"""Layer 3 – Step 9: Detect natural-experiment scenarios from raw macro data.

Reads raw CSVs from ``data/macro/`` (NOT the processed panel) and identifies
historically significant macro events.  Scenarios are granularity-independent
calendar-date events.

Output: ``data/benchmark/{granularity}/scenarios.parquet``

Uses all available FRED series + EIA commodity data to detect 49 event types
covering rates, equity, commodities, FX, inflation, labor, credit, housing,
money supply, financial conditions, and cross-asset composite signals.
Short-term (5-day) and medium-term (21-day) windows are used for daily series.
"""

from __future__ import annotations

import logging
from pathlib import Path

import numpy as np
import pandas as pd

from . import config

logger = logging.getLogger(__name__)


# ------------------------------------------------------------------
# Helpers
# ------------------------------------------------------------------

def _load_fred(series_id: str) -> pd.DataFrame:
    """Load a single FRED CSV, returning (date, value) DataFrame."""
    path = config.MACRO_DIR / f"fred_{series_id}.csv"
    if not path.exists():
        return pd.DataFrame(columns=["date", "value"])
    df = pd.read_csv(path)
    if "date" not in df.columns:
        return pd.DataFrame(columns=["date", "value"])
    df["date"] = pd.to_datetime(df["date"])
    non_date = [c for c in df.columns if c != "date"]
    if not non_date:
        return pd.DataFrame(columns=["date", "value"])
    val_col = series_id if series_id in df.columns else non_date[0]
    df = df[["date", val_col]].rename(columns={val_col: "value"})
    df["value"] = pd.to_numeric(df["value"], errors="coerce")
    return df.dropna(subset=["value"]).sort_values("date").reset_index(drop=True)


def _load_commodity_spot(subdir: str, candidates: list[str]) -> pd.DataFrame:
    """Load a commodity spot CSV from a macro subdirectory."""
    commodity_dir = config.MACRO_DIR / subdir
    if not commodity_dir.is_dir():
        return pd.DataFrame(columns=["date", "value"])
    for candidate in candidates:
        path = commodity_dir / candidate
        if not path.exists():
            continue
        df = pd.read_csv(path)
        date_col = next((c for c in df.columns if "time" in c.lower() or "date" in c.lower()), None)
        if date_col is None:
            continue
        num_cols = df.select_dtypes(include="number").columns.tolist()
        val_col = next((c for c in df.columns if c != date_col and "spot" in c.lower()), None)
        if val_col is None and num_cols:
            val_col = num_cols[0]
        if val_col is None:
            continue
        df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
        df = df[[date_col, val_col]].rename(columns={date_col: "date", val_col: "value"})
        df["value"] = pd.to_numeric(df["value"], errors="coerce")
        return df.dropna(subset=["value"]).sort_values("date").reset_index(drop=True)
    return pd.DataFrame(columns=["date", "value"])


def _load_crude_spot() -> pd.DataFrame:
    return _load_commodity_spot("crude_oil", ["crude_spot_daily.csv"])


def _load_natgas_spot() -> pd.DataFrame:
    return _load_commodity_spot("natural_gas", [
        "natural_gas_spot_weekly.csv",
        "natural_gas_spot_daily.csv",
    ])


# ------------------------------------------------------------------
# Detectors
# ------------------------------------------------------------------

def _detect_fed_rate_changes(df: pd.DataFrame) -> list[dict]:
    """Detect FEDFUNDS changes >= SCENARIO_FEDFUNDS_DELTA between consecutive observations."""
    if df.empty:
        return []
    events = []
    delta = config.SCENARIO_FEDFUNDS_DELTA
    prev_val = df["value"].iloc[0]
    for _, row in df.iloc[1:].iterrows():
        change = row["value"] - prev_val
        if abs(change) >= delta:
            direction = "raised" if change > 0 else "lowered"
            events.append({
                "event_type": "fed_rate_change",
                "event_date": row["date"],
                "event_description": (
                    f"On {row['date'].date()}, the Fed {direction} rates by "
                    f"{abs(change)*100:.0f}bps to {row['value']:.2f}%."
                ),
            })
        prev_val = row["value"]
    return events


