indicator_id'])}:{int(item['period'])}:{float(item['p1']):g}" + if key is None: + continue + raw_type = str(item.get("output_type", item.get("semantic_type", item.get("value_type", "continuous")))).lower() + output_type = "event" if raw_type in {"event", "detection", "binary_event"} else "state" if raw_type in {"state", "categorical", "boolean"} else "continuous" + domain = str(item.get("domain", item.get("semantic_domain", item.get("engineering_family", item.get("family", "unclassified"))))) + mapped[str(key)] = {"output_type": output_type, "domain": domain} + result = [mapped.get(row["feature_key"], defaults[index]) for index, row in enumerate(rows)] + missing = sum(row["feature_key"] not in mapped for row in rows) + return result, f"semantic map does not classify {missing} features" if missing else None + + +def rank_finite_columns(matrix: np.ndarray) -> np.ndarray: + """Return deterministic ordinal ranks, leaving non-finite observations masked.""" + ranks = np.full(matrix.shape, np.nan, dtype=np.float64) + for column in range(matrix.shape[1]): + finite = np.isfinite(matrix[:, column]) + count = int(finite.sum()) + if count: + order = np.argsort(matrix[finite, column], kind="mergesort") + column_ranks = np.empty(count, dtype=np.float64) + column_ranks[order] = np.arange(count) + ranks[finite, column] = column_ranks + return ranks + + +def pairwise_correlations( + values: np.ndarray, finite: np.ndarray, pair_samples: np.ndarray, min_pair_samples: int +) -> np.ndarray: + """Correlate columns using only rows finite for each pair, without pair materialization.""" + masked_values = np.where(finite, values, 0.0) + finite_float = finite.astype(np.float64) + counts = pair_samples.astype(np.float64) + sums = masked_values.T @ finite_float + squared_sums = (masked_values * masked_values).T @ finite_float + covariance = masked_values.T @ masked_values + with np.errstate(invalid="ignore", divide="ignore"): + covariance -= sums * sums.T / counts + variance = squared_sums - sums * sums / counts + correlation = covariance / np.sqrt(variance * variance.T) + invalid = (pair_samples < min_pair_samples) | (variance <= 0.0) | (variance.T <= 0.0) + correlation[invalid] = np.nan + np.fill_diagonal(correlation, np.nan) + return correlation + + +def pairwise_redundancy( + sample: np.ndarray, min_pair_samples: int +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Calculate pairwise-finite Pearson and pre-ranked ordinal correlations.""" + finite = np.isfinite(sample) + pair_samples = finite.astype(np.int32).T @ finite.astype(np.int32) + pearson = pairwise_correlations(sample, finite, pair_samples, min_pair_samples) + # Ranks are computed once per feature, rather than once per feature pair. + ranks = rank_finite_columns(sample) + spearman = pairwise_correlations(ranks, finite, pair_samples, min_pair_samples) + return pearson, spearman, pair_samples + + +def type_aware_redundancy( + sample: np.ndarray, output_types: list[str], min_pair_samples: int +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Use correlations for continuous values and overlap metrics for detections.""" + feature_count = sample.shape[1] + finite = np.isfinite(sample) + pair_samples = finite.astype(np.int32).T @ finite.astype(np.int32) + pearson = np.full((feature_count, feature_count), np.nan) + spearman = np.full((feature_count, feature_count), np.nan) + agreement = np.full((feature_count, feature_count), np.nan) + jaccard = np.full((feature_count, feature_count), np.nan) + continuous = np.array([kind == "continuous" for kind in output_types]) + if continuous.any(): + indices = np.flatnonzero(continuous) + continuous_values = sample[:, indices] + continuous_finite = finite[:, indices] + continuous_counts = pair_samples[np.ix_(indices, indices)] + pearson[np.ix_(indices, indices)] = pairwise_correlations( + continuous_values, continuous_finite, continuous_counts, min_pair_samples + ) + ranks = rank_finite_columns(continuous_values) + spearman[np.ix_(indices, indices)] = pairwise_correlations( + ranks, continuous_finite, continuous_counts, min_pair_samples + ) + + discrete = ~continuous + if discrete.any(): + indices = np.flatnonzero(discrete) + values = sample[:, indices] + valid = finite[:, indices] + detected = valid & (values != 0) + intersection = detected.astype(np.int32).T @ detected.astype(np.int32) + detected_counts = detected.sum(axis=0) + union = detected_counts[:, None] + detected_counts[None, :] - intersection + discrete_counts = pair_samples[np.ix_(indices, indices)] + with np.errstate(invalid="ignore", divide="ignore"): + discrete_jaccard = intersection / union + discrete_jaccard[(discrete_counts < min_pair_samples) | (union == 0)] = np.nan + np.fill_diagonal(discrete_jaccard, np.nan) + jaccard[np.ix_(indices, indices)] = discrete_jaccard + + binary = np.all(~valid | ((values == 0) | (values == 1)), axis=0) + zero = valid & (values == 0) + equal_binary = ( + zero.astype(np.int32).T @ zero.astype(np.int32) + intersection + ) + with np.errstate(invalid="ignore", divide="ignore"): + binary_agreement = equal_binary / discrete_counts + binary_pairs = binary[:, None] & binary[None, :] + binary_agreement[(discrete_counts < min_pair_samples) | ~binary_pairs] = np.nan + np.fill_diagonal(binary_agreement, np.nan) + agreement[np.ix_(indices, indices)] = binary_agreement + + # Categorical state outputs are uncommon; retain exact equality semantics for them. + for left_offset, left in enumerate(indices): + if binary[left_offset]: + continue + for right_offset, right in enumerate(indices): + if ( + right == left + or (not binary[right_offset] and right < left) + or pair_samples[left, right] < min_pair_samples + ): + continue + mask = finite[:, left] & finite[:, right] + value = float(np.mean(sample[mask, left] == sample[mask, right])) + agreement[left, right] = agreement[right, left] = value + return pearson, spearman, agreement, jaccard, pair_samples + + +def primary_redundancy_matrix( + pearson: np.ndarray, agreement: np.ndarray, jaccard: np.ndarray, output_types: list[str] +) -> np.ndarray: + result = np.full_like(pearson, np.nan) + for left in range(len(output_types)): + for right in range(len(output_types)): + if output_types[left] == output_types[right] == "continuous": + result[left, right] = abs(pearson[left, right]) + elif output_types[left] == output_types[right] == "event": + result[left, right] = jaccard[left, right] + elif output_types[left] == output_types[right] == "state": + result[left, right] = agreement[left, right] + return result + + +def clusters(correlation: np.ndarray, threshold: float = 0.95) -> list[list[int]]: + parent = list(range(correlation.shape[0])) + + def find(item: int) -> int: + while parent[item] != item: + parent[item] = parent[parent[item]] + item = parent[item] + return item + + def join(left: int, right: int) -> None: + left, right = find(left), find(right) + if left != right: + parent[right] = left + + for left in range(correlation.shape[0]): + for right in range(left): + if abs(correlation[left, right]) >= threshold: + join(left, right) + grouped: dict[int, list[int]] = {} + for index in range(len(parent)): + grouped.setdefault(find(index), []).append(index) + return [members for members in grouped.values() if len(members) > 1] + + +def parquet_counts(path: Path, label: str) -> tuple[Counter[str], str | None]: + try: + import pyarrow.parquet as pq + except ImportError: + return Counter(), "pyarrow unavailable" + try: + table = pq.read_table(path) + except Exception as error: + return Counter(), f"cannot read {label} parquet: {error}" + names = set(table.column_names) + key_column = next( + (name for name in ("feature_key", "primitive", "indicator_id") if name in names), None + ) + count_column = next( + (name for name in ("usage_count", "count", "strategy_count") if name in names), None + ) + if key_column is None: + return Counter(), f"{label} parquet has no recognized feature key column" + keys = table[key_column].to_pylist() + counts = table[count_column].to_pylist() if count_column else [1] * len(keys) + return Counter( + {str(key): float(count or 0) for key, count in zip(keys, counts, strict=True)} + ), None + + +def lineage_combo(value: Any) -> tuple[tuple[int, int, float], ...]: + """Parse the five ordered primitive triples stored at the start of lineage combos.""" + if isinstance(value, bytes): + value = value.decode("utf-8") + if isinstance(value, str): + value = json.loads(value) + if isinstance(value, dict): + if "combo" in value: + return lineage_combo(value["combo"]) + if all(role in value for role in LINEAGE_ROLES): + value = [value[role] for role in LINEAGE_ROLES] + elif "triples" in value: + value = value["triples"] + if not isinstance(value, (list, tuple)): + raise ValueError("combo is not an array") + if len(value) < len(LINEAGE_ROLES) * 3: + raise ValueError("combo does not contain at least five triples") + # Historical rows append strategy parameters after the five primitives. + triples = [value[index : index + 3] for index in range(0, len(LINEAGE_ROLES) * 3, 3)] + result = [] + for triple in triples: + if not isinstance(triple, (list, tuple)) or len(triple) != 3: + raise ValueError("combo contains an invalid primitive triple") + indicator_id, period, p1 = triple + if isinstance(indicator_id, bool) or isinstance(period, bool): + raise ValueError("combo primitive IDs must be integers") + indicator_id, period, p1 = int(indicator_id), int(period), float(p1) + if not np.isfinite(p1): + raise ValueError("combo primitive p1 must be finite") + result.append((indicator_id, period, p1)) + return tuple(result) + + +def lineage_usage(path: Path) -> tuple[Counter[str], dict[str, Any], str | None]: + try: + import pyarrow.parquet as pq + except ImportError: + return Counter(), {}, "pyarrow unavailable" + try: + table = pq.read_table(path) + except Exception as error: + return Counter(), {}, f"cannot read lineage parquet: {error}" + if "combo_json" not in table.column_names: + return Counter(), {}, "lineage parquet has no combo_json column" + + primitive_counts: Counter[str] = Counter() + role_counts: Counter[str] = Counter() + combo_counts: Counter[str] = Counter() + strategy_ids: set[str] = set() + invalid_rows = 0 + records = table.to_pylist() + for record in records: + try: + triples = lineage_combo(record["combo_json"]) + except (TypeError, ValueError, json.JSONDecodeError, UnicodeDecodeError, OverflowError): + invalid_rows += 1 + continue + signature = json.dumps(triples, separators=(",", ":")) + combo_counts[signature] += 1 + strategy_id = record.get("strategy_id") + if strategy_id is not None: + strategy_ids.add(str(strategy_id)) + for role, (indicator_id, period, p1) in zip(LINEAGE_ROLES, triples, strict=True): + primitive_counts[f"{indicator_id}:{period}:{p1:g}"] += 1 + role_counts[role] += 1 + blocker = ( + f"lineage parquet has {invalid_rows} rows with an unsupported combo_json format" + if invalid_rows + else None + ) + evidence = { + "lineage_rows": len(records), + "parsed_combo_rows": sum(combo_counts.values()), + "invalid_combo_rows": invalid_rows, + "strategy_ids_observed": len(strategy_ids), + "role_counts": dict(sorted(role_counts.items())), + "combo_counts": [ + {"combo_signature": signature, "usage_count": count} + for signature, count in sorted(combo_counts.items(), key=lambda item: (-item[1], item[0])) + ], + "feature_family_counts": dict(sorted(combo_counts.items())), + "primitive_counts": dict(sorted(primitive_counts.items())), + } + return primitive_counts, evidence, blocker + + +def deq_feature_key(value: Any) -> tuple[str, str]: + """Return the primitive and indicator-family keys exported with a strategy genome.""" + if not isinstance(value, dict): + raise ValueError("feature triple is not an object") + indicator_id, period, p1 = value.get("indicator_id"), value.get("period"), value.get("p1") + if isinstance(indicator_id, bool) or isinstance(period, bool): + raise ValueError("feature triple IDs must be integers") + indicator_id, period, p1 = int(indicator_id), int(period), float(p1) + if not np.isfinite(p1): + raise ValueError("feature triple p1 must be finite") + return f"{indicator_id}:{period}:{p1:g}", str(indicator_id) + + +def deq_strategy_feature_keys(strategy: dict[str, Any]) -> list[tuple[str, str]]: + """Read exported feature triples, falling back to the verified genome combo schema.""" + try: + triples = strategy.get("feature_triples") + if not