code/config.py

"""Centralised configuration for the What-If Scenario Benchmark pipeline.

Every tunable parameter lives here so that notebooks and scripts have a
single source of truth.

Architecture:
  Layer 1 (Raw Collection)  -> data/{source}/
  Layer 2 (Preprocessing)   -> data/processed/{GRANULARITY}/
  Layer 3 (Benchmark)       -> data/benchmark/{GRANULARITY}/
"""

from pathlib import Path

# ---------------------------------------------------------------------------
# Paths -- Layer 1 (raw data)
# ---------------------------------------------------------------------------
import os as _os

BASE_DIR = Path(__file__).resolve().parent
# Small-cap rebuild: all data lives under data_small_caps/ for the
# clean-slate small-cap-and-below universe rebuild (Apr 2026).
DATA_DIR = BASE_DIR / _os.environ.get("WHATIF_DATA_DIR", "data_small_caps")

UNIVERSE_DIR = DATA_DIR / "universe"
FUNDAMENTALS_DIR = DATA_DIR / "fundamentals"
PRICES_DIR = DATA_DIR / "prices"
FILINGS_DIR = DATA_DIR / "filings"
MACRO_DIR = DATA_DIR / "macro"
REAL_ESTATE_DIR = DATA_DIR / "real_estate"
NEWS_DIR = DATA_DIR / "news"
XBRL_DIR = DATA_DIR / "xbrl"

# ---------------------------------------------------------------------------
# Paths -- Layer 2 & 3 (derived from GRANULARITY)
# ---------------------------------------------------------------------------
GRANULARITY: str = "daily"  # "daily", "weekly", or "monthly"


def get_processed_dir(granularity: str | None = None) -> Path:
    """Return the processed-data directory for *granularity* (default: GRANULARITY)."""
    return DATA_DIR / "processed" / (granularity or GRANULARITY)


def get_benchmark_dir(granularity: str | None = None) -> Path:
    """Return the benchmark-output directory for *granularity* (default: GRANULARITY)."""
    return DATA_DIR / "benchmark" / (granularity or GRANULARITY)


# Legacy module-level aliases (point to the default granularity).
# Use the functions above when the caller might override granularity.
PROCESSED_DIR = get_processed_dir()
BENCHMARK_DIR = get_benchmark_dir()

# ---------------------------------------------------------------------------
# Date range (fixed for reproducibility)
# ---------------------------------------------------------------------------
START_DATE = "2021-01-01"
END_DATE = "2026-04-01"
START_YEAR = int(START_DATE[:4])  # 2021 — used by collect_filings.py
END_YEAR = int(END_DATE[:4])      # 2026 — used by collect_filings.py

# ---------------------------------------------------------------------------
# Global reproducibility seed
# ---------------------------------------------------------------------------
BENCHMARK_SEED = 42

# ---------------------------------------------------------------------------
# Ticker universe
# ---------------------------------------------------------------------------
# iShares Russell 2000 ETF holdings CSV URL
IWM_HOLDINGS_URL = (
    "https://www.ishares.com/us/products/239710/"
    "ishares-russell-2000-etf/1467271812596.ajax?"
    "fileType=csv&fileName=IWM_holdings&dataType=fund"
)
# iShares Core S&P SmallCap ETF (IJR) — tracks S&P SmallCap 600 index
# Defines official "small-cap" range: $1B – $7.4B (S&P methodology, 2025).
IJR_HOLDINGS_URL = (
    "https://www.ishares.com/us/products/239774/"
    "ishares-core-sp-smallcap-etf/1467271812596.ajax?"
    "fileType=csv&fileName=IJR_holdings&dataType=fund"
)
# iShares Micro-Cap ETF holdings CSV URL (micro-caps below small-cap threshold)
IWC_HOLDINGS_URL = (
    "https://www.ishares.com/us/products/239724/"
    "ishares-microcap-etf/1467271812596.ajax?"
    "fileType=csv&fileName=IWC_holdings&dataType=fund"
)

# Market-cap upper bound for the "small-cap and below" universe.
# $7.4B = official S&P 600 SmallCap upper bound (S&P Dow Jones Indices, 2025).
# Tickers with median derived_market_cap above this are filtered out as
# mid-cap or larger and excluded from the benchmark.
SMALL_CAP_MAX_MEDIAN_MCAP: float = 7.4e9

# Market-cap percentile threshold to label "lower end" of Russell 2000
LOWER_END_PERCENTILE = 50  # bottom 50 %

# Cap the total number of tickers (set to None for full universe)
MAX_TICKERS: int | None = None

# Tickers excluded from the universe (none — filter is applied via market cap).
EXCLUDED_TICKERS: list[str] = []

# ---------------------------------------------------------------------------
# Fundamentals collection
# ---------------------------------------------------------------------------
FUNDAMENTALS_WORKERS = 2  # ThreadPoolExecutor parallelism (low to avoid yfinance rate limits)

# ---------------------------------------------------------------------------
# Price collection
# ---------------------------------------------------------------------------
PRICE_BATCH_SIZE = 50  # tickers per yf.download() call