def _detect_vix_spikes(df: pd.DataFrame) -> list[dict]:
    """Detect VIX > ratio * rolling mean."""
    if len(df) < config.SCENARIO_VIX_ROLLING_WINDOW:
        return []
    events = []
    ratio = config.SCENARIO_VIX_SPIKE_RATIO
    window = config.SCENARIO_VIX_ROLLING_WINDOW
    df = df.copy()
    df["rolling_mean"] = df["value"].rolling(window, min_periods=window).mean()
    df = df.dropna(subset=["rolling_mean"])
    spike_mask = df["value"] > ratio * df["rolling_mean"]
    # Group consecutive spike days; take the first day of each group
    if spike_mask.any():
        spike_idx = spike_mask[spike_mask].index
        groups: list[list[int]] = []
        current: list[int] = [spike_idx[0]]
        for i in spike_idx[1:]:
            if i == current[-1] + 1:
                current.append(i)
            else:
                groups.append(current)
                current = [i]
        groups.append(current)
        for g in groups:
            row = df.loc[g[0]]
            events.append({
                "event_type": "vix_spike",
                "event_date": row["date"],
                "event_description": (
                    f"On {row['date'].date()}, VIX spiked to {row['value']:.1f} "
                    f"({row['value']/row['rolling_mean']:.1f}x its {window}-day average of "
                    f"{row['rolling_mean']:.1f})."
                ),
            })
    return events


def _detect_oil_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect crude-oil moves >= threshold over rolling window."""
    if len(df) < config.SCENARIO_OIL_ROLLING_WINDOW:
        return []
    events = []
    window = config.SCENARIO_OIL_ROLLING_WINDOW
    pct = config.SCENARIO_OIL_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return events
    # De-duplicate: keep events at least `window` days apart
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "surged" if row["pct_change"] > 0 else "plunged"
        events.append({
            "event_type": "oil_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, crude oil {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over the prior {window} trading days "
                f"to ${row['value']:.2f}/bbl."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_market_drawdowns(df: pd.DataFrame) -> list[dict]:
    """Detect S&P 500 drops >= threshold over rolling window."""
    if len(df) < config.SCENARIO_SP500_ROLLING_WINDOW:
        return []
    events = []
    window = config.SCENARIO_SP500_ROLLING_WINDOW
    pct = config.SCENARIO_SP500_DRAWDOWN
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    drops = df[df["pct_change"] <= -pct].copy()
    if drops.empty:
        return events
    prev_date = None
    for _, row in drops.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        events.append({
            "event_type": "market_drawdown",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the S&P 500 dropped "
                f"{abs(row['pct_change'])*100:.1f}% over the prior {window} trading days "
                f"to {row['value']:.0f}."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_natgas_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect natural-gas spot moves >= threshold over rolling window."""
    window = config.SCENARIO_NATGAS_ROLLING_WINDOW
    if len(df) < window:
        return []
    pct = config.SCENARIO_NATGAS_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window * 7:
            continue
        direction = "surged" if row["pct_change"] > 0 else "plunged"
        events.append({
            "event_type": "natgas_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, natural gas {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over the prior {window} periods "
                f"to ${row['value']:.2f}/MMBtu."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_nasdaq_moves(df: pd.DataFrame) -> list[dict]:
    """Detect NASDAQ large moves (crashes or rallies) over rolling window."""
    window = config.SCENARIO_NASDAQ_ROLLING_WINDOW
    if len(df) < window:
        return []
    pct = config.SCENARIO_NASDAQ_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "rallied" if row["pct_change"] > 0 else "dropped"
        events.append({
            "event_type": "nasdaq_move",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the NASDAQ Composite {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over the prior {window} trading days "
                f"to {row['value']:.0f}."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_yield_curve_events(dgs10: pd.DataFrame, dgs2: pd.DataFrame) -> list[dict]:
    """Detect yield curve inversions and steep re-steepening events."""
    if dgs10.empty or dgs2.empty:
        return []
    merged = pd.merge(dgs10, dgs2, on="date", suffixes=("_10y", "_2y"))
    if merged.empty:
        return []
    merged = merged.sort_values("date").reset_index(drop=True)
    merged["spread"] = merged["value_10y"] - merged["value_2y"]

    window = config.SCENARIO_YIELD_CURVE_WINDOW
    events = []