isinstance(triples, list) or not triples: + raise ValueError("strategy has no feature_triples") + return [deq_feature_key(triple) for triple in triples] + except (TypeError, ValueError, OverflowError) as exported_error: + genome = strategy.get("genome") + try: + if not isinstance(genome, dict): + raise ValueError("strategy has no genome object") + return [ + (f"{indicator_id}:{period}:{p1:g}", str(indicator_id)) + for indicator_id, period, p1 in lineage_combo(genome.get("combo")) + ] + except (TypeError, ValueError, json.JSONDecodeError, UnicodeDecodeError, OverflowError) as genome_error: + raise ValueError( + f"strategy has no usable feature triples or genome combo ({exported_error}; {genome_error})" + ) from genome_error + + +def deq_summary(samples: list[dict[str, Any]]) -> dict[str, Any]: + """Summarize signed, already-recorded DEQ primitives without rerunning strategies.""" + result: dict[str, Any] = { + "status": "AVAILABLE" if samples else "INSUFFICIENT", + "trade_count": len(samples), + "fold_coverage": sorted({sample["fold"] for sample in samples}), + "scenario_coverage": sorted({sample["scenario"] for sample in samples}), + "returns": {}, + "means": {}, + "primitives": {}, + } + result["fold_count"] = len(result["fold_coverage"]) + result["scenario_count"] = len(result["scenario_coverage"]) + for field in DEQ_RETURN_FIELDS: + values = [sample["deq"].get(field) for sample in samples] + values = [float(value) for value in values if value is not None] + result["returns"][field] = { + "available_count": len(values), + "mean": float(np.mean(values)) if values else None, + # DEQ returns are signed in the strategy direction. Positive is therefore + # directionally correct, but says nothing causal about an associated feature. + "directional_correct_count": sum(value > 0 for value in values), + "directional_incorrect_count": sum(value < 0 for value in values), + "directional_neutral_count": sum(value == 0 for value in values), + "directional_correctness_inferable": bool(values), + } + for field in DEQ_MEAN_FIELDS: + values = [sample["deq"].get(field) for sample in samples] + values = [float(value) for value in values if value is not None] + result["means"][field] = { + "available_count": len(values), + "mean": float(np.mean(values)) if values else None, + } + for field in DEQ_PRIMITIVE_FIELDS: + values = [sample["deq"].get(field) for sample in samples] + available = [value for value in values if value is not None] + if available and all(isinstance(value, bool) for value in available): + result["primitives"][field] = { + "available_count": len(available), + "true_count": sum(available), + "false_count": len(available) - sum(available), + } + else: + numeric = [float(value) for value in available] + result["primitives"][field] = { + "available_count": len(numeric), + "mean": float(np.mean(numeric)) if numeric else None, + } + return result + + +def cohort_deq_evidence(path: Path) -> tuple[dict[str, Any], list[dict[str, Any]], str | None]: + """Aggregate the read-only Cohort001 DEQ export by strategy and associated genome inputs.""" + try: + payload = load_json(path) + if payload.get("contract") != "Cohort001-DEQ-ledger-export-v1": + raise ValueError("unexpected contract") + strategies = payload.get("strategies") + if not isinstance(strategies, list) or not strategies: + raise ValueError("strategies must be a non-empty array") + strategy_summaries = [] + feature_samples: dict[str, list[dict[str, Any]]] = {} + family_samples: dict[str, list[dict[str, Any]]] = {} + for strategy in strategies: + if not isinstance(strategy, dict) or strategy.get("strategy_version_id") is None: + raise ValueError("strategy has no strategy_version_id") + feature_keys = deq_strategy_feature_keys(strategy) + samples = [] + for fold in strategy.get("folds", []): + if not isinstance(fold, dict) or not isinstance(fold.get("fold"), str): + raise ValueError("strategy fold is invalid") + for scenario in fold.get("scenarios", []): + if not isinstance(scenario, dict) or not isinstance(scenario.get("scenario"), str): + raise ValueError("strategy scenario is invalid") + ledger = scenario.get("deq_samples") + if not isinstance(ledger, list): + raise ValueError("scenario has no deq_samples array") + for row in ledger: + if not isinstance(row, dict) or not isinstance(row.get("deq"), dict): + raise ValueError("DEQ sample has no deq object") + samples.append({"fold": fold["fold"], "scenario": scenario["scenario"], "deq": row["deq"]}) + strategy_id = str(strategy["strategy_version_id"]) + summary = deq_summary(samples) + strategy_summaries.append({"strategy_version_id": strategy_id, **summary}) + for feature_key, family_key in feature_keys: + feature_samples.setdefault(feature_key, []).extend(samples) + family_samples.setdefault(family_key, []).extend(samples) + feature_summaries = [ + {"feature_key": key, "attribution": "ASSOCIATIVE_NOT_CAUSAL", **deq_summary(samples)} + for key, samples in sorted(feature_samples.items()) + ] + family_summaries = [ + {"indicator_id": key, "attribution": "ASSOCIATIVE_NOT_CAUSAL", **deq_summary(samples)} + for key, samples in sorted(family_samples.items(), key=lambda item: int(item[0])) + ] + parquet_rows = [ + {"entity_type": "strategy", "entity_key": item["strategy_version_id"], **deq_parquet_row(item)} + for item in strategy_summaries + ] + [ + {"entity_type": "feature", "entity_key": item["feature_key"], **deq_parquet_row(item)} + for item in feature_summaries + ] + [ + {"entity_type": "family", "entity_key": item["indicator_id"], **deq_parquet_row(item)} + for item in family_summaries + ] + return { + "available": True, + "status": "AVAILABLE", + "attribution": "ASSOCIATIVE_NOT_CAUSAL", + "attribution_note": "Multi-feature genome attribution is associative, not causal.", + "strategy_summaries": strategy_summaries, + "feature_summaries": feature_summaries, + "family_summaries": family_summaries, + }, parquet_rows, None + except (TypeError, ValueError, json.JSONDecodeError) as error: + return {}, [], f"cannot use cohort DEQ export: {error}" + + +def deq_parquet_row(summary: dict[str, Any]) -> dict[str, Any]: + row = { + "status": summary["status"], + "attribution": summary.get("attribution", "STRATEGY_LEVEL"), + "trade_count": summary["trade_count"], + "fold_count": len(summary["fold_coverage"]), + "scenario_count": len(summary["scenario_coverage"]), + "fold_coverage": json.dumps(summary["fold_coverage"]), + "scenario_coverage": json.dumps(summary["scenario_coverage"]), + } + for field, values in summary["returns"].items(): + row[f"{field}_count"] = values["available_count"] + row[f"{field}_mean"] = values["mean"] + row[f"{field}_directional_correct_count"] = values["directional_correct_count"] + row[f"{field}_directional_incorrect_count"] = values["directional_incorrect_count"] + for field, values in summary["means"].items(): + row[f"{field}_count"] = values["available_count"] + row[f"{field}_mean"] = values["mean"] + for field, values in summary["primitives"].items(): + if "true_count" in values: + row[f"{field}_true_count"] = values["true_count"] + row[f"{field}_false_count"] = values["false_count"] + else: + row.setdefault(f"{field}_count", values["available_count"]) + row.setdefault(f"{field}_mean", values["mean"]) + return row + + +def failure_mining(report: Path | None, database: Path | None) -> tuple[Counter[str], list[str]]: + counts: Counter[str] = Counter() + blockers: list[str] = [] + if report: + + def walk(value: Any) -> None: + if isinstance(value, dict): + for key, item in value.items(): + if ( + key.lower() in {"status", "outcome", "failure_reason", "reason"} + and isinstance(item, str) + and any( + word in item.lower() for word in ("fail", "reject", "error", "block") + ) + ): + counts[item] += 1 + walk(item) + elif isinstance(value, list): + for item in value: + walk(item) + + walk(load_json(report)) + if database: + try: + connection = sqlite3.connect(f"file:{database.resolve().as_posix()}?mode=ro", uri=True) + with connection: + for (table,) in connection.execute( + "select name from sqlite_master where type='table'" + ): + columns = [ + row[1] for row in connection.execute(f'pragma table_info("{table}")') + ] + status = next( + ( + column + for column in columns + if column.lower() in {"status", "outcome", "failure_reason", "reason"} + ), + None, + ) + if status: + for (value,) in connection.execute( + f'select "{status}" from "{table}" where "{status}" is not null' + ): + text = str(value) + if any( + word in text.lower() + for word in ("fail", "reject", "error", "block") + ): + counts[text] += 1 + except sqlite3.Error as error: + blockers.append(f"cannot read qualification DB export read-only: {error}") + return counts, blockers + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + source = parser.add_mutually_exclusive_group(required=True) + source.add_argument("--engineering-map", type=Path) + source.add_argument("--oracle-request", type=Path) + checkpoint = parser.add_mutually_exclusive_group(required=True) + checkpoint.add_argument("--oracle-checkpoint", type=Path, help="frozen .npz checkpoint") + checkpoint.add_argument( + "--oracle-checkpoint-dir", + type=Path, + help="directory of frozen hs22_{id}_{period}_{p1}.npy checkpoints", + ) + parser.add_argument("--acceptance-manifest", type=Path, required=True) + parser.add_argument( + "--semantic-map", type=Path, + help="metadata map with feature_key or primitive fields and output_type/domain", + ) + parser.add_argument( + "--historical-usage", type=Path, required=True, help="historical usage parquet" + ) + parser.add_argument("--lineage", type=Path, required=True, help="lineage parquet") + parser.add_argument("--qualification-report", type=Path) + parser.add_argument("--qualification-db-export", type=Path) + parser.add_argument( + "--cohort-deq", + type=Path, + help="read-only Cohort001 DEQ JSON emitted by export_cohort_deq_v1.py", + ) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--sample-rows", type=int, default=8192) + parser.add_argument( + "--min-pair-samples", + type=int, + default=2, + help="minimum jointly finite sampled observations required for a feature pair", + ) + args = parser.parse_args() + if args.sample_rows < 2: + parser.error("--sample-rows must be at least 2") + if args.sample_rows > MAX_SAMPLE_ROWS: + parser.error( + f"--sample-rows may not exceed {MAX_SAMPLE_ROWS}; exact pairwise Spearman is " + "intentionally not offered because it is not bounded for 711 x 129k inputs" + ) + if args.min_pair_samples < 2: + parser.error("--min-pair-samples must be at least 2") + inputs = [ + path + for path in ( + args.engineering_map, + args.oracle_request, + args.oracle_checkpoint, + args.oracle_checkpoint_dir, + args.acceptance_manifest, + args.semantic_map, + args.historical_usage, + args.lineage, + args.qualification_report, + args.qualification_db_export, + args.cohort_deq, + ) + if path + ] + if any(not path.is_file() for path in inputs if path != args.oracle_checkpoint_dir): + parser.error("all supplied input paths except --oracle-checkpoint-dir must be files") + if args.oracle_checkpoint_dir and not args.oracle_checkpoint_dir.is_dir(): + parser.error("--oracle-checkpoint-dir must be a directory") + rows = feature_rows(args.engineering_map or args.oracle_request) + manifest = load_json(args.acceptance_manifest) + sample, names, source_row_count = sampled_checkpoint_columns( + args.oracle_checkpoint, args.oracle_checkpoint_dir, rows, args.sample_rows + ) + blockers: list[str] = [] + semantics, semantic_blocker = semantic_types(args.semantic_map, rows) + if semantic_blocker: + blockers.append(semantic_blocker) + output_types = [item["output_type"] for item in semantics] + finite_samples = np.isfinite(sample).sum(axis=0) + pearson, spearman, agreement, jaccard, pair_samples = type_aware_redundancy( + sample, output_types, args.min_pair_samples + ) + primary = primary_redundancy_matrix(pearson, agreement, jaccard, output_types) + usage, usage_blocker = parquet_counts(args.historical_usage, "historical usage") + lineage, lineage_evidence, lineage_blocker = lineage_usage(args.lineage) + blockers.extend(item for item in (usage_blocker, lineage_blocker) if item) + failures, failure_blockers = failure_mining( + args.qualification_report, args.qualification_db_export + ) + blockers.extend(failure_blockers) + deq_rows: list[dict[str, Any]] = [] + if args.cohort_deq: + deq_evidence, deq_rows, deq_blocker = cohort_deq_evidence(args.cohort_deq) + if deq_blocker: + blockers.append(deq_blocker) + deq_evidence = { + "available": False, + "status": "BLOCKED", + "blocker": deq_blocker, + } + else: + deq_evidence = { + "available": False, + "status": "BLOCKED", + "blocker": ( + "No supplied DEQ artifact or executable decision-equivalence protocol; " + "this runner does not fabricate DEQ." + ), + } + request_ids = {str(row["request_id"]) for row in rows if row["request_id"]} + accepted = accepted_ids(manifest) + if request_ids and not request_ids <= accepted: + blockers.append( + "acceptance manifest does not cover " + f"{len(request_ids - accepted)} source-map request IDs" + ) + acceptance_status = "covered" if request_ids and request_ids <= accepted else "not_proven" + feature_output = [] + for index, row in enumerate(rows): + scores = primary[index].copy() + scores[index] = np.nan + peer = int(np.nanargmax(scores)) if np.isfinite(scores).any() else None + finite_count = int(finite_samples[index]) + redundancy_status = ( + "unusable_no_finite_observations" + if finite_count == 0 + else "usable" + if peer is not None + else "insufficient_pairwise_observations" + ) + feature_output.append( + { + **row, + "checkpoint_key": names[index], + **semantics[index], + "historical_usage": usage.get( + row["feature_key"], usage.get(str(row["indicator_id"]), 0) + ), + "lineage_mentions": lineage.get(row["feature_key"], 0), + "finite_sample_count": finite_count, + "redundancy_status": redundancy_status, + "redundancy_metric": ( + "abs_pearson" if output_types[index] == "continuous" else + "jaccard_detection" if output_types[index] == "event" else "agreement" + ), + "max_redundancy": float(scores[peer]) if peer is not None else None, + "max_abs_pearson": float(abs(pearson[index, peer])) if peer is not None and np.isfinite(pearson[index, peer]) else None, + "max_abs_spearman": float(abs(spearman[index, peer])) if peer is not None and np.isfinite(spearman[index, peer]) else None, + "max_agreement": float(agreement[index, peer]) if peer is not None and np.isfinite(agreement[index, peer]) else None, + "max_jaccard_detection": float(jaccard[index, peer]) if peer is not None and np.isfinite(jaccard[index, peer]) else None, + "most_redundant_feature_key": ( + rows[peer]["feature_key"] if peer is not None else None + ), + "most_redundant_pair_sample_count": ( + int(pair_samples[index, peer]) if peer is not None else 0 + ), + "acceptance": acceptance_status, + } + ) + deq_by_feature = { + item["feature_key"]: item for item in deq_evidence.get("feature_summaries", []) + } + cluster_rows = [] + for index, members in enumerate(clusters(primary), start=1): + # Historical usage is the deterministic representative tie-breaker. + representative = max( + members, + key=lambda item: (feature_output[item]["historical_usage"], rows[item]["feature_key"]), + ) + cluster_rows.append( + { + "cluster_id": index, + "metric": feature_output[representative]["redundancy_metric"], + "representative_feature_key": rows[representative]["feature_key"], + "members": [rows[item]["feature_key"] for item in members], + "size": len(members), + "domains": sorted({semantics[item]["domain"] for item in members}), + "usage_total": sum(feature_output[item]["historical_usage"] for item in members), + "lineage_mentions_total": sum(feature_output[item]["lineage_mentions"] for item in members), + "representative_deq": deq_by_feature.get(rows[representative]["feature_key"]), + } + ) + taxonomy = load_json(Path(__file__).with_name("hyperscalper_candidate_taxonomy_v1.json")) + result = { + "schema_version": 1, + "artifact": ARTIFACT, + "read_only": True, + "strategy_execution": "not performed", + "source_hashes": {str(path): source_hash(path) for path in inputs}, + "information_map": { + "expected_features": EXPECTED_FEATURES, + "mapped_features": len(rows), + "acceptance": acceptance_status, + "features": feature_output, + }, + "redundancy": { + "method": ( + "type-aware pairwise-finite sampled metrics: Pearson and pre-ranked ordinal " + "correlation for continuous, agreement for state, Jaccard detection for event" + ), + "mode": "sampled_bounded_memory", + "exact_mode": "not offered: exact pairwise Spearman across 711 x 129k is not bounded-memory/time practical", + "source_rows": source_row_count, + "sample_rows_requested": args.sample_rows, + "sample_rows_used": int(sample.shape[0]), + "min_pair_samples": args.min_pair_samples, + "features_without_finite_observations": int((finite_samples == 0).sum()), + "cluster_threshold": 0.95, + "clusters": cluster_rows, + "map": { + "format": "parquet", + "path": "hyperscalper_feature_redundancy_map_v1.parquet", + "pair_count": len(rows) * (len(rows) - 1) // 2, + }, + }, + "lineage_usage_bias": { + "historical_usage_available": usage_blocker is None, + "lineage_available": lineage_blocker is None, + "historical_search_bias_evidence": lineage_evidence, + }, + "cohort_failure_mining": { + "failures": dict(failures.most_common()), + "report_supplied": bool(args.qualification_report), + "db_export_supplied": bool(args.qualification_db_export), + }, + "decision_equivalence": deq_evidence, + "candidate_taxonomy": taxonomy, + "blockers": blockers, + } + args.output_dir.mkdir(parents=True, exist_ok=True) + json_path = args.output_dir / "hyperscalper_feature_gap_analysis_v1.json" + markdown = ( + "# HYPERSCALPER Feature Gap Analysis V1\n\n" + "Read-only: yes. Strategy execution: not performed.\n\n" + f"- Information map: {len(rows)}/711 features\n" + f"- Acceptance: {acceptance_status}\n" + f"- Redundancy clusters: {len(cluster_rows)}\n" + f"- DEQ: {deq_evidence['status']}" + ) + ( + " (supplied Cohort001 ledger; multi-feature attribution is associative, not causal)\n" + if deq_evidence["available"] + else " (not supplied or invalid; not fabricated)\n" + ) + ( + f"- Blockers: {len(blockers)}\n\n## Blockers\n" + + "\n".join(f"- {item}" for item in blockers) + + "\n" + ) + (args.output_dir / "hyperscalper_feature_gap_analysis_v1.md").write_text( + markdown, encoding="utf-8" + ) + (args.output_dir / "hyperscalper_feature_redundancy_clusters_v1.json").write_text( + json.dumps( + { + "artifact": "HYPERSCALPER_FEATURE_REDUNDANCY_CLUSTERS_V1", + "read_only": True, + "strategy_execution": "not performed", + "mode": result["redundancy"]["mode"], + "clusters": cluster_rows, + }, + indent=2, + sort_keys=True, + ) + "\n", + encoding="utf-8", + ) + try: + import pyarrow as pa + import pyarrow.parquet as pq + + pq.write_table( + pa.Table.from_pylist(feature_output), + args.output_dir / "hyperscalper_feature_gap_analysis_v1.parquet", + compression="zstd", + ) + pq.write_table( + pa.Table.from_pylist( + [ + { + "left_feature_key": rows[left]["feature_key"], + "right_feature_key": rows[right]["feature_key"], + "left_output_type": output_types[left], + "right_output_type": output_types[right], + "redundancy_metric": ( + "abs_pearson" if output_types[left] == output_types[right] == "continuous" else + "jaccard_detection" if output_types[left] == output_types[right] == "event" else + "agreement" if output_types[left] == output_types[right] == "state" else "not_comparable" + ), + "pair_sample_count": int(pair_samples[left, right]), + "pearson": ( + float(pearson[left, right]) + if np.isfinite(pearson[left, right]) + else None + ), + "spearman_ordinal": ( + float(spearman[left, right]) + if np.isfinite(spearman[left, right]) + else None + ), + "agreement": ( + float(agreement[left, right]) + if np.isfinite(agreement[left, right]) + else None + ), + "jaccard_detection": ( + float(jaccard[left, right]) + if np.isfinite(jaccard[left, right]) + else None + ), + "primary_redundancy": ( + float(primary[left, right]) + if np.isfinite(primary[left, right]) + else None + ), + } + for left in range(len(rows)) + for right in range(left + 1, len(rows)) + ] + ), + args.output_dir / "hyperscalper_feature_redundancy_map_v1.parquet", + compression="zstd", + ) + if args.cohort_deq and deq_evidence["available"]: + pq.write_table( + pa.Table.from_pylist(deq_rows), + args.output_dir / "historical_feature_deq_summary_v1.parquet", + compression="zstd", + ) + except ImportError: + result["parquet"] = "not written: pyarrow unavailable" + result["redundancy"]["map"]["status"] = "not written: pyarrow unavailable" + json_path.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json_path) + + +if __name__ == "__main__": + main() diff --git a/hyperscalper_wave1_feature_synthesis_v1.py b/hyperscalper_wave1_feature_synthesis_v1.py new file mode 100644 index 0000000..5d725b2 --- /dev/null +++ b/hyperscalper_wave1_feature_synthesis_v1.py @@ -0,0 +1,310 @@ +#!/usr/bin/env python3 +"""Synthesize evidence-backed, OHLCV-only Wave 1 feature candidates. + +This is a read-only reporting tool. It does not evaluate formulas, search +strategies, infer data provenance, or turn associative evidence into causality. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from collections import defaultdict +from pathlib import Path +from typing import Any + +ARTIFACT = "HYPERSCALPER_WAVE1_FEATURE_SYNTHESIS_V1" +MIN_CANDIDATES = 20 +MAX_CANDIDATES = 50 +ASSOCIATIVE_LIMITATION = ( + "Evidence is observational and multi-feature attribution is associative, not causal." +) + + +def load_json(path: Path) -> dict[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError(f"{path} must contain a JSON object") + return value + + +def source_record(path: Path | None) -> dict[str, Any]: + if path is None: + return {"status": "UNAVAILABLE", "reason": "not supplied"} + return { + "status": "AVAILABLE", + "path": str(path), + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + } + + +def rows(value: dict[str, Any], *names: str) -> list[dict[str, Any]]: + for name in names: + found = value.get(name) + if isinstance(found, list): + return [item for item in found if isinstance(item, dict)] + return [] + + +def feature_key(row: dict[str, Any]) -> str | None: + value = row.get("feature_key") + if value is not None: + return str(value) + try: + return f"{int(row['indicator_id'])}:{int(row['period'])}:{float(row['p1']):g}" + except (KeyError, TypeError, ValueError): + return None + + +def is_ohlcv_only(row: dict[str, Any]) -> bool: + """Accept only an explicit, finite OHLCV dependency declaration.""" + scope = str(row.get("input_scope", row.get("data_scope", ""))).upper() + if scope in {"OHLCV", "OHLCV_ONLY"}: + return True + dependencies = row.get("data_dependencies", row.get("inputs")) + if not isinstance(dependencies, list) or not dependencies: + return False + return {str(item).lower() for item in dependencies} <= { + "open", + "high", + "low", + "close", + "volume", + } + + +def numeric(value: Any) -> float: + return float(value) if isinstance(value, (int, float)) and not isinstance(value, bool) else 0.0 + + +def failure_evidence(payload: dict[str, Any]) -> dict[str, list[dict[str, Any]]]: + result: dict[str, list[dict[str, Any]]] = defaultdict(list) + for row in rows(payload, "feature_failure_comparisons", "features", "comparisons"): + key = feature_key(row) + if key: + result[key].append(row) + return result + + +def deq_evidence(payload: dict[str, Any]) -> dict[str, dict[str, Any]]: + deq = payload.get("decision_equivalence", payload) + if not isinstance(deq, dict): + return {} + return { + str(row["feature_key"]): row + for row in rows(deq, "feature_summaries", "features") + if row.get("feature_key") is not None + } + + +def redundancy_evidence(payload: dict[str, Any]) -> dict[str, dict[str, Any]]: + result: dict[str, dict[str, Any]] = {} + for cluster in rows(payload.get("redundancy", payload), "clusters"): + for key in cluster.get("members", []): + result[str(key)] = { + "cluster_id": cluster.get("cluster_id"), + "cluster_size": cluster.get("size"), + "representative_feature_key": cluster.get("representative_feature_key"), + } + return result + + +def synthesis( + semantic: dict[str, Any], + redundancy: dict[str, Any], + failure: dict[str, Any] | None, + deq: dict[str, Any], + lineage_gap: dict[str, Any], +) -> tuple[list[dict[str, Any]], list[str], int]: + feature_rows = rows(semantic, "features", "primitives", "rows", "semantic_map") + if not feature_rows: + feature_rows = rows(lineage_gap.get("information_map", {}), "features") + failures = failure_evidence(failure) if failure else {} + deq_by_feature = deq_evidence(deq) + clusters = redundancy_evidence(redundancy) + lineage_by_feature = { + str(row.get("feature_key")): row + for row in rows(lineage_gap.get("information_map", {}), "features") + if row.get("feature_key") is not None + } + excluded_non_ohlcv = 0 + candidates = [] + for row in feature_rows: + key = feature_key(row) + if key is None: + continue + if not is_ohlcv_only(row): + excluded_non_ohlcv += 1 + continue + lineage = lineage_by_feature.get(key, row) + feature_failures = failures.get(key, []) + candidates.append( + { + "candidate_id": f"ohlcv:{key}", + "feature_key": key, + "indicator_id": row.get("indicator_id"), + "period": row.get("period"), + "p1": row.get("p1"), + "domain": row.get("domain", row.get("semantic_domain", "unclassified")), + "output_type": row.get("output_type", row.get("semantic_type", "unclassified")), + "input_scope": "OHLCV_ONLY", + "evidence": { + "semantic": {"feature_key": key, "explicit_ohlcv_only": True}, + "lineage_gap": { + "historical_usage": lineage.get("historical_usage"), + "lineage_mentions": lineage.get("lineage_mentions"), + "max_redundancy": lineage.get("max_redundancy"), + "redundancy_status": lineage.get("redundancy_status"), + }, + "redundancy_cluster": clusters.get(key), + "failure_mining": feature_failures or None, + "decision_equivalence": deq_by_feature.get(key), + }, + "limitations": [ + ASSOCIATIVE_LIMITATION, + "Not a strategy approval or causal feature claim.", + ], + } + ) + + # Evidence ordering only: failure contrast, DEQ observations, lineage use, then redundancy. + def rank(candidate: dict[str, Any]) -> tuple[float, float, float, float, str]: + failure_rows = candidate["evidence"]["failure_mining"] or [] + failure_strength = max( + (numeric(item.get("ks_statistic")) for item in failure_rows), default=0.0 + ) + deq_row = candidate["evidence"]["decision_equivalence"] or {} + deq_support = numeric(deq_row.get("trade_count")) + lineage = candidate["evidence"]["lineage_gap"] + usage = numeric(lineage.get("historical_usage")) + numeric(lineage.get("lineage_mentions")) + redundancy_value = lineage.get("max_redundancy") + redundancy = numeric(redundancy_value) if redundancy_value is not None else 1.0 + return (-failure_strength, -deq_support, -usage, redundancy, candidate["feature_key"]) + + return sorted(candidates, key=rank), [], excluded_non_ohlcv + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--semantic-summary", type=Path, required=True) + parser.add_argument("--redundancy-clusters", type=Path, required=True) + parser.add_argument("--deq-summary", type=Path, required=True) + parser.add_argument("--lineage-gap-analysis", type=Path, required=True) + parser.add_argument( + "--failure-mining", type=Path, help="Copied Spark failure-mining JSON when available" + ) + parser.add_argument("--output-dir", type=Path, required=True) + args = parser.parse_args() + required = ( + args.semantic_summary, + args.redundancy_clusters, + args.deq_summary, + args.lineage_gap_analysis, + ) + if any(not path.is_file() for path in required) or ( + args.failure_mining and not args.failure_mining.is_file() + ): + parser.error("all supplied artifact paths must be files") + + semantic, redundancy, deq, lineage = (load_json(path) for path in required) + failure = load_json(args.failure_mining) if args.failure_mining else None + candidates, blockers, excluded_non_ohlcv = synthesis( + semantic, redundancy, failure, deq, lineage + ) + selected = candidates[:MAX_CANDIDATES] + if args.failure_mining is None: + blockers.append( + "Spark failure-mining result is unavailable; copy its JSON before relying on " + "failure evidence." + ) + if len(selected) < MIN_CANDIDATES: + blockers.append( + f"Only {len(selected)} explicitly OHLCV-only candidates are evidenced; refusing to " + f"invent the required {MIN_CANDIDATES}-{MAX_CANDIDATES} selection." + ) + status = "READY" if not blockers else "BLOCKED" + sources = { + "semantic_summary": source_record(args.semantic_summary), + "redundancy_clusters": source_record(args.redundancy_clusters), + "failure_mining": source_record(args.failure_mining), + "deq_summary": source_record(args.deq_summary), + "lineage_gap_analysis": source_record(args.lineage_gap_analysis), + } + common = { + "schema_version": 1, + "artifact": ARTIFACT, + "read_only": True, + "strategy_search": "not performed", + "associative_limitations": ASSOCIATIVE_LIMITATION, + "status": status, + "sources": sources, + "blockers": blockers, + } + taxonomy = { + **common, + "artifact": "FEATURE_GAP_TAXONOMY_V1", + "candidates": selected, + "excluded_without_explicit_ohlcv_only_declaration": excluded_non_ohlcv, + } + plan = { + **common, + "artifact": "FEATURE_EXPANSION_PLAN_V1", + "wave": 1, + "admission_rule": "Explicit OHLCV-only declaration and evidence-ranked source records.", + "candidate_count": len(selected), + "candidates": [item["candidate_id"] for item in selected], + } + registry = {**common, "artifact": "NEW_FEATURE_CANDIDATE_REGISTRY_V1", "candidates": selected} + spec = { + **common, + "artifact": "HYPERSCALPER_WAVE1_FEATURE_SPEC_V1", + "features": selected, + "implementation_constraint": ( + "Implement features from OHLCV inputs only; no strategy search or causal claims." + ), + } + args.output_dir.mkdir(parents=True, exist_ok=True) + outputs = { + "feature_gap_taxonomy_v1.json": taxonomy, + "feature_expansion_plan_v1.json": plan, + "new_feature_candidate_registry_v1.json": registry, + "hyperscalper_wave1_feature_spec_v1.json": spec, + } + for name, payload in outputs.items(): + (args.output_dir / name).write_text( + json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + markdown = ( + "# HYPERSCALPER Wave 1 Feature Spec V1\n\n" + f"Status: {status}. Read-only: yes. Strategy search: not performed.\n\n" + f"Selected candidates: {len(selected)}. Explicitly excluded without OHLCV-only evidence: " + f"{excluded_non_ohlcv}.\n\n" + "## Limitations\n\n" + f"- {ASSOCIATIVE_LIMITATION}\n" + "- No feature is a strategy, approval, or causal claim.\n\n" + "## Candidates\n\n" + + "\n".join( + f"- `{item['candidate_id']}`: {item['domain']} / {item['output_type']}" + for item in selected + ) + + "\n\n## Blockers\n\n" + + "\n".join(f"- {item}" for item in blockers) + + "\n" + ) + (args.output_dir / "hyperscalper_wave1_feature_spec_v1.md").write_text( + markdown, encoding="utf-8" + ) + executive = ( + f"Wave 1 synthesis is {status}: {len(selected)} evidence-ranked, explicitly OHLCV-only " + "candidates. " + f"Failure mining is {'available' if failure else 'unavailable'}. {ASSOCIATIVE_LIMITATION}\n" + ) + (args.output_dir / "hyperscalper_wave1_executive_summary_v1.md").write_text( + executive, encoding="utf-8" + ) + print(args.output_dir / "feature_gap_taxonomy_v1.json") + + +if __name__ == "__main__": + main() diff --git a/run_gpu_feature_batch01_v1.sh b/run_gpu_feature_batch01_v1.sh new file mode 100644 index 0000000..58b1a7a --- /dev/null +++ b/run_gpu_feature_batch01_v1.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Usage: ./run_gpu_feature_batch01_v1.sh [cache-directory] +# The source mounts are read-only; only the cache directory is writable. +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +CACHE_DIR="${1:-${ROOT}/.gpu-feature-cache}" +mkdir -p "${CACHE_DIR}" + +docker build -f "${ROOT}/Dockerfile.gpu-feature-v1" -t artifex-gpu-feature-v1 "${ROOT}" +docker run --rm --gpus all \ + --mount "type=bind,src=${ROOT}/hs22_oracle_v1,dst=/data,readonly" \ + --mount "type=bind,src=${ROOT},dst=/oracle,readonly" \ + --mount "type=bind,src=${CACHE_DIR},dst=/cache" \ + artifex-gpu-feature-v1 --mode parity --report /cache/batch01_gpu_parity_report.json + +echo "Wrote ${CACHE_DIR}/batch01_gpu_parity_report.json" diff --git a/scripts/generate_historical_feature_semantic_domain_map_v1.py b/scripts/generate_historical_feature_semantic_domain_map_v1.py new file mode 100644 index 0000000..12d77ae --- /dev/null +++ b/scripts/generate_historical_feature_semantic_domain_map_v1.py @@ -0,0 +1,230 @@ +"""Generate a read-only semantic map for the 711 historical primitives. + +The generator consumes engineering/source metadata only. It never imports or +evaluates a historical formula, and optional checkpoints are inspected solely +to count already-materialized finite observations. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Any + +import numpy as np +import pyarrow as pa +import pyarrow.parquet as pq + + +EXPECTED_PRIMITIVES = 711 + + +def digest(path: Path) -> str: + if path.is_file(): + return hashlib.sha256(path.read_bytes()).hexdigest() + hasher = hashlib.sha256() + for item in sorted(path.glob("*.npy")): + hasher.update(item.name.encode("utf-8")) + hasher.update(b"\0") + hasher.update(bytes.fromhex(digest(item))) + return hasher.hexdigest() + + +def key_for(row: dict[str, Any]) -> tuple[int, int, float]: + return int(row["indicator_id"]), int(row["period"]), float(row["p1"]) + + +def key_text(key: tuple[int, int, float]) -> str: + return f"{key[0]}:{key[1]}:{key[2]:g}" + + +def composite_key(value: Any) -> tuple[int, int, float]: + """Decode canonical primitive keys stored as strings or Parquet structs.""" + if isinstance(value, dict): + return key_for(value) + if isinstance(value, (list, tuple)) and len(value) == 3: + return int(value[0]), int(value[1]), float(value[2]) + if not isinstance(value, str): + raise ValueError(f"invalid composite primitive key: {value!r}") + parts = value.strip().split(":") + if len(parts) != 3: + parts = value.strip().split("_") + if len(parts) == 4 and parts[0] == "hs22": + parts = parts[1:] + if len(parts) != 3: + raise ValueError(f"invalid composite primitive key: {value!r}") + try: + return int(parts[0]), int(parts[1]), float(parts[2]) + except ValueError as error: + raise ValueError(f"invalid composite primitive key: {value!r}") from error + + +def source_name(row: dict[str, Any]) -> str: + return str(row.get("source_function", "")).lower() + + +def semantic_mapping(row: dict[str, Any]) -> tuple[str, str, str, str, str]: + """Return domain, subdomain, type, provenance, and deterministic rule id.""" + name = source_name(row) + family = str(row.get("engineering_family", "")) + rules = ( + (("cross_", "crossover", "crossunder", "breakout", "divergence"), "price_action", "crossing_or_breakout", "event", "source_derived", "explicit_event_name"), + (("regime_", "entropy_", "chop", "fractal_dimension"), "market_regime", "regime_detection", "state", "source_derived", "explicit_regime_name"), + (("time_", "session", "weekday", "hour"), "time_context", "session_or_calendar", "state", "source_derived", "explicit_time_name"), + (("micro_", "orderflow", "vwap", "obv", "mfi", "cmf", "volume"), "volume_and_flow", "volume_flow", "continuous", "source_derived", "explicit_volume_name"), + (("osc_", "rsi", "stoch", "cci", "williams", "roc", "momentum"), "momentum", "oscillator_or_rate", "continuous", "source_derived", "explicit_momentum_name"), + (("bb_", "kelt_", "donch_", "ichimoku_", "supertrend", "psar", "channel"), "volatility", "bands_and_channels", "state", "source_derived", "explicit_band_name"), + (("vol_", "atr", "true_range", "variance", "std", "range"), "volatility", "range_and_dispersion", "continuous", "source_derived", "explicit_volatility_name"), + (("ma_", "ema", "sma", "wma", "tema", "dema"), "trend", "moving_average", "continuous", "source_derived", "explicit_average_name"), + (("adx", "aroon_", "linreg_", "trend_"), "trend", "direction_and_strength", "continuous", "source_derived", "explicit_trend_name"), + (("pivot_", "prev_", "rolling_", "quantile_"), "price_structure", "reference_level", "state", "source_derived", "explicit_reference_name"), + (("norm_", "zscore", "percentile"), "normalization", "scaled_price_or_signal", "continuous", "source_derived", "explicit_normalization_name"), + ) + for prefixes, domain, subdomain, value_type, provenance, rule in rules: + if any(prefix in name for prefix in prefixes): + return domain, subdomain, value_type, provenance, rule + family_fallbacks = { + "moving_average": ("trend", "moving_average", "continuous"), + "oscillator": ("momentum", "oscillator_or_rate", "continuous"), + "volatility": ("volatility", "range_and_dispersion", "continuous"), + "regime": ("market_regime", "regime_detection", "state"), + "microstructure": ("market_microstructure", "price_volume_structure", "continuous"), + "momentum": ("momentum", "oscillator_or_rate", "continuous"), + "trend_structure": ("trend", "direction_and_strength", "continuous"), + "normalization": ("normalization", "scaled_price_or_signal", "continuous"), + "cross_indicator": ("price_action", "crossing_or_breakout", "event"), + "time_session": ("time_context", "session_or_calendar", "state"), + "reference_level": ("price_structure", "reference_level", "state"), + "band_channel": ("volatility", "bands_and_channels", "state"), + } + domain, subdomain, value_type = family_fallbacks.get( + family, ("specialized", "unclassified_source_function", "continuous") + ) + return domain, subdomain, value_type, "heuristic", "engineering_family_fallback" + + +def load_engineering_map(path: Path) -> list[dict[str, Any]]: + payload = json.loads(path.read_text(encoding="utf-8")) + if payload.get("artifact") != "ENGINEERING_FAMILY_MAP_V1": + raise ValueError("--engineering-map is not ENGINEERING_FAMILY_MAP_V1") + rows = payload.get("primitives") + if not isinstance(rows, list): + raise ValueError("engineering map has no primitives list") + keys = [key_for(row) for row in rows] + if len(rows) != EXPECTED_PRIMITIVES or len(set(keys)) != EXPECTED_PRIMITIVES: + raise ValueError("engineering map must contain exactly 711 unique primitives") + return rows + + +def usage_counts(path: Path, expected_indicator_ids: set[int]) -> dict[int, int]: + """Load indicator-level usage; it cannot distinguish parameterized primitives.""" + counts: dict[int, int] = {} + source = pq.ParquetFile(path) + columns = set(source.schema.names) + required = {"entity_type", "indicator_id", "usage_count"} + if missing := required - columns: + raise ValueError(f"usage parquet is missing required columns: {', '.join(sorted(missing))}") + for batch in source.iter_batches(columns=["entity_type", "indicator_id", "usage_count"]): + for row in batch.to_pylist(): + if row["entity_type"] != "indicator_id": + continue + indicator_id = int(row["indicator_id"]) + if indicator_id not in expected_indicator_ids: + continue + if indicator_id in counts: + raise ValueError(f"usage parquet has duplicate indicator row: {indicator_id}") + counts[indicator_id] = int(row["usage_count"]) + if set(counts) != expected_indicator_ids: + raise ValueError("usage parquet does not cover every engineering indicator ID") + return counts + + +def checkpoint_counts(directory: Path, expected: set[tuple[int, int, float]]) -> tuple[dict[tuple[int, int, float], int], dict[tuple[int, int, float], str]]: + counts: dict[tuple[int, int, float], int] = {} + names: dict[tuple[int, int, float], str] = {} + for path in sorted(directory.glob("*.npy")): + parts = path.stem.split("_") + if len(parts) != 4 or parts[0] != "hs22": + raise ValueError(f"invalid checkpoint filename: {path.name}") + try: + key = int(parts[1]), int(parts[2]), float(parts[3]) + except ValueError as error: + raise ValueError(f"invalid checkpoint filename: {path.name}") from error + if key not in expected or key in counts: + raise ValueError(f"checkpoint is not a unique engineering primitive: {path.name}") + counts[key] = int(np.isfinite(np.load(path, allow_pickle=False)).sum()) + names[key] = path.name + if set(counts) != expected: + raise ValueError("checkpoint directory does not cover every engineering primitive") + return counts, names + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--engineering-map", type=Path, required=True) + parser.add_argument("--usage-parquet", type=Path) + parser.add_argument("--checkpoint-dir", type=Path) + parser.add_argument("--output-dir", type=Path, required=True) + args = parser.parse_args() + if args.usage_parquet and not args.usage_parquet.is_file(): + parser.error("--usage-parquet must be a file") + if args.checkpoint_dir and not args.checkpoint_dir.is_dir(): + parser.error("--checkpoint-dir must be a directory") + + engineering = load_engineering_map(args.engineering_map) + expected = {key_for(row) for row in engineering} + expected_indicator_ids = {key[0] for key in expected} + usage = usage_counts(args.usage_parquet, expected_indicator_ids) if args.usage_parquet else {} + finite, checkpoint_names = checkpoint_counts(args.checkpoint_dir, expected) if args.checkpoint_dir else ({}, {}) + rows = [] + for engineering_row in engineering: + key = key_for(engineering_row) + domain, subdomain, value_type, provenance, rule = semantic_mapping(engineering_row) + rows.append({ + "feature_key": key_text(key), "indicator_id": key[0], "period": key[1], "p1": key[2], + "source_function": engineering_row.get("source_function"), + "source_expression": engineering_row.get("source_expression"), + "engineering_family": engineering_row.get("engineering_family"), + "semantic_domain": domain, "semantic_subdomain": subdomain, "semantic_type": value_type, + "semantic_mapping_provenance": provenance, "semantic_mapping_rule": rule, + "usage_count": usage.get(key[0]), + "usage_grain": "indicator_id_replicated_not_parameter_specific" if args.usage_parquet else None, + "finite_observation_count": finite.get(key), + "checkpoint_file": checkpoint_names.get(key), + }) + + args.output_dir.mkdir(parents=True, exist_ok=True) + parquet_path = args.output_dir / "historical_feature_semantic_domain_map_v1.parquet" + pq.write_table(pa.Table.from_pylist(rows), parquet_path, compression="zstd") + sources: dict[str, dict[str, str]] = {"engineering_map": {"path": str(args.engineering_map), "sha256": digest(args.engineering_map)}} + if args.usage_parquet: + sources["usage_parquet"] = {"path": str(args.usage_parquet), "sha256": digest(args.usage_parquet)} + if args.checkpoint_dir: + sources["checkpoint_dir"] = {"path": str(args.checkpoint_dir), "sha256": digest(args.checkpoint_dir)} + summary = { + "schema_version": 1, + "artifact": "HISTORICAL_FEATURE_SEMANTIC_DOMAIN_MAP_V1_SUMMARY", + "method": "read-only source-function semantic classification; explicit name rules are source-derived and family fallbacks are heuristic; no historical formulas were imported or evaluated", + "source_artifacts": sources, + "counts": { + "primitives": len(rows), + "semantic_domains": len({row["semantic_domain"] for row in rows}), + "semantic_subdomains": len({row["semantic_subdomain"] for row in rows}), + "source_derived_mappings": sum(row["semantic_mapping_provenance"] == "source_derived" for row in rows), + "heuristic_mappings": sum(row["semantic_mapping_provenance"] == "heuristic" for row in rows), + "usage_count_total_replicated_across_primitives": sum(row["usage_count"] or 0 for row in rows), + "finite_observation_count_total": sum(row["finite_observation_count"] or 0 for row in rows), + }, + "semantic_type_counts": {kind: sum(row["semantic_type"] == kind for row in rows) for kind in ("continuous", "state", "event")}, + "usage_grain": "indicator_id_replicated_not_parameter_specific" if args.usage_parquet else None, + "artifacts": {"semantic_domain_map_parquet": str(parquet_path), "semantic_domain_map_parquet_sha256": digest(parquet_path)}, + } + (args.output_dir / "historical_feature_semantic_domain_map_v1_summary.json").write_text( + json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + + +if __name__ == "__main__": + main() diff --git a/supertrend_atr_numeric_drift_diagnostic_v1_2.json b/supertrend_atr_numeric_drift_diagnostic_v1_2.json new file mode 100644 index 0000000..c63bdfe --- /dev/null +++ b/supertrend_atr_numeric_drift_diagnostic_v1_2.json @@ -0,0 +1 @@ +{"adversarial": {"generator": "deterministic_adversarial_ohlcv", "length": 256, "seed": 0}, "artifact": "GPU_SUPERTREND_ATR_SEMANTIC_DIAGNOSTIC_V1_2", "corpora": {"adversarial": {"artifact": "GPU_SUPERTREND_ATR_SEMANTIC_DIAGNOSTIC_V1_2", "bars": 256, "device": "cuda", "period": 10, "records": [{"branch_predicates": {"active_long": {"exact": true, "first_divergence": null}, "close_ge_lower": {"exact": true, "first_divergence": null}, "close_le_upper": {"exact": true, "first_divergence": null}, "output_uses_lower": {"exact": true, "first_divergence": null}, "prior_close_ge_lower": {"exact": true, "first_divergence": null}, "prior_close_le_upper": {"exact": true, "first_divergence": null}}, "direction": {"exact": true, "first_divergence": null}, "multiplier": 2.0, "numeric": {"atr": {"finite_pairs": 247, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "basic_lower": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "basic_upper": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "lower": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "output": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "true_range": {"finite_pairs": 256, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "upper": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}}, "period": 10, "transitions": {"direction_transition": {"exact": true, "first_divergence": null}}}, {"branch_predicates": {"active_long": {"exact": true, "first_divergence": null}, "close_ge_lower": {"exact": true, "first_divergence": null}, "close_le_upper": {"exact": true, "first_divergence": null}, "output_uses_lower": {"exact": true, "first_divergence": null}, "prior_close_ge_lower": {"exact": true, "first_divergence": null}, "prior_close_le_upper": {"exact": true, "first_divergence": null}}, "direction": {"exact": true, "first_divergence": null}, "multiplier": 3.0, "numeric": {"atr": {"finite_pairs": 247, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "basic_lower": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "basic_upper": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, 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true, "first_divergence": null}, "output_uses_lower": {"exact": true, "first_divergence": null}, "prior_close_ge_lower": {"exact": true, "first_divergence": null}, "prior_close_le_upper": {"exact": true, "first_divergence": null}}, "direction": {"exact": true, "first_divergence": null}, "multiplier": 4.0, "numeric": {"atr": {"finite_pairs": 247, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "basic_lower": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "basic_upper": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "lower": {"finite_pairs": 246, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, 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"nonfinite_mismatches": 0}, "output": {"finite_pairs": 64789, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "true_range": {"finite_pairs": 64799, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}, "upper": {"finite_pairs": 64789, "first_divergence": null, "max_abs": 0.0, "max_rel": 0.0, "max_rel_unbounded_at_zero_cpu": false, "max_ulp": 0.0, "nonfinite_mismatches": 0}}, "period": 10, "transitions": {"direction_transition": {"exact": true, "first_divergence": null}}}], "schema_version": "1.2"}}, "device": "cuda", "schema_version": "1.2"} diff --git a/tests/test_cohort001_feature_failure_mining_v1.py b/tests/test_cohort001_feature_failure_mining_v1.py new file mode 100644 index 0000000..32e076e --- /dev/null +++ b/tests/test_cohort001_feature_failure_mining_v1.