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1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 | """Layer 2: Preprocess raw data into a task-agnostic panel.
A **pure function** of (raw data files + config). No API calls, no side
effects. Given the same raw data and config the output is deterministic.
Takes ``config.GRANULARITY`` (``"daily"``, ``"weekly"``, ``"monthly"``) and
produces:
data/processed/{granularity}/panel.parquet -- merged panel
data/processed/{granularity}/columns.json -- column-name groups
Steps:
2a. Load raw data (no transformations)
2b. Resample to target granularity
2c. Merge into panel
2d. Derive time-varying metrics
2e. Save
"""
from __future__ import annotations
import json
import logging
import re
from pathlib import Path
import numpy as np
import pandas as pd
from . import config
logger = logging.getLogger(__name__)
# ===================================================================
# 2a -- Load helpers (pure loading, no transformations)
# ===================================================================
def _load_prices() -> pd.DataFrame:
"""Load raw daily prices and filter out rows with NaN close."""
path = config.PRICES_DIR / "daily_prices.csv"
if not path.exists():
raise FileNotFoundError(f"Run Step 3 first: {path}")
df = pd.read_csv(path, parse_dates=["Date"])
df = df.rename(columns={
"Date": "date", "Ticker": "ticker",
"Open": "open", "High": "high", "Low": "low",
"Close": "close", "Volume": "volume", "Adj Close": "adj_close",
})
# Keep only known columns (guard against extras)
keep = ["ticker", "date", "open", "high", "low", "close", "volume", "adj_close"]
df = df[[c for c in keep if c in df.columns]]
# Explicitly coerce date to datetime64 — mixed formats (e.g. manually-appended
# rows with time components) can cause pd.read_csv to fall back to object dtype
# even with parse_dates=. Downstream resampling requires datetime64.
df["date"] = pd.to_datetime(df["date"], errors="coerce")
# Bad Yahoo data recovery: negative adj_close is impossible (e.g. CBIO had
# negative adj_close values that flipped downstream derivations).
# Recovery: fall back to close value (loses dividend adjustment for those
# rows but preserves valid positive price data — better than NaN).
if "adj_close" in df.columns:
bad_adj = df["adj_close"] < 0
if bad_adj.any():
n = int(bad_adj.sum())
df.loc[bad_adj, "adj_close"] = df.loc[bad_adj, "close"]
logger.info("Recovery: replaced %d negative adj_close rows with close value", n)
# OHLC invariant enforcement: high = max(O,H,L,C), low = min(O,H,L,C).
# A handful of Yahoo rows have high<open or low>open (e.g. CWEN-A 2021-05-05,
# SITC 2021-05-05, UA 2021-05-05, WLY 2021-05-05, CWEN-A 2023-06-05).
# Preserves all four values while forcing the invariant to hold.
if all(c in df.columns for c in ("open", "high", "low", "close")):
prev_bad = ((df["high"] < df[["open", "low", "close"]].max(axis=1)) |
(df["low"] > df[["open", "high", "close"]].min(axis=1))).sum()
if prev_bad:
ohlc = df[["open", "high", "low", "close"]].to_numpy()
df["high"] = ohlc.max(axis=1)
df["low"] = ohlc.min(axis=1)
logger.info("OHLC sanity: enforced high=max(O,H,L,C) / low=min(O,H,L,C) on %d rows", int(prev_bad))
# Defensive: drop rows where close is NaN (junk/delisted/pre-listing)
before = len(df)
df = df.dropna(subset=["close"])
dropped = before - len(df)
if dropped > 0:
logger.info("Dropped %d rows with NaN close in price data.", dropped)
return df.sort_values(["ticker", "date"]).reset_index(drop=True)
def _load_statement_long(ticker: str) -> pd.DataFrame:
"""Load per-ticker statement CSVs into long-form (date, metric, value).
yfinance statement CSVs: index = metric names, columns = date strings.
"""
records: list[dict] = []
for suffix, key_map in [
("income", config.INCOME_KEYS),
("balance", config.BALANCE_KEYS),
("cashflow", config.CASHFLOW_KEYS),
]:
csv_path = config.FUNDAMENTALS_DIR / f"{ticker}_{suffix}.csv"
if not csv_path.exists():
continue
try:
raw = pd.read_csv(csv_path, index_col=0)
for orig_name, col_name in key_map.items():
if orig_name in raw.index:
row = raw.loc[orig_name]
for date_str, val in row.items():
try:
records.append({
"date": pd.to_datetime(date_str),
"metric": col_name,
"value": pd.to_numeric(val, errors="coerce"),
})
except Exception:
continue
except Exception as exc:
logger.debug("Could not load %s for %s: %s", suffix, ticker, exc)
if not records:
return pd.DataFrame()
long_df = pd.DataFrame(records)
# Pivot: rows=date, columns=metric, values=value
wide = long_df.pivot_table(index="date", columns="metric", values="value", aggfunc="first")
wide = wide.reset_index().sort_values("date")
wide.columns.name = None
# Compute trailing-twelve-month (TTM) sums for flow metrics.
# Balance-sheet items (stock variables) are point-in-time and don't need TTM.
_FLOW_METRICS = {
"stmt_revenue", "stmt_net_income", "stmt_ebitda", "stmt_ebit",
"stmt_gross_profit", "stmt_operating_income", "stmt_basic_eps",
"stmt_operating_cashflow", "stmt_free_cashflow", "stmt_capex",
"stmt_cogs", "stmt_operating_expenses", "stmt_financing_cashflow",
}
for col in list(wide.columns):
if col in _FLOW_METRICS:
ttm_col = f"{col}_ttm"
wide[ttm_col] = wide[col].rolling(window=4, min_periods=4).sum()
wide["ticker"] = ticker
return wide
def _load_xbrl_statements(tickers: list[str]) -> pd.DataFrame:
"""Load historical financial statements from SEC EDGAR XBRL facts.
Reads ``data/xbrl/parsed/company_facts.parquet`` and pivots the
relevant tags into the same (ticker, date, stmt_*) wide format that
``_load_statement_long`` produces. This gives us quarterly data
going back 10+ years — far beyond yfinance's ~5-quarter window.
Returns an empty DataFrame if the XBRL data is unavailable.
"""
xbrl_path = config.DATA_DIR / "xbrl" / "parsed" / "company_facts.parquet"
if not xbrl_path.exists():
logger.warning("XBRL facts not found at %s — skipping.", xbrl_path)
return pd.DataFrame()
# Collect all XBRL tags we care about
wanted_tags: set[str] = set()
for tags in config.XBRL_TAG_MAP.values():
wanted_tags.update(tags)
wanted_tags.update(config.XBRL_DA_TAGS)
# Add balance equation validation tag (not mapped to a column, used for equity fix)
wanted_tags.add("LiabilitiesAndStockholdersEquity")
facts = pd.read_parquet(
xbrl_path,
columns=["ticker", "tag", "period_start", "period_end", "value",
"form", "fiscal_year", "fiscal_period", "filed"],
)
# Keep 10-K/10-Q (US), 20-F/6-K (foreign), 40-F (Canadian) for tickers in our universe
facts = facts[
facts["form"].isin(["10-K", "10-Q", "10-K/A", "10-Q/A", "20-F", "20-F/A", "6-K", "40-F", "40-F/A"])
& facts["ticker"].isin(tickers)
& facts["tag"].isin(wanted_tags)
].copy()
if facts.empty:
logger.warning("No matching XBRL facts after filtering.")