    # Detect inversions: spread crosses below 0
    merged["prev_spread"] = merged["spread"].shift(1)
    inversions = merged[
        (merged["spread"] < config.SCENARIO_YIELD_CURVE_INVERSION) &
        (merged["prev_spread"] >= config.SCENARIO_YIELD_CURVE_INVERSION)
    ]
    prev_date = None
    for _, row in inversions.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        events.append({
            "event_type": "yield_curve_event",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the yield curve inverted: "
                f"10Y-2Y spread fell to {row['spread']*100:.0f}bps "
                f"(10Y={row['value_10y']:.2f}%, 2Y={row['value_2y']:.2f}%)."
            ),
        })
        prev_date = row["date"]

    # Detect un-inversions: spread crosses back above 0
    un_inversions = merged[
        (merged["spread"] >= config.SCENARIO_YIELD_CURVE_INVERSION) &
        (merged["prev_spread"] < config.SCENARIO_YIELD_CURVE_INVERSION)
    ]
    prev_date = None
    for _, row in un_inversions.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        events.append({
            "event_type": "yield_curve_event",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the yield curve un-inverted: "
                f"10Y-2Y spread recovered to {row['spread']*100:.0f}bps "
                f"(10Y={row['value_10y']:.2f}%, 2Y={row['value_2y']:.2f}%)."
            ),
        })
        prev_date = row["date"]

    # Detect large steepening/flattening moves
    if len(merged) > window:
        merged["spread_change"] = merged["spread"] - merged["spread"].shift(window)
        threshold = config.SCENARIO_YIELD_CURVE_STEEPENING
        large = merged[merged["spread_change"].abs() >= threshold].dropna(subset=["spread_change"])
        prev_date = None
        for _, row in large.iterrows():
            if prev_date is not None and (row["date"] - prev_date).days < window:
                continue
            direction = "steepened" if row["spread_change"] > 0 else "flattened"
            events.append({
                "event_type": "yield_curve_event",
                "event_date": row["date"],
                "event_description": (
                    f"On {row['date'].date()}, the yield curve {direction} by "
                    f"{abs(row['spread_change'])*100:.0f}bps over {window} days: "
                    f"10Y-2Y spread at {row['spread']*100:.0f}bps."
                ),
            })
            prev_date = row["date"]

    return events


def _detect_treasury_rate_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large moves in the 10-year Treasury yield."""
    window = config.SCENARIO_DGS10_ROLLING_WINDOW
    if len(df) < window:
        return []
    delta = config.SCENARIO_DGS10_DELTA
    df = df.copy()
    df["abs_change"] = df["value"] - df["value"].shift(window)
    large = df[df["abs_change"].abs() >= delta].dropna(subset=["abs_change"])
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "surged" if row["abs_change"] > 0 else "plunged"
        events.append({
            "event_type": "treasury_rate_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the 10-year Treasury yield {direction} "
                f"{abs(row['abs_change'])*100:.0f}bps over {window} trading days "
                f"to {row['value']:.2f}%."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_usd_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large moves in the trade-weighted USD index."""
    window = config.SCENARIO_USD_ROLLING_WINDOW
    if len(df) < window:
        return []
    pct = config.SCENARIO_USD_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "strengthened" if row["pct_change"] > 0 else "weakened"
        events.append({
            "event_type": "usd_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the trade-weighted USD {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over {window} trading days "
                f"to {row['value']:.1f}."
            ),
        })
        prev_date = row["date"]
    return events


# ------------------------------------------------------------------
# Generic helpers for monthly / weekly series
# ------------------------------------------------------------------

