File size: 9,911 Bytes
5995ef5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | """Layer 3 – Step 8: Assemble benchmark artifacts from the processed panel.
Reads ``data/processed/{granularity}/panel.parquet`` and produces:
data/benchmark/{granularity}/panel_train.parquet
data/benchmark/{granularity}/panel_test.parquet
data/benchmark/{granularity}/panel_full.csv (CSV compatibility)
data/benchmark/{granularity}/task_definition.json
data/benchmark/{granularity}/filing_corpus.parquet
data/benchmark/{granularity}/metadata.json
Does NOT re-process raw data. All heavy lifting happened in
``preprocess.py`` (Layer 2).
"""
from __future__ import annotations
import json
import logging
from pathlib import Path
import numpy as np
import pandas as pd
from . import config
logger = logging.getLogger(__name__)
# ------------------------------------------------------------------
# Filing corpus
# ------------------------------------------------------------------
def _build_filing_corpus() -> pd.DataFrame:
"""Build a filing corpus index from ``data/filings/{TICKER}/*.md``.
Stores only **metadata** (ticker, filing_type, filing_date, filing_path)
-- NOT the full text -- to avoid OOM with thousands of large filings.
Text is loaded on-demand by ``benchmark_loader.py`` using ``filing_path``.
Columns: ticker, filing_type, filing_date, filing_path, text_length.
"""
import re as _re
rows: list[dict] = []
if not config.FILINGS_DIR.is_dir():
logger.warning("Filings directory does not exist: %s", config.FILINGS_DIR)
return pd.DataFrame(columns=["ticker", "filing_type", "filing_date", "filing_path", "text_length"])
for ticker_dir in sorted(config.FILINGS_DIR.iterdir()):
if not ticker_dir.is_dir():
continue
ticker = ticker_dir.name
for md_file in sorted(ticker_dir.glob("*.md")):
ftype = "10-K" if "10-K" in md_file.name else "10-Q" if "10-Q" in md_file.name else "8-K" if "8-K" in md_file.name else "other"
match = _re.search(r"(\d{4}-\d{2}-\d{2})", md_file.name)
fdate = match.group(1) if match else None
# Only measure length (not load entire text into memory)
try:
text_len = md_file.stat().st_size
except Exception:
text_len = 0
rows.append({
"ticker": ticker,
"filing_type": ftype,
"filing_date": fdate,
"filing_path": str(md_file.relative_to(config.DATA_DIR)),
"text_length": text_len,
})
df = pd.DataFrame(rows)
if not df.empty and "filing_date" in df.columns:
df["filing_date"] = pd.to_datetime(df["filing_date"], errors="coerce")
logger.info("Filing corpus index: %d documents across %d tickers.",
len(df), df["ticker"].nunique() if not df.empty else 0)
return df
# ------------------------------------------------------------------
# Task definition
# ------------------------------------------------------------------
def _build_task_definition(panel: pd.DataFrame, granularity: str) -> dict:
"""Create the formal forecasting-task contract."""
# Read column roles from the processed output
col_roles_path = config.DATA_DIR / "processed" / granularity / "columns.json"
if col_roles_path.exists():
column_roles = json.loads(col_roles_path.read_text())
else:
column_roles = {}
return {
"benchmark_name": "MacroLens",
"version": "1.0",
"granularity": granularity,
"targets": {
"primary": "close",
"secondary": "volume",
},
"horizons": config.get_horizons(granularity),
"lookback_windows": config.get_lookback_windows(granularity),
"column_roles": column_roles,
"context_taxonomy": {
"historical": "10-K / 10-Q filing text (nearest filing as-of each date)",
"covariate": "FRED / EIA macro indicators (exogenous_macro + exogenous_commodity)",
"causal": "Fundamental ratios derived from statements + price (exogenous_fundamental)",
"future_scenario": "Natural experiment events detected from macro data (scenarios.parquet)",
"intemporal": "Sector / industry knowledge (metadata columns)",
},
"evaluation": {
"metrics": ["MSE", "MAE", "RMSE", "directional_accuracy"],
"baseline": "naive_last_value",
"primary_metric": "MSE",
},
"scenario_method": "natural_experiments",
}
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def run(granularity: str | None = None) -> None:
"""Execute Layer 3 benchmark assembly."""