# ---------------------------------------------------------------------------
# SEC filings
# ---------------------------------------------------------------------------
SEC_FILING_TYPES: list[str] = ["10-K", "10-Q", "8-K", "20-F", "6-K", "N-CSR", "N-CSRS"]
SEC_FILING_WORKERS = 4  # asyncio.Semaphore concurrency

# ---------------------------------------------------------------------------
# FRED macro series
# ---------------------------------------------------------------------------
FRED_SERIES: dict[str, str] = {
    # ── Rates & monetary policy ──
    "FEDFUNDS": "Federal Funds Effective Rate",
    "SOFR": "Secured Overnight Financing Rate",
    "DGS2": "2-Year Treasury Constant Maturity Rate",
    "DGS10": "10-Year Treasury Constant Maturity Rate",
    "DGS30": "30-Year Treasury Constant Maturity Rate",
    "T10Y3M": "10-Year Treasury Minus 3-Month Treasury",
    "T10Y2Y": "10-Year Treasury Minus 2-Year Treasury",
    "MORTGAGE30US": "30-Year Fixed Rate Mortgage Average",
    # ── Equity & volatility ──
    "SP500": "S&P 500 Index",
    "NASDAQCOM": "NASDAQ Composite Index",
    "DJIA": "Dow Jones Industrial Average",
    "VIXCLS": "CBOE Volatility Index (VIX)",
    # ── Commodities (FRED daily) ──
    "DCOILWTICO": "Crude Oil Prices: West Texas Intermediate (WTI)",
    "DHHNGSP": "Henry Hub Natural Gas Spot Price",
    # ── Currency & exchange rates ──
    "DTWEXBGS": "Trade Weighted U.S. Dollar Index",
    "DEXUSEU": "U.S. / Euro Foreign Exchange Rate",
    "DEXJPUS": "Japan / U.S. Foreign Exchange Rate",
    "DEXUSUK": "U.S. / U.K. Foreign Exchange Rate",
    "DEXCHUS": "China / U.S. Foreign Exchange Rate",
    # ── Inflation & prices ──
    "CPIAUCSL": "Consumer Price Index For All Urban Consumers (All Items)",
    "CPILFESL": "Consumer Price Index Less Food and Energy (Core CPI)",
    "PPIACO": "Producer Price Index (All Commodities)",
    "T10YIE": "10-Year Breakeven Inflation Rate",
    "T5YIE": "5-Year Breakeven Inflation Rate",
    "PCEPI": "Personal Consumption Expenditures: Chain-type Price Index",
    # ── Labor market ──
    "UNRATE": "Unemployment Rate",
    "ICSA": "Initial Claims (Weekly Jobless Claims)",
    "PAYEMS": "All Employees Total Nonfarm (Payrolls)",
    "JTSJOL": "Job Openings: Total Nonfarm (JOLTS)",
    "CES0500000003": "Average Hourly Earnings of All Employees (Total Private)",
    # ── Credit & financial stress ──
    "BAMLH0A0HYM2": "ICE BofA US High Yield Option-Adjusted Spread",
    "BAMLC0A0CM": "ICE BofA US Corporate Master Option-Adjusted Spread",
    "TEDRATE": "TED Spread (3-Month LIBOR minus 3-Month T-Bill)",
    "STLFSI2": "St. Louis Fed Financial Stress Index",
    "NFCI": "Chicago Fed National Financial Conditions Index",
    # ── Economic activity ──
    "INDPRO": "Industrial Production Index",
    "RSAFS": "Advance Retail Sales: Retail and Food Services",
    "UMCSENT": "University of Michigan Consumer Sentiment",
    "TOTALSA": "Total Vehicle Sales",
    "PERMIT": "New Privately-Owned Housing Units Authorized (Building Permits)",
    # ── Housing ──
    "CSUSHPISA": "S&P/Case-Shiller U.S. National Home Price Index",
    "HOUST": "Housing Starts: Total New Privately Owned",
    # ── Money supply & central bank ──
    "M2SL": "M2 Money Stock",
    "BOGMBASE": "Monetary Base; Total",
    "WALCL": "Federal Reserve Total Assets (Balance Sheet)",
    # ── Business lending ──
    "BUSLOANS": "Commercial and Industrial Loans, All Commercial Banks",
}