def _detect_mom_change(df: pd.DataFrame, event_type: str, label: str,
                       threshold: float, unit: str = "", fmt: str = ".1f",
                       de_dup_days: int = 28) -> list[dict]:
    """Generic month-over-month percentage change detector."""
    if len(df) < 2:
        return []
    df = df.copy()
    df["pct_change"] = df["value"].pct_change()
    large = df[df["pct_change"].abs() >= threshold].dropna(subset=["pct_change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < de_dup_days:
            continue
        direction = "jumped" if row["pct_change"] > 0 else "dropped"
        events.append({
            "event_type": event_type,
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, {label} {direction} "
                f"{abs(row['pct_change'])*100:{fmt}}% month-over-month "
                f"to {row['value']:{fmt}}{unit}."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_level_change(df: pd.DataFrame, event_type: str, label: str,
                         delta: float, window: int, unit: str = "%",
                         de_dup_days: int | None = None) -> list[dict]:
    """Generic absolute level change detector over a rolling window."""
    if len(df) < window:
        return []
    de_dup = de_dup_days or window
    df = df.copy()
    df["abs_change"] = df["value"] - df["value"].shift(window)
    large = df[df["abs_change"].abs() >= delta].dropna(subset=["abs_change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < de_dup:
            continue
        direction = "surged" if row["abs_change"] > 0 else "plunged"
        events.append({
            "event_type": event_type,
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, {label} {direction} "
                f"{abs(row['abs_change'])*100:.0f}bps over {window} periods "
                f"to {row['value']:.2f}{unit}."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_spike_ratio(df: pd.DataFrame, event_type: str, label: str,
                        ratio: float, window: int, unit: str = "",
                        de_dup_days: int | None = None) -> list[dict]:
    """Generic spike detector: value > ratio * rolling mean."""
    if len(df) < window:
        return []
    de_dup = de_dup_days or window * 7
    df = df.copy()
    df["rolling_mean"] = df["value"].rolling(window, min_periods=window).mean()
    df = df.dropna(subset=["rolling_mean"])
    spike_mask = df["value"] > ratio * df["rolling_mean"]
    if not spike_mask.any():
        return []
    events = []
    spike_df = df[spike_mask]
    prev_date = None
    for _, row in spike_df.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < de_dup:
            continue
        events.append({
            "event_type": event_type,
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, {label} spiked to {row['value']:.0f}{unit} "
                f"({row['value']/row['rolling_mean']:.1f}x its {window}-period average "
                f"of {row['rolling_mean']:.0f}{unit})."
            ),
        })
        prev_date = row["date"]
    return events


# ------------------------------------------------------------------
# New detectors: inflation, labor, credit, housing, etc.
# ------------------------------------------------------------------

def _detect_cpi_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large CPI month-over-month changes."""
    return _detect_mom_change(df, "inflation_shock", "CPI",
                              config.SCENARIO_CPI_MOM_THRESHOLD, fmt=".2f")


def _detect_ppi_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large PPI month-over-month changes."""
    return _detect_mom_change(df, "ppi_shock", "PPI",
                              config.SCENARIO_PPI_MOM_THRESHOLD, fmt=".1f")


def _detect_unemployment_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect unemployment rate jumps."""
    if len(df) < 2:
        return []
    df = df.copy()
    df["change"] = df["value"].diff()
    large = df[df["change"].abs() >= config.SCENARIO_UNRATE_DELTA].dropna(subset=["change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 28:
            continue
        direction = "rose" if row["change"] > 0 else "fell"
        events.append({
            "event_type": "unemployment_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the unemployment rate {direction} "
                f"{abs(row['change']):.1f}pp to {row['value']:.1f}%."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_jobless_claims_spikes(df: pd.DataFrame) -> list[dict]:
    """Detect spikes in initial jobless claims."""
    return _detect_spike_ratio(df, "jobless_claims_spike", "initial jobless claims",
                               config.SCENARIO_ICSA_SPIKE_RATIO,
                               config.SCENARIO_ICSA_ROLLING_WINDOW,
                               unit="K", de_dup_days=28)


def _detect_payroll_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in nonfarm payrolls."""
    return _detect_mom_change(df, "payroll_shock", "nonfarm payrolls",
                              config.SCENARIO_PAYROLLS_DELTA, fmt=".1f")


def _detect_hy_spread_events(df: pd.DataFrame) -> list[dict]:
    """Detect high-yield credit spread blow-outs."""
    return _detect_level_change(df, "hy_spread_event", "the high-yield credit spread",
                                config.SCENARIO_HY_SPREAD_DELTA,
                                config.SCENARIO_HY_SPREAD_WINDOW)


def _detect_ig_spread_events(df: pd.DataFrame) -> list[dict]:
    """Detect investment-grade corporate spread moves."""
    return _detect_level_change(df, "ig_spread_event", "the IG corporate spread",
                                config.SCENARIO_IG_SPREAD_DELTA,
                                config.SCENARIO_IG_SPREAD_WINDOW)