return pd.DataFrame()
facts["period_start"] = pd.to_datetime(facts["period_start"], errors="coerce")
facts["period_end"] = pd.to_datetime(facts["period_end"], errors="coerce")
facts["filed"] = pd.to_datetime(facts["filed"], errors="coerce")
facts["value"] = pd.to_numeric(facts["value"], errors="coerce")
facts = facts.dropna(subset=["period_end", "value"])
# --- Separate flow metrics into standalone quarterly values ---
# XBRL 10-Q filings report BOTH cumulative year-to-date figures
# (e.g. 6-month Q1+Q2) and standalone 3-month quarter figures at
# the same period_end. We need standalone quarter values for correct
# TTM computation. Additionally, Q4 standalone values only exist
# in the 10-K as full-year (FY), so we derive Q4 = FY - (Q1+Q2+Q3).
_BALANCE_SHEET_TAGS = {
"Assets", "Liabilities", "LiabilitiesAndStockholdersEquity",
"StockholdersEquity", "StockholdersEquityIncludingPortionAttributableToNoncontrollingInterest",
"LongTermDebt", "LongTermDebtAndCapitalLeaseObligations",
"ShortTermBorrowings", "DebtCurrent", "LongTermDebtNoncurrent",
"CashAndCashEquivalentsAtCarryingValue",
"CashCashEquivalentsRestrictedCashAndRestrictedCashEquivalents",
"EntityCommonStockSharesOutstanding", "CommonStockSharesOutstanding",
"WeightedAverageNumberOfShareOutstandingBasicAndDiluted",
"WeightedAverageNumberOfSharesOutstandingBasic",
"WeightedAverageNumberOfDilutedSharesOutstanding",
"CommonSharesIssued", "CommonSharesOutstanding",
# New balance-sheet detail tags
"AccountsReceivableNetCurrent", "AccountsReceivableNet",
"TradeAndOtherCurrentReceivables",
"InventoryNet", "Inventories", "CurrentInventories",
"AssetsCurrent", "CurrentAssets",
"PropertyPlantAndEquipmentNet", "PropertyPlantAndEquipment",
"Goodwill", "GoodwillGross",
"AccountsPayableCurrent", "AccountsPayable",
"TradeAndOtherCurrentPayables",
"LiabilitiesCurrent", "CurrentLiabilities",
}
# Split: balance sheet tags keep all periods; flow tags need duration filtering
is_balance = facts["tag"].isin(_BALANCE_SHEET_TAGS)
balance_facts = facts[is_balance].copy()
flow_facts = facts[~is_balance].copy()
# For flow facts: keep only standalone quarter values (duration ≤ 100 days)
quarterly_flow = flow_facts[flow_facts["fiscal_period"].isin(["Q1", "Q2", "Q3", "Q4"])].copy()
if quarterly_flow["period_start"].notna().any():
duration = (quarterly_flow["period_end"] - quarterly_flow["period_start"]).dt.days
quarterly_flow = quarterly_flow[duration.isna() | (duration <= 100)]
# Derive Q4 = FY_value - sum(Q1+Q2+Q3 within FY date range).
# Vectorised: cross-join Q1-Q3 onto FY by (ticker, tag), filter by
# date range, aggregate, then subtract.
fy_flow = flow_facts[flow_facts["fiscal_period"] == "FY"].copy()
if not fy_flow.empty and not quarterly_flow.empty:
fy_deduped = fy_flow.sort_values("filed").drop_duplicates(
subset=["ticker", "tag", "period_end"], keep="last",
).dropna(subset=["period_start", "period_end"])
q_deduped = quarterly_flow.sort_values("filed").drop_duplicates(
subset=["ticker", "tag", "period_end"], keep="last",
)
q_deduped = q_deduped[q_deduped["fiscal_period"].isin(["Q1", "Q2", "Q3"])]
if not fy_deduped.empty and not q_deduped.empty:
fy_key = fy_deduped[["ticker", "tag", "period_start", "period_end", "value"]].copy()
fy_key = fy_key.rename(columns={
"period_start": "fy_start", "period_end": "fy_end", "value": "fy_value",
})
q_key = q_deduped[["ticker", "tag", "period_end", "value"]].copy()
q_key = q_key.rename(columns={"period_end": "q_end", "value": "q_value"})
merged = fy_key.merge(q_key, on=["ticker", "tag"], how="inner")
merged = merged[(merged["q_end"] > merged["fy_start"]) & (merged["q_end"] <= merged["fy_end"])]
agg = merged.groupby(["ticker", "tag", "fy_end"]).agg(
q_count=("q_value", "size"),
q_sum=("q_value", "sum"),
fy_value=("fy_value", "first"),
fy_start=("fy_start", "first"),
).reset_index()
agg = agg[agg["q_count"] == 3]
agg["q4_value"] = agg["fy_value"] - agg["q_sum"]
agg = agg[agg["q4_value"] > 0]
if not agg.empty:
q4_rows = fy_deduped.merge(
agg[["ticker", "tag", "fy_end", "q4_value"]],
left_on=["ticker", "tag", "period_end"],
right_on=["ticker", "tag", "fy_end"],
how="inner",
)
q4_rows["value"] = q4_rows["q4_value"]
q4_rows["fiscal_period"] = "Q4_derived"
q4_rows = q4_rows.drop(columns=["fy_end", "q4_value"], errors="ignore")
quarterly_flow = pd.concat([quarterly_flow, q4_rows], ignore_index=True)
logger.info("Derived %d Q4 standalone values from FY - (Q1+Q2+Q3).", len(q4_rows))
# Recombine balance sheet + flow facts
balance_facts = balance_facts[balance_facts["fiscal_period"].isin(
["Q1", "Q2", "Q3", "Q4", "FY"]
)]
facts = pd.concat([balance_facts, quarterly_flow], ignore_index=True)
# Deduplicate: keep the latest filing per (ticker, tag, period_end)
facts = facts.sort_values("filed").drop_duplicates(
subset=["ticker", "tag", "period_end"], keep="last",
)
# --- Resolve priority: for each stmt_ column pick the first available tag ---
col_frames: dict[str, pd.DataFrame] = {}
for stmt_col, tag_list in config.XBRL_TAG_MAP.items():
# Try tags in priority order; only use lower-priority tags for
# (ticker, period_end) combinations not covered by higher ones.
parts: list[pd.DataFrame] = []
covered_keys: set[tuple[str, pd.Timestamp]] = set()
for tag in tag_list:
subset = facts[facts["tag"] == tag][
["ticker", "period_end", "value"]
].copy()
if subset.empty:
continue
if covered_keys:
keep = [
(t, d) not in covered_keys
for t, d in zip(subset["ticker"], subset["period_end"])
]
subset = subset[keep]
if subset.empty:
continue
covered_keys.update(
zip(subset["ticker"], subset["period_end"])
)
parts.append(subset)
if parts:
combined = pd.concat(parts, ignore_index=True)
combined = combined.rename(columns={"value": stmt_col})
col_frames[stmt_col] = combined
if not col_frames:
logger.warning("No XBRL facts resolved to stmt_ columns.")
return pd.DataFrame()
# Merge all stmt_ columns into one wide DataFrame keyed by (ticker, period_end)
items = iter(col_frames.values())
wide = next(items)
for extra in items:
wide = wide.merge(extra, on=["ticker", "period_end"], how="outer")
# --- Derive composite metrics ---
# EBITDA = Operating Income + D&A
if "stmt_ebit" in wide.columns:
da_facts = facts[facts["tag"].isin(config.XBRL_DA_TAGS)].copy()
if not da_facts.empty:
da_facts = da_facts.sort_values("filed").drop_duplicates(
subset=["ticker", "period_end"], keep="last",
)
da_map = da_facts.set_index(["ticker", "period_end"])["value"]
wide_idx = wide.set_index(["ticker", "period_end"])
da_aligned = da_map.reindex(wide_idx.index)
ebitda_derived = wide_idx["stmt_ebit"] + da_aligned
if "stmt_ebitda" not in wide.columns:
wide["stmt_ebitda"] = ebitda_derived.values
else:
mask = wide["stmt_ebitda"].isna()
wide.loc[mask, "stmt_ebitda"] = ebitda_derived.values[mask.values]
# Free Cash Flow = Operating CF - CapEx
if "stmt_operating_cashflow" in wide.columns and "stmt_capex" in wide.columns:
if "stmt_free_cashflow" not in wide.columns:
wide["stmt_free_cashflow"] = (
wide["stmt_operating_cashflow"] - wide["stmt_capex"].abs()
)
else:
mask = wide["stmt_free_cashflow"].isna()
wide.loc[mask, "stmt_free_cashflow"] = (
wide.loc[mask, "stmt_operating_cashflow"]
- wide.loc[mask, "stmt_capex"].abs()
)
# Tax rate = Tax / Pretax
if "stmt_tax_provision" in wide.columns and "stmt_pretax_income" in wide.columns:
if "stmt_tax_rate" not in wide.columns:
pretax = wide["stmt_pretax_income"].replace(0, np.nan)
wide["stmt_tax_rate"] = (wide["stmt_tax_provision"].abs() / pretax).clip(0, 0.5)
# Gross Profit derivation: if missing, derive from Revenue - COGS
if "stmt_revenue" in wide.columns and "stmt_cogs" in wide.columns:
if "stmt_gross_profit" not in wide.columns:
wide["stmt_gross_profit"] = wide["stmt_revenue"] - wide["stmt_cogs"].abs()
else:
mask = wide["stmt_gross_profit"].isna()
wide.loc[mask, "stmt_gross_profit"] = (
wide.loc[mask, "stmt_revenue"] - wide.loc[mask, "stmt_cogs"].abs()
)