# ---------------------------------------------------------------------------
# Real estate metros (address anchors for RentCast radius search)
# ---------------------------------------------------------------------------
_ALL_METROS: list[str] = [
    # ── Top 20 (original) ──
    "350 5th Ave, New York, NY 10118",
    "233 S Wacker Dr, Chicago, IL 60606",
    "1000 Vin Scully Ave, Los Angeles, CA 90012",
    "600 Travis St, Houston, TX 77002",
    "400 S Tryon St, Charlotte, NC 28202",
    "100 Peachtree St NW, Atlanta, GA 30303",
    "200 E Las Olas Blvd, Fort Lauderdale, FL 33301",
    "700 2nd Ave S, Nashville, TN 37210",
    "1 N Central Ave, Phoenix, AZ 85004",
    "2001 Ross Ave, Dallas, TX 75201",
    "200 E Colfax Ave, Denver, CO 80203",
    "1 S Broad St, Philadelphia, PA 19107",
    "100 Summer St, Boston, MA 02110",
    "700 5th Ave, Seattle, WA 98104",
    "50 Fremont St, San Francisco, CA 94105",
    "401 E Pratt St, Baltimore, MD 21202",
    "1 S Main St, Salt Lake City, UT 84111",
    "400 S Orange Ave, Orlando, FL 32801",
    "100 NE 2nd Ave, Portland, OR 97232",
    "325 John Knox Rd, Tallahassee, FL 32303",
    # ── 21-40: Large metros ──
    "1 Riverfront Plz, Newark, NJ 07102",
    "100 N Main St, Memphis, TN 38103",
    "200 W Washington St, Indianapolis, IN 46204",
    "100 S Main St, Las Vegas, NV 89101",
    "600 E Market St, San Antonio, TX 78205",
    "200 E Pratt St, Milwaukee, WI 53202",
    "100 N Broadway, Oklahoma City, OK 73102",
    "500 Main St, Louisville, KY 40202",
    "100 N Main St, Richmond, VA 23219",
    "1 S Pinckney St, Madison, WI 53703",
    "200 E Main St, Norfolk, VA 23510",
    "100 W Capitol Ave, Little Rock, AR 72201",
    "100 S Main St, Tulsa, OK 74103",
    "1 Canal St, New Orleans, LA 70130",
    "100 E Capitol St, Jackson, MS 39201",
    "200 W Adams St, Jacksonville, FL 32202",
    "100 N Main St, Wichita, KS 67202",
    "100 State St, Hartford, CT 06103",
    "1 Exchange Pl, Providence, RI 02903",
    "100 N Tryon St, Raleigh, NC 27601",
    # ── 41-60: Mid-size metros ──
    "200 E Main St, Lexington, KY 40507",
    "100 N Main St, Dayton, OH 45402",
    "100 W 10th St, Wilmington, DE 19801",
    "100 S Main St, Akron, OH 44308",
    "200 N Main St, Greenville, SC 29601",
    "100 E Washington St, Boise, ID 83702",
    "1 City Hall Plz, Durham, NC 27701",
    "100 W Trade St, Winston-Salem, NC 27101",
    "100 S Virginia St, Reno, NV 89501",
    "200 E Main St, Chattanooga, TN 37402",
    "100 N Main St, Columbia, SC 29201",
    "1 S Main St, Spokane, WA 99201",
    "100 E Congress St, Tucson, AZ 85701",
    "200 W Markham St, Birmingham, AL 35203",
    "100 S Main St, Omaha, NE 68102",
    "100 W Broad St, Columbus, OH 43215",
    "100 W Michigan Ave, Kalamazoo, MI 49007",
    "200 N Main St, Ann Arbor, MI 48104",
    "100 E 8th St, Cincinnati, OH 45202",
    "100 S 4th St, Minneapolis, MN 55401",
    # ── 61-80: Growing metros ──
    "100 N Main St, Knoxville, TN 37902",
    "200 W Camelback Rd, Scottsdale, AZ 85251",
    "100 S State St, Provo, UT 84601",
    "100 N College Ave, Fort Collins, CO 80524",
    "200 E Main St, Lakeland, FL 33801",
    "100 S Main St, Savannah, GA 31401",
    "100 W Liberty St, Roanoke, VA 24011",
    "200 E Bay St, Charleston, SC 29401",
    "100 N Main St, Greensburg, PA 15601",
    "100 S Palafox St, Pensacola, FL 32502",
    "200 W Capitol Dr, Baton Rouge, LA 70801",
    "100 E Main St, Mesa, AZ 85201",
    "100 N Central Ave, St. Louis, MO 63101",
    "200 Ross St, Pittsburgh, PA 15219",
    "100 Woodward Ave, Detroit, MI 48226",
    "100 W Main St, Bozeman, MT 59715",
    "100 S 1st Ave, Sioux Falls, SD 57104",
    "200 N Main St, Santa Fe, NM 87501",
    "100 N Stone Ave, Albuquerque, NM 87102",
    "100 S Capitol Blvd, Boise, ID 83702",
    # ── 81-100: Smaller / emerging metros ──
    "200 E Main St, Asheville, NC 28801",
    "100 Congress Ave, Austin, TX 78701",
    "200 E Commerce St, San Jose, CA 95113",
    "100 W Flagler St, Miami, FL 33130",
    "200 S Orange Ave, Sarasota, FL 34236",
    "100 N Main St, Gainesville, FL 32601",
    "200 E College Ave, Tallahassee, FL 32301",
    "100 N Main St, Fayetteville, AR 72701",
    "100 E Market St, Des Moines, IA 50309",
    "200 N Main St, McAllen, TX 78501",
    "100 S Broadway, Wichita Falls, TX 76301",
    "100 W Front St, Missoula, MT 59802",
    "200 E Main St, Rapid City, SD 57701",
    "100 N 1st St, Bismarck, ND 58501",
    "200 W Superior St, Duluth, MN 55802",
    "100 E Main St, Rochester, NY 14604",
    "200 S Warren St, Syracuse, NY 13202",
    "100 Main St, Buffalo, NY 14202",
    "200 E State St, Trenton, NJ 08608",
    "100 S Main St, Harrisburg, PA 17101",
]
# For testing: set MAX_METROS to limit (None = all 100)
MAX_METROS: int | None = None
METROS: list[str] = _ALL_METROS[:MAX_METROS] if MAX_METROS else _ALL_METROS
RENTCAST_PROPERTY_TYPES = ["Multi-Family", "Apartment", "Single Family", "Condo", "Townhouse"]
RENTCAST_RADIUS_MILES = 5.0
RENTCAST_MAX_RESULTS = 500  # max properties per endpoint per metro (1 page)