def _detect_ted_spread_spikes(df: pd.DataFrame) -> list[dict]:
    """Detect TED spread crossing above threshold."""
    if df.empty:
        return []
    df = df.copy()
    df["prev"] = df["value"].shift(1)
    crossings = df[(df["value"] >= config.SCENARIO_TED_SPIKE) &
                   (df["prev"] < config.SCENARIO_TED_SPIKE)].dropna(subset=["prev"])
    events = []
    prev_date = None
    for _, row in crossings.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 30:
            continue
        events.append({
            "event_type": "ted_spread_spike",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the TED spread spiked to "
                f"{row['value']*100:.0f}bps, signaling interbank stress."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_financial_stress(df: pd.DataFrame) -> list[dict]:
    """Detect financial stress index exceeding threshold."""
    if df.empty:
        return []
    threshold = config.SCENARIO_FSI_THRESHOLD
    df = df.copy()
    df["prev"] = df["value"].shift(1)
    crossings = df[(df["value"] >= threshold) &
                   (df["prev"] < threshold)].dropna(subset=["prev"])
    events = []
    prev_date = None
    for _, row in crossings.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 60:
            continue
        events.append({
            "event_type": "financial_stress",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the St. Louis Fed Financial Stress Index "
                f"rose to {row['value']:.2f}, indicating elevated systemic stress."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_mortgage_rate_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large moves in 30-year mortgage rates."""
    return _detect_level_change(df, "mortgage_rate_shock", "the 30-year mortgage rate",
                                config.SCENARIO_MORTGAGE_DELTA,
                                config.SCENARIO_MORTGAGE_ROLLING_WINDOW)


def _detect_sentiment_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large drops in consumer sentiment."""
    if len(df) < config.SCENARIO_SENTIMENT_ROLLING_WINDOW + 1:
        return []
    df = df.copy()
    w = config.SCENARIO_SENTIMENT_ROLLING_WINDOW
    df["pct_change"] = df["value"].pct_change(periods=w)
    large = df[df["pct_change"].abs() >= config.SCENARIO_SENTIMENT_PCT_CHANGE].dropna(subset=["pct_change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 28:
            continue
        direction = "surged" if row["pct_change"] > 0 else "plunged"
        events.append({
            "event_type": "sentiment_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, U. of Michigan Consumer Sentiment {direction} "
                f"{abs(row['pct_change'])*100:.1f}% to {row['value']:.1f}."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_industrial_production_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large changes in industrial production."""
    return _detect_mom_change(df, "industrial_production_shock", "industrial production",
                              config.SCENARIO_INDPRO_PCT_CHANGE, fmt=".1f")


def _detect_retail_sales_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large changes in retail sales."""
    return _detect_mom_change(df, "retail_sales_shock", "retail sales",
                              config.SCENARIO_RETAIL_PCT_CHANGE,
                              unit="B", fmt=".0f")


def _detect_housing_starts_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large changes in housing starts."""
    return _detect_mom_change(df, "housing_starts_shock", "housing starts",
                              config.SCENARIO_HOUSING_PCT_CHANGE, fmt=".0f",
                              de_dup_days=28)


def _detect_home_price_events(df: pd.DataFrame) -> list[dict]:
    """Detect Case-Shiller home price acceleration/deceleration."""
    if len(df) < 13:
        return []
    df = df.copy()
    df["yoy"] = df["value"].pct_change(periods=12)
    df["yoy_change"] = df["yoy"] - df["yoy"].shift(3)
    large = df[df["yoy_change"].abs() >= config.SCENARIO_HOME_PRICE_YOY_DELTA].dropna(subset=["yoy_change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 60:
            continue
        direction = "accelerated" if row["yoy_change"] > 0 else "decelerated"
        events.append({
            "event_type": "home_price_event",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, U.S. home price growth {direction}: "
                f"YoY rate shifted {row['yoy_change']*100:+.1f}pp to "
                f"{row['yoy']*100:.1f}% (Case-Shiller index at {row['value']:.1f})."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_m2_events(df: pd.DataFrame) -> list[dict]:
    """Detect M2 money supply contraction or surge."""
    if len(df) < 13:
        return []
    df = df.copy()
    df["yoy"] = df["value"].pct_change(periods=12)
    events = []
    prev_date = None
    # Detect contraction
    contracting = df[df["yoy"] <= config.SCENARIO_M2_YOY_THRESHOLD].dropna(subset=["yoy"])
    for _, row in contracting.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 60:
            continue
        events.append({
            "event_type": "m2_contraction",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, M2 money supply contracted "
                f"{abs(row['yoy'])*100:.1f}% year-over-year to "
                f"${row['value']/1e6:.2f}T, a rare monetary tightening signal."
            ),
        })
        prev_date = row["date"]
    # Detect surges (>10% YoY)
    prev_date = None
    surging = df[df["yoy"] >= 0.10].dropna(subset=["yoy"])
    for _, row in surging.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 60:
            continue
        events.append({
            "event_type": "m2_surge",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, M2 money supply surged "
                f"{row['yoy']*100:.1f}% year-over-year to "
                f"${row['value']/1e6:.2f}T, signaling aggressive monetary expansion."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_dgs30_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large moves in the 30-year Treasury yield."""
    return _detect_level_change(df, "long_bond_shock", "the 30-year Treasury yield",
                                config.SCENARIO_DGS30_DELTA,
                                config.SCENARIO_DGS30_ROLLING_WINDOW)