# --- Balance equation fix (multi-pass) ---
# The accounting identity Assets = Liabilities + Equity should hold exactly,
# but XBRL data has several failure modes:
# (a) Equity tag excludes NCI while Assets/Liabilities are consolidated totals
# (b) Liabilities tag is missing but Assets and Equity are present
# (c) Assets tag is missing but Liabilities and Equity are present
# (d) All three present but mutually inconsistent (issuer error)
# Strategy: run multiple fix passes, then drop rows that still mismatch > 1%.
if all(c in wide.columns for c in ["stmt_total_assets", "stmt_total_liabilities", "stmt_total_equity"]):
# Build lookup for LiabilitiesAndStockholdersEquity cross-check
lae_facts = facts[facts["tag"] == "LiabilitiesAndStockholdersEquity"][
["ticker", "period_end", "value", "filed"]
].copy()
lae_map = pd.Series(dtype=float)
if not lae_facts.empty:
lae_facts = lae_facts.sort_values("filed").drop_duplicates(
subset=["ticker", "period_end"], keep="last",
)
lae_map = lae_facts.set_index(["ticker", "period_end"])["value"]
# Work on indexed copy
widx = wide.set_index(["ticker", "period_end"])
A = widx["stmt_total_assets"]
L = widx["stmt_total_liabilities"]
E = widx["stmt_total_equity"]
lae = lae_map.reindex(widx.index) if not lae_map.empty else pd.Series(np.nan, index=widx.index)
def _rel_diff(x, y):
return (x - y).abs() / x.abs().replace(0, np.nan)
TOL = 0.01 # 1% tolerance
pre_bad = _rel_diff(A, L.fillna(0) + E.fillna(0)) > TOL
pre_bad_count = pre_bad.sum()
fix_counts = {}
# Pass 1: Both L and E missing → unfixable, skip
# Pass 2: E missing, A and L present → E = A - L
pass2_mask = A.notna() & L.notna() & E.isna()
if pass2_mask.any():
widx.loc[pass2_mask, "stmt_total_equity"] = (A - L)[pass2_mask]
fix_counts["derive_E_from_A_minus_L"] = pass2_mask.sum()
# Pass 3: L missing, A and E present → L = A - E
pass3_mask = A.notna() & L.isna() & E.notna()
if pass3_mask.any():
widx.loc[pass3_mask, "stmt_total_liabilities"] = (A - E)[pass3_mask]
fix_counts["derive_L_from_A_minus_E"] = pass3_mask.sum()
# Pass 4: A missing but LAE or (L+E) present → A = L + E
pass4_mask = A.isna() & L.notna() & E.notna()
if pass4_mask.any():
widx.loc[pass4_mask, "stmt_total_assets"] = (L + E)[pass4_mask]
fix_counts["derive_A_from_L_plus_E"] = pass4_mask.sum()
# Refresh after passes 2-4
A = widx["stmt_total_assets"]
L = widx["stmt_total_liabilities"]
E = widx["stmt_total_equity"]
# Pass 5: A ≈ LAE but (L + E) ≠ A → equity tag is wrong, derive E = A - L
if not lae_map.empty:
lae_aligned = lae_map.reindex(widx.index)
bad5 = (_rel_diff(A, L.fillna(0) + E.fillna(0)) > TOL) & \
(_rel_diff(A, lae_aligned) <= TOL) & lae_aligned.notna() & \
A.notna() & L.notna()
if bad5.any():
widx.loc[bad5, "stmt_total_equity"] = (A - L)[bad5]
fix_counts["equity_fix_via_LAE"] = bad5.sum()
# Pass 6: A ≠ LAE but LAE ≈ (L + E) → Assets tag is wrong, use LAE as A
refresh_E = widx["stmt_total_equity"]
bad6 = (_rel_diff(A, lae_aligned) > TOL) & \
(_rel_diff(lae_aligned, L.fillna(0) + refresh_E.fillna(0)) <= TOL) & \
lae_aligned.notna() & L.notna() & refresh_E.notna()
if bad6.any():
widx.loc[bad6, "stmt_total_assets"] = lae_aligned[bad6]
fix_counts["assets_fix_via_LAE"] = bad6.sum()
# Final check: any remaining > 1% mismatches get ALL THREE set to NaN
# (unreliable data — don't let it pollute derived metrics)
A = widx["stmt_total_assets"]
L = widx["stmt_total_liabilities"]
E = widx["stmt_total_equity"]
still_bad = _rel_diff(A, L.fillna(0) + E.fillna(0)) > TOL
if still_bad.any():
n_drop = still_bad.sum()
widx.loc[still_bad, ["stmt_total_assets", "stmt_total_liabilities", "stmt_total_equity"]] = np.nan
fix_counts["dropped_unreliable"] = n_drop
wide = widx.reset_index()
total_fixes = sum(fix_counts.values())
logger.info(
"Balance equation: %d pre-fix mismatches. Applied: %s. Total fixed/dropped: %d / %d.",
int(pre_bad_count), fix_counts, total_fixes, len(wide),
)
wide = wide.rename(columns={"period_end": "date"})
wide = wide.sort_values(["ticker", "date"]).reset_index(drop=True)
# Compute TTM (trailing-twelve-month) rolling sums for flow metrics,
# matching what _load_statement_long does for yfinance data.
# We must compute per-ticker on the non-null subset only, because the
# wide DataFrame has NaN gaps (different metrics populate different rows).
_FLOW_METRICS = {
"stmt_revenue", "stmt_net_income", "stmt_ebitda", "stmt_ebit",
"stmt_gross_profit", "stmt_operating_income", "stmt_basic_eps",
"stmt_operating_cashflow", "stmt_free_cashflow", "stmt_capex",
"stmt_cogs", "stmt_operating_expenses", "stmt_financing_cashflow",
}
for col in list(wide.columns):
if col in _FLOW_METRICS:
ttm_col = f"{col}_ttm"
wide[ttm_col] = np.nan
for ticker, grp in wide.groupby("ticker"):
valid = grp[col].dropna()
if len(valid) >= 4:
ttm_vals = valid.rolling(window=4, min_periods=4).sum()
wide.loc[ttm_vals.index, ttm_col] = ttm_vals
logger.info(
"Loaded XBRL statements: %d rows, %d tickers, %d stmt columns, "
"date range %s to %s.",
len(wide), wide["ticker"].nunique(),
sum(1 for c in wide.columns if c.startswith("stmt_")),
wide["date"].min().date(), wide["date"].max().date(),
)
return wide
def _load_macro_raw() -> pd.DataFrame:
"""Load all FRED + EIA CSVs into one date-indexed DataFrame (native granularity)."""
macro = pd.DataFrame()
# FRED series
for series_id in config.FRED_SERIES:
csv_path = config.MACRO_DIR / f"fred_{series_id}.csv"
if not csv_path.exists():
continue
try:
df = pd.read_csv(csv_path)
if "date" not in df.columns:
continue
df["date"] = pd.to_datetime(df["date"])
non_date = [c for c in df.columns if c != "date"]
if not non_date:
logger.warning("FRED %s CSV has no value column, skipping.", series_id)
continue
col = series_id if series_id in df.columns else non_date[0]
df = df[["date", col]].rename(columns={col: f"fred_{series_id}"})
df[f"fred_{series_id}"] = pd.to_numeric(df[f"fred_{series_id}"], errors="coerce")
if macro.empty:
macro = df
else:
macro = macro.merge(df, on="date", how="outer")
except Exception as exc:
logger.warning("Could not load FRED %s: %s", series_id, exc)
# EIA commodities
for commodity_type in ["crude_oil", "natural_gas"]:
commodity_dir = config.MACRO_DIR / commodity_type
if not commodity_dir.is_dir():
continue
for csv_file in sorted(commodity_dir.glob("*.csv")):
if "_raw" in csv_file.stem:
continue
try:
df = pd.read_csv(csv_file)
date_col = next(
(c for c in df.columns if "date" in c.lower() or "period" in c.lower() or "time" in c.lower()),
None,
)
if date_col is None:
continue
df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
df = df.dropna(subset=[date_col])
num_cols = df.select_dtypes(include="number").columns.tolist()
if not num_cols:
continue
col_name = f"eia_{commodity_type}_{csv_file.stem}"
df = df[[date_col, num_cols[0]]].rename(columns={date_col: "date", num_cols[0]: col_name})
if macro.empty:
macro = df
else:
macro = macro.merge(df, on="date", how="outer")
except Exception as exc:
logger.warning("Could not load EIA %s: %s", csv_file.name, exc)
if not macro.empty:
macro = macro.sort_values("date").reset_index(drop=True)
return macro
def _load_universe() -> pd.DataFrame:
"""Load benchmark_universe.csv."""