# ---------------------------------------------------------------------------
# Preprocessing (Layer 2)
# ---------------------------------------------------------------------------
# Key metrics to extract from per-ticker financial statement CSVs
INCOME_KEYS: dict[str, str] = {
    "Total Revenue": "stmt_revenue",
    "Net Income": "stmt_net_income",
    "EBITDA": "stmt_ebitda",
    "EBIT": "stmt_ebit",
    "Gross Profit": "stmt_gross_profit",
    "Operating Income": "stmt_operating_income",
    "Basic EPS": "stmt_basic_eps",
    # Valuation inputs (WACC / effective tax rate / cost of debt)
    "Tax Provision": "stmt_tax_provision",
    "Pretax Income": "stmt_pretax_income",
    "Interest Expense": "stmt_interest_expense",
    "Tax Rate For Calcs": "stmt_tax_rate",
    # Income-statement detail items
    "Cost Of Revenue": "stmt_cogs",
    "Operating Expense": "stmt_operating_expenses",
}
BALANCE_KEYS: dict[str, str] = {
    "Total Assets": "stmt_total_assets",
    "Total Liabilities Net Minority Interest": "stmt_total_liabilities",
    "Total Debt": "stmt_total_debt",
    "Total Equity Gross Minority Interest": "stmt_total_equity",
    "Cash And Cash Equivalents": "stmt_cash",
    "Ordinary Shares Number": "stmt_shares_outstanding",
    "Share Issued": "stmt_shares_issued",
    # Balance-sheet detail items
    "Accounts Receivable": "stmt_accounts_receivable",
    "Net Receivables": "stmt_accounts_receivable",
    "Inventory": "stmt_inventory",
    "Current Assets": "stmt_current_assets",
    "Net PPE": "stmt_ppe_net",
    "Goodwill": "stmt_goodwill",
    "Accounts Payable": "stmt_accounts_payable",
    "Current Liabilities": "stmt_current_liabilities",
    "Long Term Debt": "stmt_lt_debt",
}
CASHFLOW_KEYS: dict[str, str] = {
    "Operating Cash Flow": "stmt_operating_cashflow",
    "Free Cash Flow": "stmt_free_cashflow",
    "Capital Expenditure": "stmt_capex",
    "Financing Cash Flow": "stmt_financing_cashflow",
}