def _detect_sp_nasdaq_divergence(sp: pd.DataFrame, nq: pd.DataFrame) -> list[dict]:
    """Detect S&P 500 vs NASDAQ divergence (sector rotation signals)."""
    if sp.empty or nq.empty:
        return []
    merged = pd.merge(sp, nq, on="date", suffixes=("_sp", "_nq")).sort_values("date")
    if len(merged) < config.SCENARIO_SP_NASDAQ_WINDOW:
        return []
    w = config.SCENARIO_SP_NASDAQ_WINDOW
    merged["sp_ret"] = merged["value_sp"].pct_change(periods=w)
    merged["nq_ret"] = merged["value_nq"].pct_change(periods=w)
    merged["divergence"] = merged["nq_ret"] - merged["sp_ret"]
    large = merged[merged["divergence"].abs() >= config.SCENARIO_SP_NASDAQ_DIVERGENCE].dropna(subset=["divergence"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < w:
            continue
        if row["divergence"] > 0:
            desc = f"NASDAQ outperformed S&P 500 by {row['divergence']*100:.1f}pp"
        else:
            desc = f"NASDAQ underperformed S&P 500 by {abs(row['divergence'])*100:.1f}pp"
        events.append({
            "event_type": "sector_rotation",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, {desc} over {w} trading days "
                f"(NASDAQ {row['nq_ret']*100:+.1f}% vs S&P {row['sp_ret']*100:+.1f}%), "
                f"signaling sector rotation."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_vix_regime_change(df: pd.DataFrame) -> list[dict]:
    """Detect sustained elevated VIX (regime change)."""
    if df.empty:
        return []
    threshold = config.SCENARIO_VIX_REGIME_THRESHOLD
    min_days = config.SCENARIO_VIX_REGIME_MIN_DAYS
    df = df.copy()
    df["elevated"] = df["value"] >= threshold
    events = []
    in_regime = False
    start_date = None
    for _, row in df.iterrows():
        if row["elevated"] and not in_regime:
            in_regime = True
            start_date = row["date"]
        elif not row["elevated"] and in_regime:
            duration = (row["date"] - start_date).days
            if duration >= min_days:
                events.append({
                    "event_type": "volatility_regime",
                    "event_date": start_date,
                    "event_description": (
                        f"Starting {start_date.date()}, VIX remained above "
                        f"{threshold:.0f} for {duration} consecutive days, "
                        f"indicating a sustained high-volatility regime."
                    ),
                })
            in_regime = False
    # Handle ongoing regime at end of data
    if in_regime and start_date is not None:
        duration = (df["date"].iloc[-1] - start_date).days
        if duration >= min_days:
            events.append({
                "event_type": "volatility_regime",
                "event_date": start_date,
                "event_description": (
                    f"Starting {start_date.date()}, VIX remained above "
                    f"{threshold:.0f} for {duration}+ days (ongoing), "
                    f"indicating a sustained high-volatility regime."
                ),
            })
    return events


def _detect_yield_curve_3m10y(df: pd.DataFrame) -> list[dict]:
    """Detect 10Y-3M yield curve inversions (classic recession signal)."""
    if df.empty:
        return []
    df = df.copy()
    df["prev"] = df["value"].shift(1)
    events = []
    # Inversion: spread crosses below 0
    inversions = df[(df["value"] < 0) & (df["prev"] >= 0)].dropna(subset=["prev"])
    prev_date = None
    for _, row in inversions.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 60:
            continue
        events.append({
            "event_type": "yield_curve_3m10y_inversion",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the 10Y-3M yield curve inverted to "
                f"{row['value']*100:.0f}bps — a classic recession warning signal."
            ),
        })
        prev_date = row["date"]
    # Un-inversion
    un_inversions = df[(df["value"] >= 0) & (df["prev"] < 0)].dropna(subset=["prev"])
    prev_date = None
    for _, row in un_inversions.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 60:
            continue
        events.append({
            "event_type": "yield_curve_3m10y_uninversion",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the 10Y-3M yield curve un-inverted to "
                f"{row['value']*100:.0f}bps after a period of inversion."
            ),
        })
        prev_date = row["date"]
    return events