path = config.UNIVERSE_DIR / "benchmark_universe.csv"
if not path.exists():
raise FileNotFoundError(f"Run Step 1 first: {path}")
return pd.read_csv(path)
def _load_company_info() -> pd.DataFrame:
"""Load company_info.csv (static metadata only)."""
path = config.FUNDAMENTALS_DIR / "company_info.csv"
if not path.exists():
return pd.DataFrame()
return pd.read_csv(path)
def _load_filing_metadata(tickers: list[str]) -> dict[str, list[tuple[pd.Timestamp, str, str]]]:
"""Scan filings directory for .md files, extract (date, type, path).
Returns {ticker: [(filing_date, filing_type, rel_path), ...]}, sorted by date.
"""
lookup: dict[str, list[tuple[pd.Timestamp, str, str]]] = {}
for ticker in tickers:
ticker_dir = config.FILINGS_DIR / ticker
entries: list[tuple[pd.Timestamp, str, str]] = []
if ticker_dir.is_dir():
for md_file in ticker_dir.glob("*.md"):
# Classify against the full set of form types we collect
# (see config.SEC_FILING_TYPES). Order matters — check
# more-specific variants first (10-K/A before 10-K).
name = md_file.name
if "10-K/A" in name: ftype = "10-K/A"
elif "10-Q/A" in name: ftype = "10-Q/A"
elif "10-K" in name: ftype = "10-K"
elif "10-Q" in name: ftype = "10-Q"
elif "8-K" in name: ftype = "8-K"
elif "20-F" in name: ftype = "20-F"
elif "40-F" in name: ftype = "40-F"
elif "N-CSRS" in name: ftype = "N-CSRS"
elif "N-CSR" in name: ftype = "N-CSR"
elif "6-K" in name: ftype = "6-K"
elif "DEF 14A" in name or "DEF14A" in name: ftype = "DEF 14A"
elif "S-1" in name: ftype = "S-1"
elif "11-K" in name: ftype = "11-K"
else: ftype = "other"
match = re.search(r"(\d{4}-\d{2}-\d{2})", md_file.name)
if match:
try:
fdate = pd.Timestamp(match.group(1))
rel_path = str(md_file.relative_to(config.DATA_DIR))
entries.append((fdate, ftype, rel_path))
except Exception:
continue
entries.sort(key=lambda x: x[0])
lookup[ticker] = entries
return lookup
def _load_real_estate_summary() -> dict[str, float | int]:
"""Load raw RE CSVs and compute summary statistics.
NOTE: These are static aggregate cross-sectional statistics (counts,
means, medians) broadcast identically to every panel row. They do
not carry temporal information and introduce negligible data leakage
between train/test splits.
"""
summary: dict[str, float | int] = {}
re_dir = config.REAL_ESTATE_DIR
for name in ["properties", "rentals", "sales"]:
csv_path = re_dir / f"{name}.csv"
if csv_path.exists():
try:
df = pd.read_csv(csv_path)
summary[f"re_{name}_count"] = len(df)
for col in ["price", "rent", "squareFootage", "square_footage",
"listPrice", "salePrice", "last_sale_price"]:
if col in df.columns:
vals = pd.to_numeric(df[col], errors="coerce").dropna()
if not vals.empty:
summary[f"re_{name}_{col}_mean"] = float(vals.mean())
summary[f"re_{name}_{col}_median"] = float(vals.median())
except Exception as exc:
logger.warning("Could not load RE %s: %s", name, exc)
demo_path = re_dir / "demographics.csv"
if demo_path.exists():
try:
df = pd.read_csv(demo_path)
summary["re_demographics_metros"] = len(df)
except Exception:
pass
return summary
# ===================================================================
# 2b -- Resample to target granularity
# ===================================================================
def _resample_prices(prices: pd.DataFrame, granularity: str) -> pd.DataFrame:
"""Resample OHLCV+adj_close to target granularity."""
if granularity == "daily":
return prices
freq = "W-FRI" if granularity == "weekly" else "MS"
agg: dict[str, str] = {
"open": "first",
"high": "max",
"low": "min",
"close": "last",
"volume": "sum",
}
if "adj_close" in prices.columns:
agg["adj_close"] = "last"
resampled = (
prices
.set_index("date")
.groupby("ticker")
.resample(freq)
.agg(agg)
.dropna(subset=["close"])
.reset_index()
)
return resampled.sort_values(["ticker", "date"]).reset_index(drop=True)
def _resample_macro(macro: pd.DataFrame, granularity: str) -> pd.DataFrame:
"""Resample macro data to target granularity.
Aggregation rules (matching the plan):
- Rates / indices (FRED series): last value in each period
- Volume / production EIA series: sum
- All other numeric: last
"""
if macro.empty or granularity == "daily":
return macro
freq = "W-FRI" if granularity == "weekly" else "MS"
# Build per-column aggregation rules
# EIA volume/production series should be summed, everything else uses last
_sum_keywords = {"export", "import", "production", "reserves"}
agg_map: dict[str, str] = {}
for col in macro.columns:
if col == "date":
continue
col_lower = col.lower()
if any(kw in col_lower for kw in _sum_keywords):
agg_map[col] = "sum"
else:
agg_map[col] = "last"
resampled = (
macro
.set_index("date")
.resample(freq)
.agg(agg_map)
.reset_index()
)
return resampled.sort_values("date").reset_index(drop=True)
# ===================================================================
# 2c -- Merge into panel
# ===================================================================
def _attach_nearest_filing(
panel: pd.DataFrame,
filing_lookup: dict[str, list[tuple[pd.Timestamp, str, str]]],
) -> pd.DataFrame:
"""For each (ticker, date), find the most recent filing as-of that date.
Uses ``pd.merge_asof`` for vectorised performance instead of iterrows.
"""
# Build a DataFrame of all filings across all tickers
filing_rows: list[dict] = []
for ticker, entries in filing_lookup.items():
for fdate, ftype, fpath in entries:
filing_rows.append({
"ticker": ticker,
"filing_date": fdate,
"filing_type": ftype,
"filing_path": fpath,
})
if not filing_rows:
panel["nearest_filing_type"] = None
panel["nearest_filing_date"] = pd.NaT
panel["nearest_filing_path"] = None
panel["days_since_filing"] = np.nan
return panel
filings_df = pd.DataFrame(filing_rows)
filings_df["filing_date"] = pd.to_datetime(filings_df["filing_date"])
filings_df = filings_df.sort_values("filing_date").reset_index(drop=True)
# merge_asof: for each panel row, find the latest filing with filing_date <= panel date
panel = panel.sort_values("date").reset_index(drop=True)
asof_result = pd.merge_asof(
panel[["ticker", "date"]],
filings_df,
left_on="date",
right_on="filing_date",
by="ticker",
direction="backward",
)
panel["nearest_filing_type"] = asof_result["filing_type"].values
panel["nearest_filing_date"] = pd.to_datetime(asof_result["filing_date"].values)
panel["nearest_filing_path"] = asof_result["filing_path"].values
panel["days_since_filing"] = (panel["date"] - panel["nearest_filing_date"]).dt.days
return panel
# ===================================================================
# 2d -- Derive time-varying metrics
# ===================================================================
def _derive_shares_outstanding(panel: pd.DataFrame, company_info: pd.DataFrame) -> pd.Series:
"""Compute shares_outstanding via the fallback chain.