# XBRL tag → stmt_ column mapping (SEC EDGAR).
# Each stmt_ column maps to a list of XBRL tags tried in priority order;
# the first non-null value wins.  Tags are US-GAAP concepts reported in
# 10-K / 10-Q filings stored in data/xbrl/parsed/company_facts.parquet.
XBRL_TAG_MAP: dict[str, list[str]] = {
    "stmt_revenue": [
        "Revenues",
        "RevenueFromContractWithCustomerExcludingAssessedTax",
        "SalesRevenueNet",
        "RevenueFromContractWithCustomerIncludingAssessedTax",
        # Banking / Financial Services equivalents
        "InterestAndDividendIncomeOperating",
        "InterestIncomeExpenseNet",
        "NetInterestIncome",
        "NoninterestIncome",
        "FinancialServicesRevenue",
        # Insurance equivalents
        "PremiumsEarnedNet",
        "InsuranceServicesRevenue",
        "PremiumsWrittenNet",
        # IFRS equivalents
        "Revenue",
        "RevenueFromContractsWithCustomers",
    ],
    "stmt_net_income": [
        "NetIncomeLoss",
        # IFRS
        "ProfitLoss",
        "ProfitLossAttributableToOwnersOfParent",
    ],
    "stmt_ebit": [
        "OperatingIncomeLoss",
        # IFRS
        "ProfitLossBeforeFinanceCostsAndTax",
        "OperatingProfitLoss",
    ],
    "stmt_gross_profit": [
        "GrossProfit",
    ],
    "stmt_operating_income": [
        "OperatingIncomeLoss",
        # IFRS
        "ProfitLossFromOperatingActivities",
        "OperatingProfitLoss",
    ],
    "stmt_basic_eps": [
        "EarningsPerShareBasic",
        # IFRS
        "BasicEarningsLossPerShare",
    ],
    "stmt_tax_provision": [
        "IncomeTaxExpenseBenefit",
        # IFRS
        "IncomeTaxExpenseContinuingOperations",
    ],
    "stmt_pretax_income": [
        "IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest",
        # IFRS
        "ProfitLossBeforeTax",
    ],
    "stmt_interest_expense": [
        "InterestExpense",
        # IFRS
        "FinanceCosts",
        "InterestExpenseOnBorrowings",
    ],
    "stmt_operating_cashflow": [
        "NetCashProvidedByUsedInOperatingActivities",
        # IFRS
        "CashFlowsFromUsedInOperatingActivities",
    ],
    "stmt_capex": [
        "PaymentsToAcquirePropertyPlantAndEquipment",
        # IFRS
        "PurchaseOfPropertyPlantAndEquipmentClassifiedAsInvestingActivities",
    ],
    "stmt_total_assets": ["Assets"],
    "stmt_total_liabilities": ["Liabilities"],
    "stmt_total_debt": [
        "LongTermDebt",
        "LongTermDebtNoncurrent",
        # IFRS
        "NoncurrentFinancialLiabilities",
        "BorrowingsNoncurrent",
        "NoncurrentPortionOfNoncurrentBorrowings",
    ],
    "stmt_total_equity": [
        "StockholdersEquity",
        "StockholdersEquityIncludingPortionAttributableToNoncontrollingInterest",
        # IFRS
        "Equity",
        "EquityAttributableToOwnersOfParent",
    ],
    "stmt_cash": [
        "CashAndCashEquivalentsAtCarryingValue",
        "CashCashEquivalentsRestrictedCashAndRestrictedCashEquivalents",
        # IFRS
        "CashAndCashEquivalents",
    ],
    "stmt_shares_outstanding": [
        "CommonStockSharesOutstanding",
        "EntityCommonStockSharesOutstanding",
        # Fallback: weighted-average for dual-class companies (CRWD, DDOG, etc.)
        "WeightedAverageNumberOfSharesOutstandingBasic",
        "WeightedAverageNumberOfDilutedSharesOutstanding",
        "CommonSharesOutstanding",
    ],
    "stmt_shares_issued": [
        "CommonStockSharesIssued",
        # IFRS
        "IssuedCapital",
    ],
    # ── Balance-sheet detail items ──
    "stmt_accounts_receivable": [
        "AccountsReceivableNetCurrent",
        "AccountsReceivableNet",
        # IFRS
        "TradeAndOtherCurrentReceivables",
    ],
    "stmt_inventory": [
        "InventoryNet",
        "Inventories",
        # IFRS
        "CurrentInventories",
    ],
    "stmt_current_assets": [
        "AssetsCurrent",
        # IFRS
        "CurrentAssets",
    ],
    "stmt_ppe_net": [
        "PropertyPlantAndEquipmentNet",
        # IFRS
        "PropertyPlantAndEquipment",
    ],
    "stmt_goodwill": [
        "Goodwill",
        # IFRS
        "GoodwillGross",
    ],
    "stmt_accounts_payable": [
        "AccountsPayableCurrent",
        "AccountsPayable",
        # IFRS
        "TradeAndOtherCurrentPayables",
    ],
    "stmt_current_liabilities": [
        "LiabilitiesCurrent",
        # IFRS
        "CurrentLiabilities",
    ],
    "stmt_lt_debt": [
        "LongTermDebtNoncurrent",
        "LongTermDebt",
        "LongTermDebtAndCapitalLeaseObligations",
        # IFRS
        "NoncurrentFinancialLiabilities",
        "BorrowingsNoncurrent",
    ],
    # ── Income-statement detail items ──
    "stmt_cogs": [
        "CostOfGoodsAndServicesSold",
        "CostOfRevenue",
        "CostOfGoodsSold",
        # IFRS
        "CostOfSales",
    ],
    "stmt_operating_expenses": [
        "OperatingExpenses",
        # IFRS
        "AdministrativeExpense",
    ],
    # ── Cash-flow detail items ──
    "stmt_financing_cashflow": [
        "NetCashProvidedByUsedInFinancingActivities",
        # IFRS
        "CashFlowsFromUsedInFinancingActivities",
    ],
}

# Auxiliary XBRL tags used to derive composite metrics (EBITDA, FCF, tax rate).
XBRL_DA_TAGS: list[str] = [
    "DepreciationDepletionAndAmortization",
    "DepreciationAndAmortization",
    "Depreciation",
    # IFRS
    "DepreciationAmortisationAndImpairmentLossReversalOfImpairmentLossRecognisedInProfitOrLoss",
    "DepreciationAndAmortisationExpense",
]

# ---------------------------------------------------------------------------
# Benchmark assembly (Layer 3)
# ---------------------------------------------------------------------------
# Temporal split configuration.
# Set TEMPORAL_SPLIT_DATE to a fixed date string (e.g. "2024-01-01") to split
# at that exact date, OR set it to None and use TEMPORAL_SPLIT_RATIO instead.
TEMPORAL_SPLIT_DATE: str | None = None

# Train fraction of unique panel dates (e.g. 0.7 = 70% train, 30% test).
# Only used when TEMPORAL_SPLIT_DATE is None.
TEMPORAL_SPLIT_RATIO: float = 0.7