# ------------------------------------------------------------------
# NEW: FX, DJIA, breakeven inflation, JOLTS, earnings, vehicles,
#      permits, existing home sales, NFCI, Fed balance sheet,
#      monetary base, business loans, PCE inflation, SOFR,
#      WTI oil (FRED), Henry Hub gas (FRED),
#      cross-asset composites, and short-term shock windows
# ------------------------------------------------------------------

def _detect_fx_shocks(df: pd.DataFrame, pair_name: str) -> list[dict]:
    """Detect large moves in an FX pair."""
    window = config.SCENARIO_FX_ROLLING_WINDOW
    if len(df) < window:
        return []
    pct = config.SCENARIO_FX_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "strengthened" if row["pct_change"] > 0 else "weakened"
        events.append({
            "event_type": "fx_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, {pair_name} {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over {window} trading days "
                f"to {row['value']:.4f}."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_breakeven_inflation_shocks(df: pd.DataFrame, tenor: str) -> list[dict]:
    """Detect large moves in breakeven inflation rates."""
    return _detect_level_change(
        df, "breakeven_inflation_shock",
        f"the {tenor} breakeven inflation rate",
        config.SCENARIO_BEI_DELTA, config.SCENARIO_BEI_ROLLING_WINDOW,
    )


def _detect_djia_moves(df: pd.DataFrame) -> list[dict]:
    """Detect DJIA large moves over rolling window."""
    window = config.SCENARIO_DJIA_ROLLING_WINDOW
    if len(df) < window:
        return []
    pct = config.SCENARIO_DJIA_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "rallied" if row["pct_change"] > 0 else "dropped"
        events.append({
            "event_type": "djia_move",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the DJIA {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over {window} trading days "
                f"to {row['value']:.0f}."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_jolts_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in JOLTS job openings."""
    return _detect_mom_change(df, "jolts_shock", "JOLTS job openings",
                              config.SCENARIO_JOLTS_PCT_CHANGE,
                              unit="K", fmt=".0f",
                              de_dup_days=config.SCENARIO_JOLTS_DEDUP_DAYS)


def _detect_earnings_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in average hourly earnings."""
    return _detect_mom_change(df, "earnings_shock", "average hourly earnings",
                              config.SCENARIO_EARNINGS_MOM_THRESHOLD,
                              unit="$/hr", fmt=".2f")


def _detect_vehicle_sales_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in total vehicle sales."""
    return _detect_mom_change(df, "vehicle_sales_shock", "total vehicle sales",
                              config.SCENARIO_VEHICLE_PCT_CHANGE,
                              unit="M", fmt=".1f")


def _detect_permit_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in building permits."""
    return _detect_mom_change(df, "building_permit_shock", "building permits",
                              config.SCENARIO_PERMIT_PCT_CHANGE,
                              unit="K", fmt=".0f")


def _detect_existing_home_sales_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in existing home sales."""
    return _detect_mom_change(df, "existing_home_sales_shock", "existing home sales",
                              config.SCENARIO_EXISTING_HOME_SALES_PCT,
                              unit="K", fmt=".0f")