Priority:
1. stmt_shares_outstanding (balance sheet ``Ordinary Shares Number``)
2. stmt_shares_issued (balance sheet ``Share Issued``)
3. stmt_net_income / stmt_basic_eps (income statement derived)
4. Price-derived via Adj Close split-adjustment ratio
"""
shares = panel.get("stmt_shares_outstanding")
if shares is not None:
shares = shares.copy()
# Treat zero as missing — zero shares means the XBRL tag was
# reported but the company hadn't started reporting real values yet.
shares = shares.replace(0, np.nan)
else:
shares = pd.Series(np.nan, index=panel.index)
# Fallback 2: Share Issued
if "stmt_shares_issued" in panel.columns:
mask = shares.isna()
issued = panel.loc[mask, "stmt_shares_issued"].replace(0, np.nan)
shares.loc[mask] = issued
# Fallback 3: net_income / basic_eps
if "stmt_net_income" in panel.columns and "stmt_basic_eps" in panel.columns:
mask = shares.isna()
eps = panel.loc[mask, "stmt_basic_eps"].replace(0, np.nan)
shares.loc[mask] = panel.loc[mask, "stmt_net_income"] / eps
# Fallback 4: price-derived via Adj Close split-adjustment (vectorised)
if "adj_close" in panel.columns and "close" in panel.columns:
mask = shares.isna()
if mask.any() and not company_info.empty and "marketCap" in company_info.columns:
# Build anchor map from company_info
anchor_df = company_info[["ticker", "marketCap"]].dropna().drop_duplicates(subset="ticker")
anchor_map = dict(zip(anchor_df["ticker"], anchor_df["marketCap"]))
adj_ratio = panel["close"] / panel["adj_close"].replace(0, np.nan)
# For each ticker, find the anchor (latest) close and adj_ratio
# using groupby + transform to avoid Python per-ticker loop
tickers_needing_fb4 = panel.loc[mask, "ticker"].unique()
tickers_with_anchor = [t for t in tickers_needing_fb4 if t in anchor_map]
if tickers_with_anchor:
# Subset to tickers that need fallback 4 AND have an anchor
fb4_mask = mask & panel["ticker"].isin(tickers_with_anchor)
fb4_panel = panel.loc[fb4_mask | panel["ticker"].isin(tickers_with_anchor)].copy()
fb4_panel["_adj_ratio"] = adj_ratio.loc[fb4_panel.index]
# Find the anchor row (latest date) per ticker
latest_idx = fb4_panel.groupby("ticker")["date"].idxmax()
anchor_rows = fb4_panel.loc[latest_idx, ["ticker", "close", "_adj_ratio"]].set_index("ticker")
# Compute anchor shares and anchor adj_ratio per ticker
anchor_info = pd.DataFrame({
"ticker": tickers_with_anchor,
"mcap": [anchor_map[t] for t in tickers_with_anchor],
})
anchor_info = anchor_info.merge(anchor_rows, on="ticker", how="inner")
anchor_info["anchor_shares"] = anchor_info["mcap"] / anchor_info["close"].replace(0, np.nan)
anchor_info["anchor_adj_ratio"] = anchor_info["_adj_ratio"]
anchor_info = anchor_info.dropna(subset=["anchor_shares", "anchor_adj_ratio"])
anchor_info = anchor_info[anchor_info["anchor_adj_ratio"] != 0]
if not anchor_info.empty:
# Map back to panel rows
ticker_to_anchor_shares = dict(zip(anchor_info["ticker"], anchor_info["anchor_shares"]))
ticker_to_anchor_adj = dict(zip(anchor_info["ticker"], anchor_info["anchor_adj_ratio"]))
applicable = mask & panel["ticker"].isin(anchor_info["ticker"])
if applicable.any():
tk_series = panel.loc[applicable, "ticker"]
a_shares = tk_series.map(ticker_to_anchor_shares)
a_adj = tk_series.map(ticker_to_anchor_adj)
historical = a_shares / (adj_ratio.loc[applicable] / a_adj)
shares.loc[applicable] = historical
# ── Sanity check: detect XBRL unit errors (shares reported in thousands) ──
# If shares × latest close > $5T for any ticker, the shares value is
# almost certainly in wrong units. Divide by 1000 iteratively until sane.
if "close" in panel.columns:
_close = panel.groupby("ticker")["close"].transform("last")
_mcap = shares * _close
insane = _mcap > 5e12 # no real company exceeds $5T
if insane.any():
tickers_insane = panel.loc[insane, "ticker"].unique()
for t in tickers_insane:
tmask = panel["ticker"] == t
while (shares.loc[tmask] * _close.loc[tmask]).max() > 5e12:
shares.loc[tmask] = shares.loc[tmask] / 1000
logger.warning(
"Ticker %s: shares_outstanding corrected (XBRL unit error)", t
)
# Sanity check: negative shares_outstanding is physically impossible.
neg_mask = shares < 0
if neg_mask.any():
bad_tickers = panel.loc[neg_mask, "ticker"].unique()
logger.warning(
"Negative shares_outstanding for %d rows (%s) — setting to NaN.",
neg_mask.sum(), list(bad_tickers),
)
shares.loc[neg_mask] = np.nan
# Sanity check: shares_outstanding > 10B likely a unit error.
huge_mask = shares > 10e9
if huge_mask.any():
bad_tickers = panel.loc[huge_mask, "ticker"].unique()
logger.warning(
"shares_outstanding > 10B for %d rows (%s) — setting to NaN.",
huge_mask.sum(), list(bad_tickers),
)
shares.loc[huge_mask] = np.nan
return shares
def _compute_derived_metrics(panel: pd.DataFrame, granularity: str = "daily") -> pd.DataFrame:
"""Add time-varying derived value-estimation columns.
Uses TTM (trailing-twelve-month) values for flow metrics (revenue,
net income, EBITDA, FCF) so that ratios like P/E reflect annualised
earnings, not a single quarter. Falls back to single-quarter values
if TTM columns are unavailable.
"""
out = panel.copy()
so = out.get("shares_outstanding")
if so is None:
return out
close = out["close"]
# Split-adjust shares_outstanding using the close/adj_close ratio.
# XBRL shares_outstanding can be stale (from a pre-split filing) while
# yfinance close is retroactively adjusted. When close/adj_close > 1.5,
# a split occurred and we need to divide shares by the split ratio.
#
# IMPORTANT: require POSITIVE split_ratio in a sane range. Bad Yahoo
# data (e.g., CBIO had negative adj_close values) would flip shares
# to negative if we didn't guard against this.
if "adj_close" in out.columns:
adj_close_safe = out["adj_close"].replace(0, np.nan)
# Treat non-positive adj_close as bad data → skip split adjust for those rows
adj_close_safe = adj_close_safe.where(adj_close_safe > 0)
split_ratio = close / adj_close_safe
# Only adjust where the ratio is positive AND meaningfully != 1
needs_adj = ((split_ratio > 1.5) | (split_ratio < 0.67)) & (split_ratio > 0)
if needs_adj.any():
so = so.copy()
so.loc[needs_adj] = so.loc[needs_adj] / split_ratio.loc[needs_adj]
n_adj = needs_adj.sum()
n_tickers = out.loc[needs_adj, "ticker"].nunique()
logger.info(
"Split-adjusted shares_outstanding for %d rows (%d tickers) "
"using close/adj_close ratio.",
n_adj, n_tickers,
)
out["derived_market_cap"] = close * so
# Final safety: any negative mcap (shouldn't happen after above guard,
# but catches anything weird) gets NaN.
neg_mc = out["derived_market_cap"] < 0
if neg_mc.any():
n_neg = neg_mc.sum()
n_t = out.loc[neg_mc, "ticker"].nunique()
logger.warning("Negative derived_market_cap for %d rows (%d tickers) — setting to NaN.", n_neg, n_t)
out.loc[neg_mc, "derived_market_cap"] = np.nan
# Sanity: cap market cap at $100B — no small/micro-cap should exceed this.
# The largest R2K member in our universe is ~$40B (a name that has drifted
# up since reconstitution). Values above $100B (2.5× that) arise from
# XBRL shares_outstanding unit errors × prices and should be NaN'd.
# Previous threshold of $500B was too permissive for a small-cap benchmark.
_MCAP_CEILING = 100e9
mcap_insane = out["derived_market_cap"] > _MCAP_CEILING
if mcap_insane.any():
n_insane = mcap_insane.sum()
tickers_insane = out.loc[mcap_insane, "ticker"].nunique()
logger.warning(
"derived_market_cap > $%.0fB for %d rows (%d tickers) — setting to NaN.",
_MCAP_CEILING / 1e9, n_insane, tickers_insane,
)
out.loc[mcap_insane, "derived_market_cap"] = np.nan
def _col(name: str) -> pd.Series | None:
"""Return TTM column if available, else quarterly, else None."""