# Forecasting task parameters -- granularity-aware.
# Values are in *panel periods* (not calendar days).
#   daily:   5d≈1w, 21d≈1mo, 63d≈1q, 126d≈6mo, 252d≈1y
#   weekly:  4w≈1mo, 13w≈1q, 26w≈6mo, 52w≈1y
#   monthly: 1mo, 3mo≈1q, 6mo, 12mo≈1y
HORIZONS_BY_GRANULARITY: dict[str, list[int]] = {
    "daily":   [5, 21, 63, 126, 252],
    "weekly":  [4, 13, 26, 52],
    "monthly": [1, 3, 6, 12],
}
LOOKBACK_WINDOWS_BY_GRANULARITY: dict[str, list[int]] = {
    "daily":   [63, 126, 252],
    "weekly":  [13, 26, 52],
    "monthly": [3, 6, 12],
}

# Legacy flat aliases (default granularity) -- prefer the dicts above.
HORIZONS: list[int] = HORIZONS_BY_GRANULARITY[GRANULARITY]
LOOKBACK_WINDOWS: list[int] = LOOKBACK_WINDOWS_BY_GRANULARITY[GRANULARITY]


def get_horizons(granularity: str | None = None) -> list[int]:
    """Return forecast horizons for *granularity*."""
    return HORIZONS_BY_GRANULARITY[granularity or GRANULARITY]


def get_lookback_windows(granularity: str | None = None) -> list[int]:
    """Return lookback windows for *granularity*."""
    return LOOKBACK_WINDOWS_BY_GRANULARITY[granularity or GRANULARITY]

# ---------------------------------------------------------------------------
# Scenario detection thresholds (Layer 3 -- generate_scenarios.py)
# ---------------------------------------------------------------------------
# Fed funds: minimum absolute change in rate (percentage points) between
# consecutive monthly observations to flag as a rate-change event.
SCENARIO_FEDFUNDS_DELTA = 0.25  # 25 bps

# VIX: spike ratio -- current value / rolling mean must exceed this.
SCENARIO_VIX_SPIKE_RATIO = 1.4
SCENARIO_VIX_ROLLING_WINDOW = 63  # observations (daily)

# Oil (EIA commodity or FRED DCOILWTICO): pct move over rolling window.
SCENARIO_OIL_PCT_CHANGE = 0.09  # 9 %
SCENARIO_OIL_ROLLING_WINDOW = 21  # observations (daily)

# Natural gas: minimum percentage move over a rolling window.
SCENARIO_NATGAS_PCT_CHANGE = 0.15  # 15 %
SCENARIO_NATGAS_ROLLING_WINDOW = 4  # observations (weekly data)

# Market drawdown: minimum percentage drop in S&P 500 over a rolling window.
SCENARIO_SP500_DRAWDOWN = 0.025  # 2.5 %
SCENARIO_SP500_ROLLING_WINDOW = 21  # observations (daily)

# NASDAQ: minimum percentage move (crash or rally divergence).
SCENARIO_NASDAQ_PCT_CHANGE = 0.045  # 4.5 %
SCENARIO_NASDAQ_ROLLING_WINDOW = 21  # observations (daily)

# Yield curve: DGS10 - DGS2 spread thresholds.
SCENARIO_YIELD_CURVE_INVERSION = 0.0  # spread crosses below 0 = inversion
SCENARIO_YIELD_CURVE_STEEPENING = 0.50  # spread widens by ≥ 50bps over window
SCENARIO_YIELD_CURVE_WINDOW = 63  # observations (daily)

# Treasury rate (DGS10): large absolute move in 10-year yield.
SCENARIO_DGS10_DELTA = 0.45  # 45 bps move over window
SCENARIO_DGS10_ROLLING_WINDOW = 21  # observations (daily)

# USD index (DTWEXBGS): large percentage move in trade-weighted dollar.
SCENARIO_USD_PCT_CHANGE = 0.025  # 2.5 %
SCENARIO_USD_ROLLING_WINDOW = 21  # observations (daily)

# CPI / Inflation: large month-over-month change in annualized rate.
SCENARIO_CPI_MOM_THRESHOLD = 0.004  # 0.4% month-over-month (≈4.8% annualized)

# PPI: large month-over-month change.
SCENARIO_PPI_MOM_THRESHOLD = 0.01  # 1% month-over-month

# Unemployment: jump in rate between consecutive observations.
SCENARIO_UNRATE_DELTA = 0.3  # 30 bps increase

# Jobless claims (ICSA): spike ratio vs rolling mean.
SCENARIO_ICSA_SPIKE_RATIO = 1.3
SCENARIO_ICSA_ROLLING_WINDOW = 8  # observations (weekly)

# Payrolls (PAYEMS): large month-over-month change in thousands.
SCENARIO_PAYROLLS_DELTA = 0.002  # 0.2% month-over-month change

# High-yield credit spread: large move over rolling window.
SCENARIO_HY_SPREAD_DELTA = 1.0  # 100 bps widening/tightening over window
SCENARIO_HY_SPREAD_WINDOW = 21  # observations (daily)

# IG corporate spread: large move over rolling window.
SCENARIO_IG_SPREAD_DELTA = 0.30  # 30 bps over window
SCENARIO_IG_SPREAD_WINDOW = 21