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import json +import sys + +import numpy as np +import pytest + +import cohort001_feature_failure_mining_v1 as runner + + +def test_entry_bar_uses_exported_column_then_raw_ledger_and_rejects_timestamps(): + assert runner.entry_bar({"entry_bar": 4}) == (4, "column.entry_bar") + assert runner.entry_bar({"raw_ledger_json": '{"entry_index":3}'}) == (3, "raw_ledger_json.entry_index") + assert runner.entry_bar({"entry_at": "2026-01-01T00:00:00Z"}) == (None, None) + + +def test_json_compatible_normalizes_numpy_scalars_and_nonfinite_floats(): + payload = runner.json_compatible({ + "integer": np.int64(7), + "float": np.float32(1.25), + "boolean": np.bool_(True), + "nested": [np.float64(np.nan), np.float64(np.inf), float("-inf")], + }) + + assert payload == { + "integer": 7, + "float": pytest.approx(1.25), + "boolean": True, + "nested": [None, None, None], + } + assert json.loads(json.dumps(payload, allow_nan=False))["nested"] == [None, None, None] + + +def test_runner_mines_entry_values_and_skips_unmappable_rows(tmp_path, monkeypatch): + pa = pytest.importorskip("pyarrow") + pq = pytest.importorskip("pyarrow.parquet") + engineering = tmp_path / "engineering.json" + engineering.write_text(json.dumps({"primitives": [{"indicator_id": 1, "period": 10, "p1": 0}]})) + semantic = tmp_path / "semantic.parquet" + pq.write_table(pa.Table.from_pylist([{"feature_key": "1:10:0", "domain": "trend", "output_type": "continuous"}]), semantic) + checkpoints = tmp_path / "checkpoints" + checkpoints.mkdir() + np.save(checkpoints / "hs22_1_10_0.npy", np.array([1.0, 2.0, 8.0, 9.0])) + context = tmp_path / "context.parquet" + pq.write_table(pa.Table.from_pylist([ + {"failure_label": "GOOD_ENTRY", "entry_bar": 0, "fold": "F1", "feature_triples_json": '[{"indicator_id":1,"period":10,"p1":0}]'}, + {"failure_label": "GOOD_ENTRY", "entry_bar": 1, "fold": "F2", "feature_triples_json": '[{"indicator_id":1,"period":10,"p1":0}]'}, + {"failure_label": "WRONG_DIRECTION", "entry_bar": 2, "fold": "F1", "feature_triples_json": '[{"indicator_id":1,"period":10,"p1":0}]'}, + {"failure_label": "WRONG_DIRECTION", "entry_bar": 3, "fold": "F2", "feature_triples_json": '[{"indicator_id":1,"period":10,"p1":0}]'}, + {"failure_label": "WRONG_DIRECTION", "entry_at": "not a bar", "feature_triples_json": '[]'}, + ]), context) + output = tmp_path / "output" + monkeypatch.setattr(sys, "argv", ["runner", "--failure-context", str(context), "--semantic-map", str(semantic), "--oracle-checkpoint-dir", str(checkpoints), "--engineering-map", str(engineering), "--output-dir", str(output)]) + runner.main() + result = json.loads((output / "cohort001_feature_failure_mining_v1.json").read_text()) + item = result["feature_failure_comparisons"][0] + assert item["median_difference_failure_minus_good"] == pytest.approx(7.0) + assert item["fold_consistency"] == 1.0 + assert result["skipped"]["unmappable_entry_bar"] == 1 + assert item["attribution"] == "ASSOCIATIVE_NOT_CAUSAL" diff --git a/tests/test_export_cohort_failure_context_v1.py b/tests/test_export_cohort_failure_context_v1.py new file mode 100644 index 0000000..cd3fa9c --- /dev/null +++ b/tests/test_export_cohort_failure_context_v1.py @@ -0,0 +1,41 @@ +from control_plane.trading_studio.management.commands.export_cohort_failure_context_v1 import Command + + +def test_failure_labels_are_explicit_and_precedence_is_deterministic(): + cases = [ + ({"net_pnl": -1}, {"return_1_bars_bps": 5}, "GOOD_SIGNAL_BAD_MONETIZATION"), + ({"net_pnl": 1}, {"winner_negative_first": True}, "RECOVERY_DEPENDENT"), + ({}, {"mae_bps": -25}, "HIGH_MAE_ENTRY"), + ({}, {"return_1_bars_bps": -5}, "WRONG_DIRECTION"), + ({}, {"return_1_bars_bps": 0, "return_5_bars_bps": 5}, "LATE_SIGNAL"), + ({"net_pnl": 1}, {"return_1_bars_bps": 5}, "GOOD_ENTRY"), + ({}, {}, "NO_EDGE"), + ] + + for ledger, deq, expected in cases: + assert Command._label(ledger, deq)[0] == expected + + +def test_rows_preserve_raw_ledger_deq_genome_context_and_rescue(): + payload = { + "cohort_id": "cohort", "dataset_version_id": "dataset", "dataset_sha256": "hash", + "strategies": [{ + "ordinal": 1, "membership_id": "member", "strategy_version_id": "strategy", + "runner_name": "runner", "genome": {"combo": "g"}, "feature_triples": [], + "folds": [{"fold": "F1", "scenarios": [{ + "qualification_run_id": "run", "scenario": "BASE", + "summary": {"rescue": {"recovered": True}}, + "deq_samples": [{"identity": "trade", "entry_bar": 2, "entry_execution_price": 10, + "take_profit_price": 11, "context": {"regime": "x"}, + "deq": {"return_1_bars_bps": 5}}], + }]}], + }], + } + + row = Command._rows(payload)[0] + + assert row["direction"] == "LONG" + assert '"return_1_bars_bps":5' in row["raw_deq_json"] + assert '"combo":"g"' in row["genome_json"] + assert '"regime":"x"' in row["context_json"] + assert '"recovered":true' in row["rescue_json"] diff --git a/tests/test_generate_historical_feature_semantic_domain_map_v1.py b/tests/test_generate_historical_feature_semantic_domain_map_v1.py new file mode 100644 index 0000000..05af4af --- /dev/null +++ b/tests/test_generate_historical_feature_semantic_domain_map_v1.py @@ -0,0 +1,92 @@ +from __future__ import annotations + +import json +import sys + +import numpy as np +import pytest + +pytest.importorskip("pyarrow") + +from scripts import generate_historical_feature_semantic_domain_map_v1 as generator + + +def engineering_rows() -> list[dict[str, object]]: + names = ["ma_fast", "cross_over", "regime_entropy", "pivot_high", "mystery"] + return [ + { + "indicator_id": index, + "period": 10, + "p1": float(index % 2), + "source_function": names[index % len(names)], + "source_expression": names[index % len(names)] + "(close)", + "engineering_family": "specialized" if index % len(names) == 4 else "moving_average", + } + for index in range(711) + ] + + +def test_generator_writes_711_semantic_rows_with_optional_evidence(tmp_path, monkeypatch): + import pyarrow as pa + import pyarrow.parquet as pq + rows = engineering_rows() + # The map may contain multiple parameterized primitives for one indicator. + rows[-1] = {**rows[-1], "indicator_id": 0, "period": 11, "p1": 0.0} + map_path = tmp_path / "engineering.json" + map_path.write_text(json.dumps({"artifact": "ENGINEERING_FAMILY_MAP_V1", "primitives": rows}), encoding="utf-8") + usage_path = tmp_path / "usage.parquet" + pq.write_table(pa.Table.from_pylist([ + {"entity_type": "indicator_id", "indicator_id": indicator_id, "usage_count": indicator_id + 1} + for indicator_id in sorted({int(row["indicator_id"]) for row in rows}) + ]), usage_path) + checkpoints = tmp_path / "checkpoints" + checkpoints.mkdir() + for row in rows: + values = np.array([1.0, np.nan]) if row["indicator_id"] else np.array([np.nan, np.nan]) + np.save( + checkpoints / f"hs22_{row['indicator_id']}_{row['period']}_{row['p1']:g}.npy", + values, + allow_pickle=False, + ) + output = tmp_path / "output" + monkeypatch.setattr(sys, "argv", ["generator", "--engineering-map", str(map_path), "--usage-parquet", str(usage_path), "--checkpoint-dir", str(checkpoints), "--output-dir", str(output)]) + + generator.main() + + result = pq.read_table(output / "historical_feature_semantic_domain_map_v1.parquet").to_pylist() + summary = json.loads((output / "historical_feature_semantic_domain_map_v1_summary.json").read_text()) + assert len(result) == 711 + assert result[0]["semantic_domain"] == "trend" + assert result[1]["semantic_type"] == "event" + assert result[2]["semantic_type"] == "state" + assert result[4]["semantic_mapping_provenance"] == "heuristic" + assert result[0]["usage_count"] == 1 + assert result[0]["usage_grain"] == "indicator_id_replicated_not_parameter_specific" + assert result[-1]["usage_count"] == 1 + assert result[-1]["usage_grain"] == "indicator_id_replicated_not_parameter_specific" + assert result[0]["finite_observation_count"] == 0 + assert result[1]["finite_observation_count"] == 1 + assert summary["counts"]["primitives"] == 711 + assert summary["usage_grain"] == "indicator_id_replicated_not_parameter_specific" + + +def test_usage_counts_requires_all_engineering_indicator_ids(tmp_path): + import pyarrow as pa + import pyarrow.parquet as pq + path = tmp_path / "usage.parquet" + pq.write_table(pa.Table.from_pylist([{"entity_type": "indicator_id", "indicator_id": 1, "usage_count": 1}]), path) + + with pytest.raises(ValueError, match="does not cover every"): + generator.usage_counts(path, {1, 2}) + + +def test_usage_counts_replicates_indicator_count_for_parameterized_primitives(tmp_path): + import pyarrow as pa + import pyarrow.parquet as pq + path = tmp_path / "usage.parquet" + pq.write_table(pa.Table.from_pylist([ + {"entity_type": "indicator_id", "indicator_id": 1, "usage_count": 7}, + {"entity_type": "other", "indicator_id": 1, "usage_count": 3}, + ]), path) + + assert generator.usage_counts(path, {1}) == {1: 7} diff --git a/tests/test_gpu_batch01_v1_1_runner.py b/tests/test_gpu_batch01_v1_1_runner.py new file mode 100644 index 0000000..0788a42 --- /dev/null +++ b/tests/test_gpu_batch01_v1_1_runner.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +import numpy as np +import pytest + +pytest.importorskip("torch") +from gpu_batch01_v1_1_runner import read_ohlcv, role_semantics, write_ohlcv_csv + + +def test_runner_csv_round_trip_and_explicit_missing_role_semantics(tmp_path): + path = tmp_path / "ohlcv.csv" + expected = { + "close": np.array([100.0, 101.0]), + "high": np.array([101.0, 102.0]), + "low": np.array([99.0, 100.0]), + "volume": np.array([1000.0, 1001.0]), + } + write_ohlcv_csv(path, expected) + actual = read_ohlcv(path) + assert all(np.array_equal(actual[name], values) for name, values in expected.items()) + semantics = role_semantics(None) + assert semantics["status"] == "not_reconstructable" + assert "no strategy-role assignment" in semantics["reason"] diff --git a/tests/test_gpu_batch01_v1_2_runner.py b/tests/test_gpu_batch01_v1_2_runner.py new file mode 100644 index 0000000..1e6b866 --- /dev/null +++ b/tests/test_gpu_batch01_v1_2_runner.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +import pytest + +pytest.importorskip("torch") + +from gpu_batch01_v1_2_runner import DEFAULT_ATR_LIMITS, _atr_limits + + +def test_v1_2_uses_fixed_atr_defaults_covering_documented_drift(): + assert _atr_limits(None) == DEFAULT_ATR_LIMITS + assert DEFAULT_ATR_LIMITS["max_absolute_error"] >= 1.4210854715202004e-14 + + +@pytest.mark.parametrize("limits", [ + {"max_absolute_error": -1.0, "mae": 0.0}, + {"max_absolute_error": float("nan"), "mae": 0.0}, + {"max_absolute_error": 0.0}, +]) +def test_v1_2_rejects_invalid_atr_limits(limits): + with pytest.raises(ValueError, match="finite, non-negative"): + _atr_limits(limits) diff --git a/tests/test_gpu_feature_parity_contract_v1_1.py b/tests/test_gpu_feature_parity_contract_v1_1.py new file mode 100644 index 0000000..a311e38 --- /dev/null +++ b/tests/test_gpu_feature_parity_contract_v1_1.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import csv + +import numpy as np +import pytest + +from gpu_feature_parity_contract_v1_1 import ( + ARTIFACT, + FrozenContractError, + SCENARIOS, + calibrate, + compare_stateful_trace, + corpus_manifest, + cpu_psar_trace, + cpu_supertrend_trace, + deterministic_adversarial_ohlcv, + freeze_contract, + historical_cpu_oracle, + nan_gap_semantics_manifest, + validate_frozen_contract, +) + + +def _evidence(): + ohlcv = deterministic_adversarial_ohlcv(length=96, seed=7) + requests = [{"request_id": "super", "indicator_id": 19, "period": 8, "p1": 2.0}] + oracle = {"super": cpu_supertrend_trace(ohlcv["close"], ohlcv["high"], ohlcv["low"], 8, 2.0)["output"]} + corpus = corpus_manifest({"adversarial": ohlcv}, {"generator": "deterministic_adversarial_ohlcv", "seed": 7}) + calibration = calibrate(oracle, oracle, requests, ohlcv["close"], corpus) + return ohlcv, oracle, freeze_contract(calibration) + + +def test_adversarial_generator_is_deterministic_and_manifest_has_provenance(): + first = deterministic_adversarial_ohlcv(length=96, seed=3) + second = deterministic_adversarial_ohlcv(length=96, seed=3) + assert all(np.array_equal(first[key], second[key]) for key in first) + manifest = corpus_manifest({"edge": first}, {"source": "test"}) + assert manifest["scenario_categories"] == list(SCENARIOS) + assert manifest["provenance"]["source"] == "test" + assert {"constant", "nearly_constant_tiny_variance", "high_variance", "alternating", "uptrend", "downtrend", "flat_breakout", "breakout_flat", "threshold_equality", "threshold_one_ulp_above", "threshold_one_ulp_below", "repeated_equality", "zero_range", "tiny_range", "abrupt_atr", "warmup"} == set(SCENARIOS) + + +def test_frozen_contract_validates_and_rejects_injected_numeric_nan_and_decision_violations(): + ohlcv, oracle, contract = _evidence() + assert contract["artifact"] == ARTIFACT + assert {"max_ulp", "p50_ulp", "p95_ulp", "p99_ulp", "p99_9_ulp"}.issubset(contract["feature_limits"]["super"]) + assert {"max_ulp", "p50_ulp", "p95_ulp", "p99_ulp", "p99_9_ulp"}.issubset(contract["family_limits"]["supertrend"]) + assert validate_frozen_contract(contract, oracle, oracle, ohlcv["close"])["passed"] + numeric = {"super": oracle["super"].copy()}; numeric["super"][-1] += 1.0 + assert not validate_frozen_contract(contract, numeric, oracle, ohlcv["close"])["passed"] + nan = {"super": oracle["super"].copy()}; nan["super"][-1] = np.nan + assert not validate_frozen_contract(contract, nan, oracle, ohlcv["close"])["passed"] + decision = {"super": oracle["super"].copy()}; index = np.flatnonzero(np.isfinite(decision["super"]))[-1]; decision["super"][index] = ohlcv["close"][index] + 1.0 + assert not validate_frozen_contract(contract, decision, oracle, ohlcv["close"])["passed"] + + +def test_frozen_contract_rejects_a_deliberate_ulp_breach(): + ohlcv, oracle, contract = _evidence() + breach = {"super": oracle["super"].copy()} + index = np.flatnonzero(np.isfinite(breach["super"]))[-1] + breach["super"][index] = np.nextafter(breach["super"][index], np.inf) + result = validate_frozen_contract(contract, breach, oracle, ohlcv["close"]) + assert not result["passed"] + assert result["records"][0]["max_ulp"] > contract["feature_limits"]["super"]["max_ulp"] + assert "max_ulp" in result["records"][0]["numeric_violations"] + + +def test_validation_refuses_unfrozen_or_derived_contracts(): + ohlcv, oracle, contract = _evidence() + contract["status"] = "accepted" + with pytest.raises(FrozenContractError): + validate_frozen_contract(contract, oracle, oracle, ohlcv["close"]) + + +def test_trace_comparer_requires_every_discrete_state_key_and_value(): + ohlcv = deterministic_adversarial_ohlcv(length=96) + trace = cpu_psar_trace(ohlcv["close"], ohlcv["high"], ohlcv["low"], .02) + assert compare_stateful_trace(trace, trace, trace_type="psar")["exact"] + changed = dict(trace); changed["reversal"] = trace["reversal"].copy(); changed["reversal"][-1] = ~changed["reversal"][-1] + assert compare_stateful_trace(trace, changed, trace_type="psar")["mismatched_keys"] == ["reversal"] + assert not compare_stateful_trace(trace, {"output": trace["output"]}, trace_type="psar")["exact"] + + +def test_decisions_do_not_cross_nan_gaps(): + close = np.array([1., 2., 3.]) + oracle = {"super": np.array([0., np.nan, 4.])