ttm = f"{name}_ttm"
if ttm in out.columns:
return out[ttm]
if name in out.columns:
return out[name]
return None
ni = _col("stmt_net_income")
if ni is not None:
# PE is economically meaningful only for profitable companies.
# Null-mask for loss-makers (ni <= 0) rather than emitting huge
# negative values that pollute downstream stats.
ni_safe = ni.where(ni > 0)
out["derived_pe"] = out["derived_market_cap"] / ni_safe
if "stmt_total_debt" in out.columns and "stmt_cash" in out.columns:
out["derived_ev"] = out["derived_market_cap"] + out["stmt_total_debt"].fillna(0) - out["stmt_cash"].fillna(0)
rev = _col("stmt_revenue")
if "derived_ev" in out.columns and rev is not None:
out["derived_ev_to_revenue"] = out["derived_ev"] / rev.replace(0, np.nan)
ebitda = _col("stmt_ebitda")
if "derived_ev" in out.columns and ebitda is not None:
out["derived_ev_to_ebitda"] = out["derived_ev"] / ebitda.replace(0, np.nan)
fcf = _col("stmt_free_cashflow")
if fcf is not None:
out["derived_fcf_yield"] = fcf / out["derived_market_cap"].replace(0, np.nan)
if "stmt_total_equity" in out.columns:
out["derived_pb"] = out["derived_market_cap"] / out["stmt_total_equity"].replace(0, np.nan)
if "stmt_total_debt" in out.columns and "stmt_total_equity" in out.columns:
out["derived_debt_to_equity"] = out["stmt_total_debt"] / out["stmt_total_equity"].replace(0, np.nan)
# ── Valuation-ready metrics ──────────────────────────────────────
# Effective tax rate (Tax Provision / Pretax Income, clamped 0–50 %)
tax = _col("stmt_tax_provision")
pretax = _col("stmt_pretax_income")
if tax is not None and pretax is not None:
out["derived_effective_tax_rate"] = (
tax.abs() / pretax.replace(0, np.nan)
).clip(0.0, 0.50)
# Cost of debt proxy (Interest Expense / Total Debt, clamped 0–20 %)
int_exp = _col("stmt_interest_expense")
if int_exp is not None and "stmt_total_debt" in out.columns:
out["derived_cost_of_debt"] = (
int_exp.abs() / out["stmt_total_debt"].replace(0, np.nan)
).clip(0.0, 0.20)
# Rolling beta vs S&P 500 (granularity-aware window)
if "fred_SP500" in out.columns and "close" in out.columns:
_gran = granularity
if _gran == "monthly":
_beta_window, _beta_min = 36, 12
elif _gran == "weekly":
_beta_window, _beta_min = 52, 13
else:
_beta_window, _beta_min = config.BETA_LOOKBACK_DAYS, 60
out["derived_beta"] = np.nan
for tk, grp in out.groupby("ticker", sort=False):
if len(grp) < _beta_min:
continue
stk_ret = grp["close"].pct_change()
mkt_ret = grp["fred_SP500"].pct_change()
# Rolling covariance / rolling market variance
cov_sm = stk_ret.rolling(_beta_window, min_periods=_beta_min).cov(mkt_ret)
var_m = mkt_ret.rolling(_beta_window, min_periods=_beta_min).var()
beta = (cov_sm / var_m.replace(0, np.nan)).clip(0.1, 4.0)
out.loc[grp.index, "derived_beta"] = beta
# WACC estimate (simplified: Ke * E/(D+E) + Kd * (1-t) * D/(D+E))
if "derived_beta" in out.columns and "fred_DGS10" in out.columns:
rf = out["fred_DGS10"].ffill() / 100.0
ke = rf + out["derived_beta"].fillna(1.0) * config.MARKET_RISK_PREMIUM
kd = out.get("derived_cost_of_debt")
if kd is None:
kd = rf + 0.02 # fallback spread
t = out.get("derived_effective_tax_rate")
if t is None:
t = 0.21
if "stmt_total_debt" in out.columns and "derived_market_cap" in out.columns:
d = out["stmt_total_debt"].fillna(0)
e = out["derived_market_cap"].fillna(0)
total = (d + e).replace(0, np.nan)
d_w = d / total
e_w = e / total
out["derived_wacc"] = (e_w * ke + d_w * kd * (1 - t)).clip(0.03, 0.25)
# ── Margin & ratio metrics from new stmt_ fields ──────────────
# Gross Profit % = Gross Profit / Revenue
gp = _col("stmt_gross_profit")
if gp is not None and rev is not None:
out["derived_gross_margin"] = (gp / rev.replace(0, np.nan)).clip(-1, 1)
# EBITDA Margin = EBITDA / Revenue
if ebitda is not None and rev is not None:
out["derived_ebitda_margin"] = (ebitda / rev.replace(0, np.nan)).clip(-2, 2)
# Net Margin = Net Income / Revenue
if ni is not None and rev is not None:
out["derived_net_margin"] = (ni / rev.replace(0, np.nan)).clip(-2, 2)
# COGS % of Revenue = COGS / Revenue
cogs = _col("stmt_cogs")
if cogs is not None and rev is not None:
out["derived_cogs_pct"] = (cogs / rev.replace(0, np.nan)).clip(0, 2)
# Revenue Growth YoY (per-ticker, lagged by granularity-appropriate periods)
if rev is not None:
if granularity == "monthly":
lag_periods = 12
elif granularity == "weekly":
lag_periods = 52
else:
lag_periods = 252
out["derived_rev_growth_yoy"] = np.nan
for tk, grp in out.groupby("ticker", sort=False):
rev_vals = rev.loc[grp.index]
rev_lag = rev_vals.shift(lag_periods)
growth = (rev_vals - rev_lag) / rev_lag.replace(0, np.nan)
out.loc[grp.index, "derived_rev_growth_yoy"] = growth.clip(-5, 50)
# Current Ratio = Current Assets / Current Liabilities
if "stmt_current_assets" in out.columns and "stmt_current_liabilities" in out.columns:
cl = out["stmt_current_liabilities"].replace(0, np.nan)
out["derived_current_ratio"] = (out["stmt_current_assets"] / cl).clip(0, 50)
return out
# ===================================================================
# 2e -- Build column role index
# ===================================================================
def _build_column_roles(columns: list[str]) -> dict[str, list[str]]:
"""Classify panel columns into roles based on naming convention."""
roles: dict[str, list[str]] = {
"target": [],
"endogenous": [],
"exogenous_fundamental": [],
"exogenous_statement": [],
"exogenous_macro": [],
"exogenous_commodity": [],
"context_filing": [],
"context_real_estate": [],
"metadata": [],
}
for c in columns:
if c == "close":
roles["target"].append(c)
elif c in ("open", "high", "low", "volume", "adj_close"):
roles["endogenous"].append(c)
elif c.startswith("derived_") or c == "shares_outstanding":
roles["exogenous_fundamental"].append(c)
elif c.startswith("stmt_"):
roles["exogenous_statement"].append(c)
elif c.startswith("fred_"):
roles["exogenous_macro"].append(c)
elif c.startswith("eia_"):
roles["exogenous_commodity"].append(c)
elif c.startswith("nearest_filing") or c == "days_since_filing":
roles["context_filing"].append(c)
elif c.startswith("re_"):
roles["context_real_estate"].append(c)
else:
roles["metadata"].append(c)
return roles
# ===================================================================
# Public API
# ===================================================================
def run(granularity: str | None = None) -> pd.DataFrame:
"""Execute Layer 2 preprocessing and return the merged panel DataFrame.