if granularity is None:
granularity = config.GRANULARITY
panel_path = config.DATA_DIR / "processed" / granularity / "panel.parquet"
if not panel_path.exists():
raise FileNotFoundError(f"Run Step 7 (preprocess) first: {panel_path}")
out_dir = config.DATA_DIR / "benchmark" / granularity
out_dir.mkdir(parents=True, exist_ok=True)
# ---- Load processed panel ------------------------------------------------
panel = pd.read_parquet(panel_path)
logger.info("Loaded processed panel: %d rows, %d tickers, %d columns.",
len(panel), panel["ticker"].nunique(), len(panel.columns))
# ---- Temporal split ------------------------------------------------------
if config.TEMPORAL_SPLIT_DATE is not None:
split_date = pd.Timestamp(config.TEMPORAL_SPLIT_DATE)
else:
unique_dates = np.sort(panel["date"].unique())
split_idx = int(len(unique_dates) * config.TEMPORAL_SPLIT_RATIO)
split_idx = max(1, min(split_idx, len(unique_dates) - 1))
split_date = pd.Timestamp(unique_dates[split_idx])
logger.info("Ratio-based split (%.0f:%.0f): split date = %s (%d/%d unique dates)",
config.TEMPORAL_SPLIT_RATIO * 100,
(1 - config.TEMPORAL_SPLIT_RATIO) * 100,
split_date.date(), split_idx, len(unique_dates))
panel["split"] = np.where(panel["date"] < split_date, "train", "test")
train = panel[panel["split"] == "train"]
test = panel[panel["split"] == "test"]
# Cold-start tickers: IPOs that appear only in the test period.
# Kept intentionally — tests model generalisation to unseen companies.
train_tickers = set(train["ticker"].unique())
test_only = set(test["ticker"].unique()) - train_tickers
if test_only:
logger.info("%d cold-start tickers in test (IPOs).", len(test_only))
train.to_parquet(out_dir / "panel_train.parquet", index=False)
test.to_parquet(out_dir / "panel_test.parquet", index=False)
panel.to_csv(out_dir / "panel_full.csv", index=False)
logger.info("Saved train (%d rows) + test (%d rows) + CSV.", len(train), len(test))
# ---- Task definition -----------------------------------------------------
task_def = _build_task_definition(panel, granularity)
(out_dir / "task_definition.json").write_text(json.dumps(task_def, indent=2, default=str))
logger.info("Saved task_definition.json.")
# ---- Filing corpus -------------------------------------------------------
corpus = _build_filing_corpus()
if not corpus.empty:
corpus.to_parquet(out_dir / "filing_corpus.parquet", index=False)
logger.info("Saved filing_corpus.parquet (%d documents).", len(corpus))
# ---- Metadata ------------------------------------------------------------
metadata = {
"format": "panel_data",
"granularity": granularity,
"primary_key": ["ticker", "date"],
"total_rows": len(panel),
"total_tickers": int(panel["ticker"].nunique()),
"date_range": {
"start": str(panel["date"].min().date()),
"end": str(panel["date"].max().date()),
},
"temporal_split": {
"split_date": str(split_date.date()),
"split_method": (
"fixed_date" if config.TEMPORAL_SPLIT_DATE
else f"ratio_{config.TEMPORAL_SPLIT_RATIO}"
),
"train_rows": len(train),
"test_rows": len(test),
"train_date_range": {
"start": str(train["date"].min().date()) if len(train) > 0 else None,
"end": str(train["date"].max().date()) if len(train) > 0 else None,
},
"test_date_range": {
"start": str(test["date"].min().date()) if len(test) > 0 else None,
"end": str(test["date"].max().date()) if len(test) > 0 else None,
},
},
"label_distribution": panel["label"].value_counts().to_dict() if "label" in panel.columns else {},
"columns": list(panel.columns),
"column_count": len(panel.columns),
"column_roles": task_def.get("column_roles", {}),
"filing_corpus_stats": {
"total_documents": len(corpus),
"tickers_with_filings": int(corpus["ticker"].nunique()) if not corpus.empty else 0,
},
"evaluation_protocol": task_def.get("evaluation", {}),
}
(out_dir / "metadata.json").write_text(json.dumps(metadata, indent=2, default=str))
logger.info("Saved metadata.json. Base benchmark assembly complete -> %s", out_dir)
# ---- Valuation benchmark (Tasks A–F) ---------------------------------
try:
from .build_valuation_tasks import build_valuation_benchmark
logger.info("Building valuation benchmark artifacts (%s) …", granularity)
val_summary = build_valuation_benchmark(granularity=granularity)
logger.info("Valuation benchmark: %s", val_summary)
except Exception:
logger.warning("Valuation benchmark build skipped or failed", exc_info=True)
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