} + calibration = calibrate(oracle, oracle, [{"request_id": "super", "indicator_id": 19}], close, corpus_manifest({"edge": deterministic_adversarial_ohlcv(length=96)}, {})) + contract = freeze_contract(calibration) + assert validate_frozen_contract(contract, oracle, oracle, close)["passed"] + + +def test_historical_cpu_oracle_accepts_arbitrary_csv(tmp_path): + ohlcv = deterministic_adversarial_ohlcv(length=96) + path = tmp_path / "input.csv" + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=["close", "high", "low", "volume"]); writer.writeheader() + writer.writerows({key: ohlcv[key][index] for key in ohlcv} for index in range(96)) + result = historical_cpu_oracle(path, [{"request_id": "psar", "indicator_id": 28, "period": 1, "p1": .02}]) + assert result["outputs"]["psar"].shape == (96,) + assert result["source"]["evaluator"] == "evaluate_band_channel" + + +def test_nan_gap_semantics_are_manifested_and_rejected_before_historical_evaluation(tmp_path): + manifest = nan_gap_semantics_manifest() + assert manifest["historical_input_policy"] == "reject_non_finite_ohlcv" + assert manifest["internal_nan_gap_semantics"] == "undefined" + path = tmp_path / "nan_gap.csv" + path.write_text("close,high,low,volume\n100,101,99,1000\nnan,101,99,1000\n", encoding="utf-8") + with pytest.raises(ValueError, match="NaN/gap semantics are undefined"): + historical_cpu_oracle(path, [{"request_id": "psar", "indicator_id": 28, "period": 1, "p1": .02}]) diff --git a/tests/test_gpu_feature_parity_contract_v1_2.py b/tests/test_gpu_feature_parity_contract_v1_2.py new file mode 100644 index 0000000..343fe06 --- /dev/null +++ b/tests/test_gpu_feature_parity_contract_v1_2.py @@ -0,0 +1,122 @@ +from __future__ import annotations + +import builtins + +import numpy as np +import pytest + +from gpu_feature_parity_contract_v1_1 import deterministic_adversarial_ohlcv +from gpu_feature_parity_contract_v1_2 import ( + ARTIFACT, + CALIBRATION_ARTIFACT, + ROLE_C_UNVERIFIABLE, + UNVERIFIABLE, + calibrate_supertrend, + calibrate_output, + cpu_supertrend_trace, + freeze_contract, + role_surface, + validate_all_frozen_contract, + validate_frozen_contract, +) + + +def _traces(): + ohlcv = deterministic_adversarial_ohlcv(length=96, seed=19) + expected = cpu_supertrend_trace(ohlcv["close"], ohlcv["high"], ohlcv["low"], 8, 3.0) + actual = {name: value.copy() for name, value in expected.items()} + return expected, actual + + +def test_ordered_on_device_seed_matches_historical_left_to_right_seed(): + torch = pytest.importorskip("torch") + from gpu_feature_engine_v1_2 import rma_ordered_seed + + values = torch.tensor([1e16, 1.0, -1e16, 3.0], dtype=torch.float64) + result = rma_ordered_seed(values, 4) + total = 0.0 + for value in values.numpy(): + total += value + assert result[-1].item() == total / 4 + + +def test_v1_2_requires_exact_branch_state_before_atr_bounds(): + expected, actual = _traces() + calibration = {"artifact": CALIBRATION_ARTIFACT, "records": [calibrate_supertrend("super", expected, actual)]} + contract = freeze_contract(calibration) + assert contract["artifact"] == ARTIFACT + assert validate_frozen_contract(contract, {"super": (expected, actual)})["passed"] + changed = dict(actual) + changed["active_long"] = actual["active_long"].copy() + changed["active_long"][-1] = ~changed["active_long"][-1] + result = validate_frozen_contract(contract, {"super": (expected, changed)}) + assert not result["passed"] + assert result["records"][0]["atr"] is None + + +@pytest.mark.parametrize("mutation", ["shape", "nan_mask"]) +def test_v1_2_requires_atr_structure_before_measuring_bounds(mutation): + expected, actual = _traces() + calibration = {"artifact": CALIBRATION_ARTIFACT, "records": [calibrate_supertrend("super", expected, actual)]} + contract = freeze_contract(calibration) + changed = dict(actual) + changed["atr"] = actual["atr"][:-1].copy() if mutation == "shape" else actual["atr"].copy() + if mutation == "nan_mask": + changed["atr"][-1] = np.nan + result = validate_frozen_contract(contract, {"super": (expected, changed)}) + assert not result["passed"] + assert result["records"][0]["atr"] is None + assert not result["records"][0]["trace"]["atr_structure_exact"] + + +def test_unrecorded_batch01_roles_are_unverifiable_and_not_parity_blockers(): + surface = role_surface([ + {"request_id": "known", "indicator_id": 19, "period": 10, "p1": 2.0}, + {"request_id": "unknown", "indicator_id": 17, "period": 999, "p1": 2.0}, + ]) + unknown = next(item for item in surface["records"] if item["request_id"] == "unknown") + assert unknown["classification"] == ROLE_C_UNVERIFIABLE + assert unknown["role_status"] == UNVERIFIABLE + assert not unknown["parity_blocker"] + + +def test_role_surface_is_unverifiable_when_recovered_registry_is_absent(monkeypatch): + original_import = builtins.__import__ + + def missing_registry(name, *args, **kwargs): + if name == "control_plane.trading_studio.indicators.registry": + raise ModuleNotFoundError(name) + return original_import(name, *args, **kwargs) + + monkeypatch.setattr(builtins, "__import__", missing_registry) + surface = role_surface([{"request_id": "known", "indicator_id": 19, "period": 10, "p1": 2.0}]) + + record = surface["records"][0] + assert record["role_status"] == UNVERIFIABLE + assert record["reason"] == "control_plane.trading_studio.indicators.registry is unavailable" + assert not record["parity_blocker"] + + +def test_full_batch_contract_keeps_exact_and_calibration_bounded_classes_separate(): + expected, actual = _traces() + values = np.array([np.nan, 2.0, 3.0]) + calibration = { + "artifact": CALIBRATION_ARTIFACT, + "records": [ + calibrate_supertrend("super", expected, actual, atr_limits={"max_absolute_error": 0.0, "mae": 0.0}), + calibrate_output("donch", 20, values, values.copy()), + calibrate_output("psar", 28, values, values.copy()), + calibrate_output("bb", 17, values, values.copy()), + calibrate_output("kc", 23, values, values.copy()), + ], + } + contract = freeze_contract(calibration) + result = validate_all_frozen_contract( + contract, + {"super": expected["output"], "donch": values, "psar": values, "bb": values, "kc": values}, + {"super": actual["output"], "donch": values.copy(), "psar": values.copy(), "bb": values.copy(), "kc": values.copy()}, + {"super": (expected, actual)}, + ) + assert result["passed"] + assert contract["feature_limits"]["donch"]["role"] == "A_EXACT" + assert contract["feature_limits"]["bb"]["role"] == "B_BOUNDED" diff --git a/tests/test_gpu_supertrend_v1_2_diagnostic.py b/tests/test_gpu_supertrend_v1_2_diagnostic.py new file mode 100644 index 0000000..7dc1af2 --- /dev/null +++ b/tests/test_gpu_supertrend_v1_2_diagnostic.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +import pytest + +torch = pytest.importorskip("torch") + +import json + +from gpu_feature_parity_contract_v1_1 import deterministic_adversarial_ohlcv # noqa: E402 +from gpu_feature_engine_v1_2 import supertrend_trace # noqa: E402 +from gpu_feature_parity_contract_v1_2 import cpu_supertrend_trace # noqa: E402 +from gpu_supertrend_v1_2_diagnostic import diagnose # noqa: E402 + + +def test_period_10_semantic_diagnostic_has_serializable_cpu_gpu_trace_statistics(): + ohlcv = deterministic_adversarial_ohlcv(length=96, seed=11) + report = diagnose(ohlcv, device=torch.device("cpu")) + assert json.dumps(report, allow_nan=False) + assert [record["multiplier"] for record in report["records"]] == [2.0, 3.0, 4.0] + for record in report["records"]: + assert set(record["numeric"]) == {"output", "true_range", "atr", "basic_upper", "basic_lower", "upper", "lower"} + assert record["numeric"]["atr"]["first_divergence"] is None + assert record["direction"]["exact"] + assert record["transitions"]["direction_transition"]["exact"] + assert all(item["exact"] for item in record["branch_predicates"].values()) + + +def test_cpu_trace_exposes_the_complete_gpu_trace_surface(): + ohlcv = deterministic_adversarial_ohlcv(length=96, seed=11) + cpu = cpu_supertrend_trace(ohlcv["close"], ohlcv["high"], ohlcv["low"], 10, 3.0) + gpu = supertrend_trace( + torch.as_tensor(ohlcv["close"], dtype=torch.float64), + torch.as_tensor(ohlcv["high"], dtype=torch.float64), + torch.as_tensor(ohlcv["low"], dtype=torch.float64), + 10, + 3.0, + ) + assert set(cpu) == set(gpu) diff --git a/tests/test_hyperscalper_feature_gap_analysis_v1.py b/tests/test_hyperscalper_feature_gap_analysis_v1.py new file mode 100644 index 0000000..9d7105c --- /dev/null +++ b/tests/test_hyperscalper_feature_gap_analysis_v1.py @@ -0,0 +1,367 @@ +from __future__ import annotations + +import json +import sys +import time + +import numpy as np +import pytest + +import hyperscalper_feature_gap_analysis_v1 as runner + +pytestmark = pytest.mark.django_db + + +def test_read_only_runner_writes_711_map_and_honest_deq_blocker(tmp_path, monkeypatch): + rows = [ + {"request_id": f"r{index}", "indicator_id": index, "period": 10, "p1": 0.0} + for index in range(711) + ] + request = tmp_path / "request.json" + request.write_text(json.dumps({"requests": rows}), encoding="utf-8") + checkpoint = tmp_path / "checkpoint.npz" + values = {f"r{index}": np.arange(8, dtype=float) + index for index in range(711)} + values["r710"] = np.full(8, np.nan) + np.savez(checkpoint, **values) + manifest = tmp_path / "manifest.json" + manifest.write_text( + json.dumps({"coverage": {"feature_versions": [f"r{index}" for index in range(711)]}}), + encoding="utf-8", + ) + for name in ("usage.parquet", "lineage.parquet"): + (tmp_path / name).write_bytes(b"not-read-without-pyarrow") + output = tmp_path / "out" + monkeypatch.setattr( + sys, + "argv", + [ + "runner", + "--oracle-request", + str(request), + "--oracle-checkpoint", + str(checkpoint), + "--acceptance-manifest", + str(manifest), + "--historical-usage", + str(tmp_path / "usage.parquet"), + "--lineage", + str(tmp_path / "lineage.parquet"), + "--output-dir", + str(output), + "--sample-rows", + "8", + ], + ) + runner.main() + result = json.loads((output / "hyperscalper_feature_gap_analysis_v1.json").read_text()) + assert result["read_only"] is True + assert len(result["information_map"]["features"]) == 711 + assert result["information_map"]["features"][-1]["redundancy_status"] == ( + "unusable_no_finite_observations" + ) + assert result["redundancy"]["map"]["format"] == "parquet" + assert result["decision_equivalence"]["status"] == "BLOCKED" + + +def test_checkpoint_directory_maps_hs22_filenames_to_engineering_primitives(tmp_path): + rows = [ + {"indicator_id": index, "period": index + 10, "p1": float(index % 3)} + for index in range(711) + ] + checkpoint_dir = tmp_path / "checkpoints" + checkpoint_dir.mkdir() + for row in reversed(rows): + np.save( + checkpoint_dir + / f"hs22_{row['indicator_id']}_{row['period']}_{row['p1']:g}.npy", + np.array([row["indicator_id"], row["period"]], dtype=float), + allow_pickle=False, + ) + + matrix, names = runner.checkpoint_directory_columns( + checkpoint_dir, + [ + { + **row, + "feature_key": f"{row['indicator_id']}:{row['period']}:{row['p1']:g}", + "request_id": None, + } + for row in rows + ], + ) + + assert matrix.shape == (2, 711) + assert np.array_equal(matrix[:, 0], [0.0, 10.0]) + assert