Parameters
----------
granularity : str, optional
``"daily"``, ``"weekly"``, or ``"monthly"``.
Defaults to ``config.GRANULARITY``.
"""
if granularity is None:
granularity = config.GRANULARITY
out_dir = config.DATA_DIR / "processed" / granularity
out_dir.mkdir(parents=True, exist_ok=True)
# --- 2a. Load raw data ---------------------------------------------------
logger.info("Loading raw data ...")
prices_raw = _load_prices()
universe = _load_universe()
company_info = _load_company_info()
macro_raw = _load_macro_raw()
# Filter out excluded tickers (unadjusted reverse-split prices)
if config.EXCLUDED_TICKERS:
prices_raw = prices_raw[~prices_raw["ticker"].isin(config.EXCLUDED_TICKERS)]
tickers = prices_raw["ticker"].unique().tolist()
logger.info("Loaded prices: %d rows, %d tickers.", len(prices_raw), len(tickers))
# --- 2b. Resample ---------------------------------------------------------
logger.info("Resampling to %s ...", granularity)
prices = _resample_prices(prices_raw, granularity)
macro = _resample_macro(macro_raw, granularity)
logger.info("Resampled prices: %d rows.", len(prices))
# --- 2c. Merge into panel -------------------------------------------------
panel = prices.copy()
# Static metadata from universe
static_cols = ["ticker", "sector", "industry", "exchange",
"in_russell_2000", "lower_end_russell2000", "small_cap_outside"]
static_cols = [c for c in static_cols if c in universe.columns]
panel = panel.merge(universe[static_cols], on="ticker", how="left")
# Static metadata from company_info (only truly static fields).
# For sector and industry: the universe CSV has these for IWM/IJR/IWC
# tickers (from iShares) but they are NULL for UNCOVERED tickers.
# company_info.csv (from yfinance .info) has sector/industry for ~99.6%
# of all tickers. We fill NaN values from company_info AFTER the
# universe merge so that UNCOVERED tickers get their sector/industry.
if not company_info.empty:
info_static = ["ticker"]
for col in ["sector", "industry", "fullTimeEmployees"]:
if col in company_info.columns:
if col not in panel.columns:
info_static.append(col)
else:
# Column exists but may have NaN from universe merge
# (e.g. UNCOVERED tickers). Fill NaN from company_info.
ci_map = company_info.set_index("ticker")[col].dropna()
null_mask = panel[col].isna()
if null_mask.any():
filled = panel.loc[null_mask, "ticker"].map(ci_map)
panel.loc[null_mask, col] = filled
n_filled = filled.notna().sum()
if n_filled > 0:
logger.info("Filled %d NaN %s values from company_info.", n_filled, col)
if len(info_static) > 1:
panel = panel.merge(company_info[info_static], on="ticker", how="left")
# Keep marketCap for shares_outstanding fallback (not merged into panel)
# Normalize exchange names. iShares and NASDAQ Trader use different
# conventions for the same exchanges (e.g. "Nyse Mkt Llc" vs "NYSE_MKT").
_EXCHANGE_NORMALIZE: dict[str, str] = {
"Nyse Mkt Llc": "NYSE MKT",
"NYSE_MKT": "NYSE MKT",
"Non-Nms Quotation Service (Nnqs)": "OTC",
"NO MARKET (E.G. UNLISTED)": "OTC",
}
if "exchange" in panel.columns:
panel["exchange"] = panel["exchange"].replace(_EXCHANGE_NORMALIZE)
# Normalize sector names to GICS convention. iShares uses GICS names
# (e.g. "Health Care"), yfinance uses its own convention (e.g. "Healthcare").
# After filling NaN sectors from company_info, the panel has a mix of both.
# Standardize to GICS so all sector-based analysis is consistent.
_SECTOR_NORMALIZE: dict[str, str] = {
"Financial Services": "Financials",
"Healthcare": "Health Care",
"Consumer Cyclical": "Consumer Discretionary",
"Technology": "Information Technology",
"Basic Materials": "Materials",
"Communication Services": "Communication",
"Consumer Defensive": "Consumer Staples",
}
if "sector" in panel.columns:
before_unique = panel["sector"].nunique()
panel["sector"] = panel["sector"].replace(_SECTOR_NORMALIZE)
after_unique = panel["sector"].nunique()
if before_unique != after_unique:
logger.info("Normalized sector names: %d → %d unique values (GICS convention).",
before_unique, after_unique)
# Industry→Sector consistency: if an industry maps to multiple sectors
# across tickers (yfinance vs iShares taxonomies differ), force all rows
# with that industry to use the modal sector. This ensures
# industry→sector is 1:1 as expected by GICS.
if "industry" in panel.columns and "sector" in panel.columns:
mode_map = panel.dropna(subset=["industry","sector"]).groupby("industry")["sector"].agg(
lambda x: x.mode().iloc[0] if len(x.mode()) > 0 else None
)
has_ind = panel["industry"].notna()
if has_ind.any():
panel.loc[has_ind, "sector"] = panel.loc[has_ind, "industry"].map(mode_map).fillna(panel.loc[has_ind, "sector"])
logger.info("Applied industry→sector modal normalization.")
logger.info("Merged static metadata.")
# Statement financials (as-of merge)
# Source 1: yfinance quarterly statements (~5 recent quarters)
logger.info("Loading per-ticker financial statements (yfinance) ...")
yf_frames: list[pd.DataFrame] = []
for ticker in tickers:
stmt = _load_statement_long(ticker)
if not stmt.empty:
yf_frames.append(stmt)
# Source 2: SEC EDGAR XBRL facts (10+ years of history)
logger.info("Loading XBRL historical statements ...")
xbrl_stmts = _load_xbrl_statements(tickers)
# Combine: XBRL provides the long history, yfinance overwrites with
# its more recent (and often more complete) data where both exist.
all_stmts: pd.DataFrame | None = None
if not xbrl_stmts.empty:
all_stmts = xbrl_stmts
if yf_frames:
yf_all = pd.concat(yf_frames, ignore_index=True)
if all_stmts is not None:
# Align columns: ensure both DataFrames share the same stmt_ set
all_stmt_cols = sorted(
{c for c in all_stmts.columns if c.startswith("stmt_")}
| {c for c in yf_all.columns if c.startswith("stmt_")}
)
for c in all_stmt_cols:
if c not in all_stmts.columns:
all_stmts[c] = np.nan
if c not in yf_all.columns:
yf_all[c] = np.nan
# Concat then deduplicate: prefer yfinance (listed last → keep="last")
combined = pd.concat([all_stmts, yf_all], ignore_index=True)
combined = combined.sort_values("date")
combined = combined.drop_duplicates(
subset=["ticker", "date"], keep="last",
)
all_stmts = combined
else:
all_stmts = yf_all
if all_stmts is not None and not all_stmts.empty:
stmt_cols = [c for c in all_stmts.columns if c.startswith("stmt_")]
# Forward-fill per ticker: different XBRL tags report on different
# period_end dates, so the wide DataFrame is sparse. Carrying the
# last known value forward ensures merge_asof picks up the most
# recent data for *every* column, not just the columns that happen
# to be non-null at the single nearest-prior row.
all_stmts = all_stmts.sort_values(["ticker", "date"]).reset_index(drop=True)
for col in stmt_cols:
all_stmts[col] = all_stmts.groupby("ticker")[col].ffill()
# Backfill the initial gap: for rows before a ticker's first
# filing, carry the earliest known value backward so that
# merge_asof can find data for every panel row.
all_stmts[col] = all_stmts.groupby("ticker")[col].bfill()
# merge_asof requires the 'on' key to be globally sorted AND both
# sides must have matching dtype (datetime64[ns]). XBRL can produce
# date columns as object dtype when values fall outside the standard
# pandas range or contain mixed types; coerce explicitly.
all_stmts["date"] = pd.to_datetime(all_stmts["date"], errors="coerce")
panel["date"] = pd.to_datetime(panel["date"], errors="coerce")
all_stmts = all_stmts.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
panel = panel.dropna(subset=["date"]).sort_values("date").reset_index(drop=True)
# Apply balance-equation validation to the COMBINED statements
# (XBRL + yfinance). Rows with A ≠ L + E beyond 1% get A/L/E set
# to NaN so they don't propagate wrong numbers to the panel.
if all(c in all_stmts.columns for c in ["stmt_total_assets", "stmt_total_liabilities", "stmt_total_equity"]):
A = all_stmts["stmt_total_assets"]
L = all_stmts["stmt_total_liabilities"]
E = all_stmts["stmt_total_equity"]
all_present = A.notna() & L.notna() & E.notna()
rel_err = ((A - L.fillna(0) - E.fillna(0)).abs() / A.abs().replace(0, np.nan))
bad = all_present & (rel_err > 0.01)
if bad.any():
n = bad.sum()
all_stmts.loc[bad, ["stmt_total_assets", "stmt_total_liabilities", "stmt_total_equity"]] = np.nan
logger.info("Combined statements: dropped A/L/E for %d rows with balance mismatch > 1%% (post-combine).", n)
panel = pd.merge_asof(
panel, all_stmts[["ticker", "date"] + stmt_cols],
on="date", by="ticker", direction="backward",
)
logger.info("Merged statement financials (%d metrics) via as-of join.", len(stmt_cols))