# TED spread: spike above threshold.
SCENARIO_TED_SPIKE = 0.50  # 50 bps

# Financial stress index: large move.
SCENARIO_FSI_THRESHOLD = 1.0  # standard deviation units (index is z-scored)

# Mortgage rate: large move over rolling window.
SCENARIO_MORTGAGE_DELTA = 0.50  # 50 bps move over window
SCENARIO_MORTGAGE_ROLLING_WINDOW = 4  # observations (weekly)

# Consumer sentiment (UMCSENT): large drop.
SCENARIO_SENTIMENT_PCT_CHANGE = 0.10  # 10% drop
SCENARIO_SENTIMENT_ROLLING_WINDOW = 2  # observations (monthly)

# Industrial production: large month-over-month change.
SCENARIO_INDPRO_PCT_CHANGE = 0.01  # 1% month-over-month

# Retail sales: large month-over-month change.
SCENARIO_RETAIL_PCT_CHANGE = 0.02  # 2% month-over-month

# Housing starts: large month-over-month change.
SCENARIO_HOUSING_PCT_CHANGE = 0.10  # 10% month-over-month

# Home prices (Case-Shiller): year-over-year deceleration/acceleration.
SCENARIO_HOME_PRICE_YOY_DELTA = 0.03  # 3pp change in YoY rate

# Money supply (M2): year-over-year contraction.
SCENARIO_M2_YOY_THRESHOLD = -0.01  # YoY growth below -1% (contraction)

# 30-year Treasury: large move.
SCENARIO_DGS30_DELTA = 0.50  # 50 bps over window
SCENARIO_DGS30_ROLLING_WINDOW = 21

# Cross-asset: S&P 500 vs NASDAQ divergence.
SCENARIO_SP_NASDAQ_DIVERGENCE = 0.05  # 5% divergence over window
SCENARIO_SP_NASDAQ_WINDOW = 21

# VIX regime: sustained elevated volatility.
SCENARIO_VIX_REGIME_THRESHOLD = 25.0  # VIX above 25
SCENARIO_VIX_REGIME_MIN_DAYS = 10  # sustained for at least 10 days

# ── NEW: Major FX pair shocks (EUR, JPY, GBP, CNY) ──
SCENARIO_FX_PCT_CHANGE = 0.03         # 3% move over window
SCENARIO_FX_ROLLING_WINDOW = 21

# ── NEW: Breakeven inflation shocks (T10YIE, T5YIE) ──
SCENARIO_BEI_DELTA = 0.30             # 30 bps move over window
SCENARIO_BEI_ROLLING_WINDOW = 21

# ── NEW: DJIA large moves ──
SCENARIO_DJIA_PCT_CHANGE = 0.03       # 3% move over window
SCENARIO_DJIA_ROLLING_WINDOW = 21

# ── NEW: JOLTS job openings ──
SCENARIO_JOLTS_PCT_CHANGE = 0.05      # 5% month-over-month change
SCENARIO_JOLTS_DEDUP_DAYS = 28

# ── NEW: Average hourly earnings ──
SCENARIO_EARNINGS_MOM_THRESHOLD = 0.005  # 0.5% month-over-month

# ── NEW: Vehicle sales ──
SCENARIO_VEHICLE_PCT_CHANGE = 0.08    # 8% month-over-month

# ── NEW: Building permits ──
SCENARIO_PERMIT_PCT_CHANGE = 0.08     # 8% month-over-month

# ── NEW: Existing home sales ──
SCENARIO_EXISTING_HOME_SALES_PCT = 0.05  # 5% month-over-month

# ── NEW: Chicago Fed NFCI ──
SCENARIO_NFCI_THRESHOLD = 0.0         # NFCI crosses above 0 (tighter than avg)

# ── NEW: Fed balance sheet (WALCL) ──
SCENARIO_FED_BS_PCT_CHANGE = 0.05     # 5% change over window (quarterly)
SCENARIO_FED_BS_ROLLING_WINDOW = 13   # ~quarterly for weekly data

# ── NEW: Monetary base (BOGMBASE) ──
SCENARIO_MONETARY_BASE_PCT = 0.05     # 5% month-over-month

# ── NEW: Business loans (BUSLOANS) ──
SCENARIO_BUSLOANS_PCT_CHANGE = 0.02   # 2% month-over-month

# ── NEW: PCE inflation ──
SCENARIO_PCEPI_MOM_THRESHOLD = 0.004  # 0.4% month-over-month

# ── NEW: SOFR rate shocks ──
SCENARIO_SOFR_DELTA = 0.25            # 25 bps move
SCENARIO_SOFR_WINDOW = 10             # observations