np.array_equal(matrix[:, -1], [710.0, 720.0]) + assert names[0] == "hs22_0_10_0.npy" + + +def test_pairwise_redundancy_uses_staggered_finite_observations_for_clustering(): + sample = np.array( + [ + [0.0, 0.0, np.nan, np.nan], + [1.0, 2.0, np.nan, np.nan], + [2.0, 4.0, np.nan, np.nan], + [np.nan, np.nan, 5.0, np.nan], + [np.nan, np.nan, 6.0, np.nan], + ] + ) + + pearson, spearman, counts = runner.pairwise_redundancy(sample, min_pair_samples=3) + + assert counts[0, 1] == 3 + assert pearson[0, 1] == pytest.approx(1.0) + assert spearman[0, 1] == pytest.approx(1.0) + assert np.isnan(pearson[0, 2]) + assert runner.clusters(pearson) == [[0, 1]] + + +def test_pairwise_redundancy_precomputes_ranks_once_per_column_with_bounded_runtime(monkeypatch): + sample = np.arange(512 * 64, dtype=float).reshape(512, 64) + sample[::17, ::7] = np.nan + calls = 0 + original = runner.rank_finite_columns + + def counted_ranks(values): + nonlocal calls + calls += 1 + return original(values) + + monkeypatch.setattr(runner, "rank_finite_columns", counted_ranks) + started = time.perf_counter() + pearson, spearman, counts = runner.pairwise_redundancy(sample, min_pair_samples=2) + + assert time.perf_counter() - started < 3.0 + assert calls == 1 + assert counts[0, 1] > 2 + assert pearson[0, 1] == pytest.approx(1.0) + assert spearman[0, 1] == pytest.approx(1.0, abs=1e-5) + + +def test_type_aware_redundancy_uses_agreement_and_jaccard_for_detection_outputs(): + sample = np.array( + [ + [0.0, 1.0, 1.0, 0.0], + [1.0, 1.0, 1.0, 1.0], + [1.0, 0.0, 0.0, 1.0], + [0.0, 0.0, 0.0, 0.0], + ] + ) + + pearson, spearman, agreement, jaccard, counts = runner.type_aware_redundancy( + sample, ["state", "state", "event", "event"], min_pair_samples=2 + ) + primary = runner.primary_redundancy_matrix( + pearson, agreement, jaccard, ["state", "state", "event", "event"] + ) + + assert counts[0, 1] == 4 + assert agreement[0, 1] == pytest.approx(0.5) + assert jaccard[2, 3] == pytest.approx(1 / 3) + assert np.isnan(pearson[0, 1]) + assert primary[0, 1] == pytest.approx(0.5) + assert primary[2, 3] == pytest.approx(1 / 3) + + +def test_semantic_types_derives_type_and_domain_from_primitive_map(tmp_path): + semantic_map = tmp_path / "semantic-map.json" + semantic_map.write_text( + json.dumps( + { + "primitives": [ + {"indicator_id": 1, "period": 10, "p1": 0, "output_type": "boolean", "engineering_family": "regime"}, + {"indicator_id": 2, "period": 20, "p1": 1, "output_type": "event", "domain": "trigger"}, + ] + } + ), + encoding="utf-8", + ) + rows = [ + {"feature_key": "1:10:0", "indicator_id": 1, "period": 10, "p1": 0.0}, + {"feature_key": "2:20:1", "indicator_id": 2, "period": 20, "p1": 1.0}, + ] + + types, blocker = runner.semantic_types(semantic_map, rows) + + assert blocker is None + assert types == [ + {"output_type": "state", "domain": "regime"}, + {"output_type": "event", "domain": "trigger"}, + ] + + +def test_lineage_combo_rejects_incomplete_data(): + with pytest.raises(ValueError, match="at least five triples"): + runner.lineage_combo([1, 10, 0.0]) + with pytest.raises(ValueError, match="at least five triples"): + runner.lineage_combo([[1, 10, 0.0]] * 5) + + +def test_lineage_combo_reads_only_the_five_primitives_from_real_flat_encoding(): + # Flat lineage rows append tp, sl, confirmation bounds, and volume threshold. + combo = [ + 17, 20, 2.0, + 31, 14, 0.0, + 47, 9, 1.5, + 62, 30, 0.0, + 70, 20, 0.0, + 0.015, 0.01, 5, 20, 5, 20, 75, + ] + + assert runner.lineage_combo(json.dumps(combo)) == ( + (17, 20, 2.0), + (31, 14, 0.0), + (47, 9, 1.5), + (62, 30, 0.0), + (70, 20, 0.0), + ) + + +def test_lineage_usage_parses_combo_json_role_triples(tmp_path): + pa = pytest.importorskip("pyarrow") + pq = pytest.importorskip("pyarrow.parquet") + lineage = tmp_path / "lineage.parquet" + combo = [1, 10, 0.0, 2, 11, 1.5, 1, 10, 0.0, 4, 13, 2.0, 5, 14, 3.0] + pq.write_table( + pa.Table.from_pylist( + [ + {"strategy_id": 100, "combo_json": json.dumps(combo)}, + {"strategy_id": 101, "combo_json": json.dumps(combo + [0.01, 0.02, 3, 6, 3, 6, 75])}, + ] + ), + lineage, + ) + + counts, evidence, blocker = runner.lineage_usage(lineage) + + assert blocker is None + assert counts == {"1:10:0": 4, "2:11:1.5": 2, "4:13:2": 2, "5:14:3": 2} + assert evidence["role_counts"] == { + "confirm": 2, + "signal": 2, + "trend": 2, + "trigger": 2, + "vol": 2, + } + assert evidence["strategy_ids_observed"] == 2 + assert evidence["combo_counts"][0]["usage_count"] == 2 + + +def test_cohort_deq_evidence_aggregates_exported_ledger_associatively(tmp_path): + deq_path = tmp_path / "cohort-deq.json" + sample = { + "deq": { + "return_1_bars_bps": 10.0, + "return_2_bars_bps": -5.0, + "return_3_bars_bps": None, + "return_5_bars_bps": 0.0, + "return_10_bars_bps": 20.0, + "mfe_bps": 25.0, + "mae_bps": -4.0, + "time_to_positive_bars": 2, + } + } + deq_path.write_text( + json.dumps( + { + "contract": "Cohort001-DEQ-ledger-export-v1", + "strategies": [ + { + "strategy_version_id": "strategy-a", + "feature_triples": [ + {"indicator_id": 7, "period": 10, "p1": 1.0}, + {"indicator_id": 7, "period": 20, "p1": 2.0}, + ], + "folds": [ + { + "fold": "Fold 1", + "scenarios": [ + {"scenario": "BASE", "deq_samples": [sample]}, + {"scenario": "STRESS", "deq_samples": [sample]}, + ], + } + ], + } + ], + } + ), + encoding="utf-8", + ) + + evidence, parquet_rows, blocker = runner.cohort_deq_evidence(deq_path) + + assert blocker is None + assert evidence["status"] == "AVAILABLE" + assert evidence["attribution"] == "ASSOCIATIVE_NOT_CAUSAL" + assert evidence["strategy_summaries"][0]["trade_count"] == 2 + assert evidence["strategy_summaries"][0]["scenario_coverage"] == ["BASE", "STRESS"] + returns = evidence["feature_summaries"][0]["returns"] + assert returns["return_1_bars_bps"]["mean"] == 10.0 + assert returns["return_2_bars_bps"]["directional_incorrect_count"] == 2 + assert evidence["family_summaries"][0]["trade_count"] == 4 + assert {row["entity_type"] for row in parquet_rows} == {"strategy", "feature", "family"} + + +def test_cohort_deq_evidence_uses_genome_combo_when_exported_triples_are_null(tmp_path): + deq_path = tmp_path / "cohort-deq.json" + deq_path.write_text( + json.dumps( + { + "contract": "Cohort001-DEQ-ledger-export-v1", + "strategies": [ + { + "strategy_version_id": "strategy-a", + "feature_triples": None, + "genome": { + "combo": [ + 7, 10, 1.0, + 8, 11, 2.0, + 9, 12, 3.0, + 10, 13, 4.0, + 11, 14, 5.0, + 0.01, 0.02, 3, 6, 3, 6, 75, + ] + }, + "folds": [ + { + "fold": "Fold 1", + "scenarios": [ + { + "scenario": "BASE", + "deq_samples": [{"deq": {"return_1_bars_bps": 10.0}}], + } + ], + } + ], + } + ], + } + ), + encoding="utf-8", + ) + + evidence, _, blocker = runner.cohort_deq_evidence(deq_path) + + assert blocker is None + assert [item["feature_key"] for item in evidence["feature_summaries"]] == [ + "10:13:4", + "11:14:5", + "7:10:1", + "8:11:2", + "9:12:3", + ] + + +def test_deq_strategy_feature_keys_falls_back_when_exported_triples_are_invalid(): + strategy = { + "feature_triples": [{"indicator_id": "not-an-id"}], + "genome": {"combo": [7, 10, 1.0, 8, 11, 2.0, 9, 12, 3.0, 10, 13, 4.0, 11, 14, 5.0]}, + } + + assert runner.deq_strategy_feature_keys(strategy) == [ + ("7:10:1", "7"), + ("8:11:2", "8"), + ("9:12:3", "9"), + ("10:13:4", "10"), + ("11:14:5", "11"), + ] diff --git a/tests/test_hyperscalper_wave1_feature_synthesis_v1.py b/tests/test_hyperscalper_wave1_feature_synthesis_v1.py new file mode 100644 index 0000000..7d94252 --- /dev/null +++ b/tests/test_hyperscalper_wave1_feature_synthesis_v1.py @@ -0,0 +1,60 @@ +from __future__ import annotations + +import json +import sys + +import pytest + +import hyperscalper_wave1_feature_synthesis_v1 as synthesis + +pytestmark = pytest.mark.django_db + + +def test_synthesis_requires_explicit_ohlcv_and_reports_missing_failure_artifact( + tmp_path, monkeypatch +): + features = [ + { + "indicator_id": index, + "period": 10, + "p1": 0, + "domain": "trend", + "output_type": "continuous", + "input_scope": "OHLCV_ONLY", + } + for index in range(20) + ] + [{"indicator_id": 99, "period": 10, "p1": 0, "input_scope": "EXTERNAL"}] + semantic = tmp_path / "semantic.json" + semantic.write_text(json.dumps({"features": features}), encoding="utf-8") + redundancy = tmp_path / "redundancy.json" + redundancy.write_text(json.dumps({"clusters": []}), encoding="utf-8") + deq = tmp_path / "deq.json" + deq.write_text(json.dumps({"feature_summaries": []}), encoding="utf-8") + gap = tmp_path / "gap.json" + gap.write_text(json.dumps({"information_map": {"features": []}}), encoding="utf-8") + output = tmp_path / "out" + monkeypatch.setattr( + sys, + "argv", + [ + "runner", + "--semantic-summary", + str(semantic), + "--redundancy-clusters", + str(redundancy), + "--deq-summary", + str(deq), + "--lineage-gap-analysis", + str(gap), + "--output-dir", + str(output), + ], + ) + + synthesis.main() + + result = json.loads((output / "new_feature_candidate_registry_v1.json").read_text()) + assert len(result["candidates"]) == 20 + assert result["status"] == "BLOCKED" + assert result["sources"]["failure_mining"]["status"] == "UNAVAILABLE" + assert all(item["input_scope"] == "OHLCV_ONLY" for item in result["candidates"]) diff --git a/tests/test_validate_historical_oracle_acceptance.py b/tests/test_validate_historical_oracle_acceptance.py new file mode 100644 index 0000000..64d9db0 --- /dev/null +++ b/tests/test_validate_historical_oracle_acceptance.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import json + +import numpy as np +import pytest + +from validate_historical_oracle_acceptance import array_digest, validate + +pytestmark = pytest.mark.django_db + + +def test_validate_uses_runner_semantic_array_digest(tmp_path): + output_dir = tmp_path / "run" + checkpoints = output_dir / "checkpoints" + checkpoints.mkdir(parents=True) + values = np.array([1.0, 2.0], dtype=np.float64) + status = { + "artifact": "HISTORICAL_FEATURE_ORACLE_FULL_STATUS_V1", + "status": "PASS", + "completed_features": 711, + "checkpoints": [ + { + "request_id": f"feature-{index}", + "path": f"feature-{index}.npy", + "dtype": " bytes: + return json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False).encode("utf-8") + + +def array_digest(values: np.ndarray) -> str: + values = np.ascontiguousarray(values) + return hashlib.sha256( + canonical({"dtype": values.dtype.str, "shape": values.shape}) + b"\0" + values.tobytes() + ).hexdigest() + + +def validate(output_dir: Path) -> dict[str, Any]: + status_path = output_dir / "final_status.json" + status = json.loads(status_path.read_text(encoding="utf-8")) + checkpoints = status.get("checkpoints") + if status.get("artifact") != "HISTORICAL_FEATURE_ORACLE_FULL_STATUS_V1": + raise ValueError("invalid full-status artifact") + if status.get("status") != "PASS" or status.get("completed_features") != 711: + raise ValueError("full-status artifact does not attest to 711 passing features") + if not isinstance(checkpoints, list) or len(checkpoints) != 711: + raise ValueError("full-status artifact must contain exactly 711 checkpoints") + request_ids = set() + for checkpoint in checkpoints: + request_id = checkpoint.get("request_id") + if not isinstance(request_id, str) or request_id in request_ids: + raise ValueError("checkpoint request IDs must be unique strings") + request_ids.add(request_id) + path = output_dir / "checkpoints" / str(checkpoint.get("path", "")) + if path.name != f"{request_id}.npy" or not path.is_file(): + raise ValueError(f"missing checkpoint: {request_id}") + values = np.load(path, allow_pickle=False) + if ( + values.dtype.str != checkpoint.get("dtype") + or list(values.shape) != checkpoint.get("shape") + ): + raise ValueError(f"checkpoint dtype or shape mismatch: {request_id}") + if array_digest(values) != checkpoint.get("sha256"): + raise ValueError(f"checkpoint semantic digest mismatch: {request_id}") + return { + "artifact": "HISTORICAL_FEATURE_ORACLE_ACCEPTANCE_VALIDATION_V1", + "status": "PASS", + "completed_features": len(checkpoints), + "status_sha256": hashlib.sha256(status_path.read_bytes()).hexdigest(), + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--manifest", type=Path) + args = parser.parse_args() + manifest = validate(args.output_dir) + if args.manifest: + args.manifest.write_bytes(canonical(manifest) + b"\n") + os.chmod(args.manifest, 0o444) + print(canonical(manifest).decode("utf-8")) + + +if __name__ == "__main__": + main()