# Statement value sanity with RECOVERY, not just NaN.
# Negative revenue / non-positive assets often come from forward-filling
# a single bad XBRL value. The correct economic value exists in a prior
# filing — we replace each bad value with the last known-good (positive)
# value from the same ticker, forward-filled.
sanity_rules = [
("stmt_revenue", "< 0", lambda s: s < 0),
("stmt_revenue_ttm", "< 0", lambda s: s < 0),
("stmt_total_assets", "<= 0", lambda s: s <= 0),
("stmt_total_liabilities", "< 0", lambda s: s < 0),
]
panel = panel.sort_values(["ticker", "date"])
for col, rule_name, rule_fn in sanity_rules:
if col not in panel.columns:
continue
bad = rule_fn(panel[col]) & panel[col].notna()
if not bad.any():
continue
n_bad = int(bad.sum())
# Null bad values, then forward-fill per ticker to recover last valid positive
panel.loc[bad, col] = np.nan
panel[col] = panel.groupby("ticker")[col].ffill()
# Any residual (ticker never had positive value): keep NaN — genuinely unknown
still_bad = rule_fn(panel[col]) & panel[col].notna()
if still_bad.any():
panel.loc[still_bad, col] = np.nan
remaining = panel[col].isna().sum()
logger.info("Sanity fix %s %s: %d bad values recovered via per-ticker forward-fill (final nulls: %d)",
col, rule_name, n_bad, remaining)
# Final pass: post-as-of-merge balance-equation residual purge.
# The earlier per-statement fix purges bad filings before merging, but
# merge_asof can carry a small number of bad A/L/E triples forward on
# the daily panel. Additionally, independently forward-filling each
# column per ticker can recombine values from different source rows,
# producing a post-fill triple that is itself imbalanced (observed
# bug: VS ticker, 90 residual rows). Fix: forward-fill as a unified
# triple, sourcing ONLY from rows where all three were originally
# present AND balanced. Any row that cannot source from such a row
# stays NaN across all three.
ble_cols = ["stmt_total_assets", "stmt_total_liabilities", "stmt_total_equity"]
if all(c in panel.columns for c in ble_cols):
# CRITICAL: sort FIRST, then compute good — otherwise the good
# mask is aligned to the pre-sort row order and the fill below
# sources from the wrong rows (v2 bug, observed as 1,968 residuals
# vs. 113 with the simpler v1 fix). Work on a reset-index frame.
panel = panel.sort_values(["ticker", "date"]).reset_index(drop=True)
A = panel["stmt_total_assets"]
L = panel["stmt_total_liabilities"]
E = panel["stmt_total_equity"]
all_present = A.notna() & L.notna() & E.notna()
rel_err = ((A - L.fillna(0) - E.fillna(0)).abs() / A.abs().replace(0, np.nan))
good = all_present & (rel_err <= 0.01)
n_bad_initial = int((all_present & ~good).sum())
# Per-ticker row-index of the most recent good row (carries
# the triple as a unit, avoiding the independent-column drift
# that broke v1).
idx_series = pd.Series(panel.index.to_numpy(), index=panel.index)
good_idx = idx_series.where(good)
last_good = good_idx.groupby(panel["ticker"]).ffill()
fill_mask = (~good) & last_good.notna()
if fill_mask.any():
src_idx = last_good[fill_mask].astype(int).to_numpy()
dst_idx = panel.index[fill_mask].to_numpy()
for c in ble_cols:
panel.loc[dst_idx, c] = panel[c].to_numpy()[src_idx]
orphan_mask = (~good) & last_good.isna()
if orphan_mask.any():
panel.loc[orphan_mask, ble_cols] = np.nan
# Post-verify the invariant actually holds on what we kept.
A2 = panel["stmt_total_assets"]
L2 = panel["stmt_total_liabilities"]
E2 = panel["stmt_total_equity"]
all2 = A2.notna() & L2.notna() & E2.notna()
rel2 = ((A2 - L2.fillna(0) - E2.fillna(0)).abs()
/ A2.abs().replace(0, np.nan))
residual = int((all2 & (rel2 > 0.01)).sum())
logger.info(
"Balance-eq residual purge: %d bad → %d filled / %d orphan-nulled"
" / %d residual (post-fix verify)",
n_bad_initial, int(fill_mask.sum()), int(orphan_mask.sum()), residual,
)
panel = panel.reset_index(drop=True)
else:
logger.warning("No statement financials loaded.")
# Macro / commodity (as-of merge, broadcast to all tickers)
if not macro.empty:
macro = macro.sort_values("date").reset_index(drop=True)
macro_cols = [c for c in macro.columns if c != "date"]
macro[macro_cols] = macro[macro_cols].ffill()
panel = panel.sort_values("date").reset_index(drop=True)
panel = pd.merge_asof(panel, macro, on="date", direction="backward")
logger.info("Merged macro data (%d series).", len(macro_cols))
else:
logger.warning("No macro data loaded.")
# Filing context
filing_lookup = _load_filing_metadata(tickers)
tickers_with_filings = sum(1 for v in filing_lookup.values() if v)
if tickers_with_filings > 0:
panel = _attach_nearest_filing(panel, filing_lookup)
logger.info("Attached filing context (%d tickers have filings).", tickers_with_filings)
else:
logger.warning("No filings found for any ticker.")
panel["nearest_filing_type"] = None
panel["nearest_filing_date"] = pd.NaT
panel["nearest_filing_path"] = None
panel["days_since_filing"] = np.nan
# Real estate summary — removed: these 15 columns are global aggregates
# broadcast identically to every row (e.g. re_properties_count=47507).
# They carry zero per-row information and inflate the feature count.
# The summary is still available via _load_real_estate_summary().
# re_summary = _load_real_estate_summary() # disabled — see data-quality audit
# --- 2d. Derive time-varying metrics --------------------------------------
logger.info("Deriving time-varying metrics ...")
panel["shares_outstanding"] = _derive_shares_outstanding(panel, company_info)
panel = _compute_derived_metrics(panel, granularity=granularity)
# --- Small-cap filter already applied at universe collection time ---
# collect_universe.py applies the $7.4B market_cap filter to IWC and
# UNCOVERED tickers only. IWM (Russell 2000) and IJR (S&P SmallCap 600)
# tickers are kept regardless of market_cap because they are index-
# designated small-caps. No additional filtering is needed here —
# the universe CSV is the authoritative ticker set.
# Labels
panel["label"] = "other"
if "lower_end_russell2000" in panel.columns:
panel.loc[panel["lower_end_russell2000"] == True, "label"] = "lower_end_r2k" # noqa: E712
if "small_cap_outside" in panel.columns:
panel.loc[panel["small_cap_outside"] == True, "label"] = "small_cap_outside" # noqa: E712
# Final sort
panel = panel.sort_values(["ticker", "date"]).reset_index(drop=True)
# --- 2e. Save -------------------------------------------------------------
panel.to_parquet(out_dir / "panel.parquet", index=False)
col_roles = _build_column_roles(list(panel.columns))
(out_dir / "columns.json").write_text(json.dumps(col_roles, indent=2))
logger.info(
"Panel saved: %d rows, %d tickers, %d columns at %s granularity. -> %s",
len(panel), panel["ticker"].nunique(), len(panel.columns),
granularity, out_dir / "panel.parquet",
)
return panel
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