# ── NEW: Cross-asset composites ──
# Real yield: DGS10 - T10YIE (breakeven inflation)
SCENARIO_REAL_YIELD_DELTA = 0.40      # 40 bps change in real yield
SCENARIO_REAL_YIELD_WINDOW = 21
# Credit compression: HY spread minus IG spread
SCENARIO_CREDIT_COMPRESSION_DELTA = 0.75  # 75 bps change
SCENARIO_CREDIT_COMPRESSION_WINDOW = 21
# Term premium: DGS30 - DGS2
SCENARIO_TERM_PREMIUM_DELTA = 0.50    # 50 bps change
SCENARIO_TERM_PREMIUM_WINDOW = 21
# ── NEW: Short-term shock windows (5-day) for daily series ──
SCENARIO_SP500_SHORT_DRAWDOWN = 0.03  # 3% over 5 days (acute crash)
SCENARIO_SP500_SHORT_WINDOW = 5
SCENARIO_NASDAQ_SHORT_PCT = 0.04      # 4% over 5 days
SCENARIO_NASDAQ_SHORT_WINDOW = 5
SCENARIO_OIL_SHORT_PCT = 0.08         # 8% over 5 days
SCENARIO_OIL_SHORT_WINDOW = 5
SCENARIO_DGS10_SHORT_DELTA = 0.25     # 25 bps over 5 days
SCENARIO_DGS10_SHORT_WINDOW = 5

# Pre/post event windows for scenario context (calendar days).
SCENARIO_PRE_WINDOW_DAYS = 63
SCENARIO_POST_WINDOW_DAYS = 63

# ---------------------------------------------------------------------------
# News collection (Layer 1 -- collect_news.py, Step 10)
# ---------------------------------------------------------------------------
NEWS_WORKERS = 4          # ThreadPoolExecutor parallelism for yfinance news
NEWS_PER_TICKER_COUNT = 50  # articles per ticker per tab (news / press releases)
NEWS_SCENARIO_LIMIT = 10   # Firecrawl results per scenario event
NEWS_RATE_LIMIT_SEC = 1.0  # seconds between API calls

# ---------------------------------------------------------------------------
# Synthetic property generation (agents/synthetic_re/)
# ---------------------------------------------------------------------------
COMMERCIAL_RE_TYPES = ["Office", "Retail", "Industrial", "Mixed-Use"]
COMMERCIAL_RE_SEED_LIMIT = 20  # Firecrawl results per type per metro

# ---------------------------------------------------------------------------
# Valuation (agents/valuation/)
# ---------------------------------------------------------------------------
VALUATION_DIR = DATA_DIR / "valuation"

# DCF parameters
DCF_PROJECTION_YEARS = 5
DCF_TERMINAL_GROWTH_DEFAULT = 0.025   # 2.5% long-term GDP growth
MARKET_RISK_PREMIUM = 0.06            # 6% historical equity risk premium
BETA_LOOKBACK_DAYS = 252              # 1 year of trading days for rolling beta

# Comparable company analysis
COMPS_MAX_PEERS = 10
COMPS_MARKET_CAP_BAND = 0.5           # +/- 50 % for peer filtering by size

# Valuation benchmark
VALUATION_BENCHMARK_TASKS = [
    "valuation_accuracy",              # Task A: estimate intrinsic value
    "statement_generation",            # Task B: generate plausible financials
    "scenario_forecast",               # Task C: forecast impact of what-if
]
VALUATION_HOLDOUT_RATIO = 0.3          # 30 % of tickers held out for eval (Hwang: 50/50 or 70/30)

# ---------------------------------------------------------------------------
# XBRL collection & ontology (Layer 1 -- collected via collect_filings.py)
# ---------------------------------------------------------------------------
# SEC XBRL API base URL (no auth, just User-Agent required)
XBRL_COMPANY_FACTS_URL = "https://data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json"
XBRL_WORKERS = 8           # asyncio.Semaphore concurrency
XBRL_RATE_LIMIT_SEC = 0.12 # seconds between requests (≤10 req/s SEC limit)
# Filing forms to include in ontology extraction.
# Policy: include EVERY form on which SEC accepts XBRL facts from our universe
# (enumerated from raw responses — 37 distinct forms). Do not gate the
# benchmark by form type: the parser keeps everything SEC deems a valid
# XBRL-bearing filing, and downstream preprocessing picks the latest value
# per (ticker, tag, unit) regardless of form.
XBRL_FORMS: list[str] = [
    # US domestic periodic statements
    "10-K", "10-K/A", "10-Q", "10-Q/A",
    "10-KT", "10-KT/A", "10-QT",  # fiscal-year transition period filings
    # Foreign private issuer periodic (file US-GAAP or IFRS via these)
    "20-F", "20-F/A", "40-F", "40-F/A", "6-K", "6-K/A",
    # Current / event reports (earnings releases often carry full financials)
    "8-K", "8-K/A",
    # Registration statements — IPO, shelf, M&A, employee plans
    "S-1", "S-1/A", "S-1MEF",
    "F-1/A", "F-1MEF",
    "S-3", "S-3ASR",
    "S-4", "S-4/A",
    "S-8",
    "POS AM",
    # Investment company filings (cef / invest taxonomy)
    "N-CSR", "N-2",
    # Prospectus supplements
    "424B2", "424B5", "424B7",
    # Proxy statements
    "DEF 14A", "PRE 14A", "DEFR14A", "DEFC14A", "PREM14A",
    # Tender offers
    "SC TO-I",
]
# Ontology classification thresholds (fraction of companies in an industry)
XBRL_CORE_THRESHOLD = 0.70     # tag appears in ≥70% → core
XBRL_COMMON_THRESHOLD = 0.30   # tag appears in ≥30% → common (else extension)