def _detect_nfci_events(df: pd.DataFrame) -> list[dict]:
    """Detect Chicago Fed NFCI crossing above 0 (tighter than average)."""
    if df.empty:
        return []
    threshold = config.SCENARIO_NFCI_THRESHOLD
    df = df.copy()
    df["prev"] = df["value"].shift(1)
    # Tightening: crosses above threshold
    crossings_up = df[(df["value"] >= threshold) &
                      (df["prev"] < threshold)].dropna(subset=["prev"])
    # Loosening: crosses back below from above
    crossings_down = df[(df["value"] < threshold) &
                        (df["prev"] >= threshold)].dropna(subset=["prev"])
    events = []
    prev_date = None
    for _, row in crossings_up.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 30:
            continue
        events.append({
            "event_type": "nfci_tightening",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the Chicago Fed NFCI rose to "
                f"{row['value']:.3f}, crossing above 0 — signaling tighter-than-average "
                f"financial conditions."
            ),
        })
        prev_date = row["date"]
    prev_date = None
    for _, row in crossings_down.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < 30:
            continue
        events.append({
            "event_type": "nfci_loosening",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the Chicago Fed NFCI fell to "
                f"{row['value']:.3f}, crossing below 0 — signaling easing "
                f"financial conditions."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_fed_balance_sheet_events(df: pd.DataFrame) -> list[dict]:
    """Detect large changes in Fed balance sheet (WALCL)."""
    window = config.SCENARIO_FED_BS_ROLLING_WINDOW
    if len(df) < window:
        return []
    pct = config.SCENARIO_FED_BS_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].dropna(subset=["pct_change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window * 7:
            continue
        direction = "expanded" if row["pct_change"] > 0 else "contracted"
        events.append({
            "event_type": "fed_balance_sheet",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the Fed balance sheet {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over {window} weeks "
                f"to ${row['value']/1e6:.2f}T."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_monetary_base_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in the monetary base."""
    return _detect_mom_change(df, "monetary_base_shock", "the monetary base",
                              config.SCENARIO_MONETARY_BASE_PCT,
                              unit="B", fmt=".0f")


def _detect_business_loan_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in C&I loans."""
    return _detect_mom_change(df, "business_loan_shock", "C&I loans",
                              config.SCENARIO_BUSLOANS_PCT_CHANGE,
                              unit="B", fmt=".0f")


def _detect_pce_inflation_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large month-over-month changes in PCE price index."""
    return _detect_mom_change(df, "pce_inflation_shock", "PCE price index",
                              config.SCENARIO_PCEPI_MOM_THRESHOLD,
                              fmt=".2f")


def _detect_sofr_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect large moves in SOFR rate."""
    return _detect_level_change(df, "sofr_shock", "the SOFR rate",
                                config.SCENARIO_SOFR_DELTA,
                                config.SCENARIO_SOFR_WINDOW)


def _detect_wti_oil_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect WTI oil shocks from FRED daily data (DCOILWTICO)."""
    window = config.SCENARIO_OIL_ROLLING_WINDOW
    if len(df) < window:
        return []
    pct = config.SCENARIO_OIL_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "surged" if row["pct_change"] > 0 else "plunged"
        events.append({
            "event_type": "wti_oil_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, WTI crude oil {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over {window} trading days "
                f"to ${row['value']:.2f}/bbl."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_henry_hub_shocks(df: pd.DataFrame) -> list[dict]:
    """Detect Henry Hub natural gas shocks from FRED daily data (DHHNGSP)."""
    window = config.SCENARIO_OIL_ROLLING_WINDOW  # reuse same window size
    if len(df) < window:
        return []
    pct = config.SCENARIO_NATGAS_PCT_CHANGE
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "surged" if row["pct_change"] > 0 else "plunged"
        events.append({
            "event_type": "henry_hub_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, Henry Hub natural gas {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over {window} trading days "
                f"to ${row['value']:.2f}/MMBtu."
            ),
        })
        prev_date = row["date"]
    return events


# ------------------------------------------------------------------
# Cross-asset composite detectors
# ------------------------------------------------------------------

def _detect_real_yield_shocks(dgs10: pd.DataFrame, bei: pd.DataFrame) -> list[dict]:
    """Detect real yield (DGS10 - T10YIE) large moves."""
    if dgs10.empty or bei.empty:
        return []
    merged = pd.merge(dgs10, bei, on="date", suffixes=("_nom", "_bei")).sort_values("date")
    if merged.empty:
        return []
    merged["real_yield"] = merged["value_nom"] - merged["value_bei"]
    window = config.SCENARIO_REAL_YIELD_WINDOW
    if len(merged) < window:
        return []
    merged["change"] = merged["real_yield"] - merged["real_yield"].shift(window)
    delta = config.SCENARIO_REAL_YIELD_DELTA
    large = merged[merged["change"].abs() >= delta].dropna(subset=["change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "surged" if row["change"] > 0 else "plunged"
        events.append({
            "event_type": "real_yield_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the real yield (10Y nominal - breakeven) "
                f"{direction} {abs(row['change'])*100:.0f}bps over {window} days "
                f"to {row['real_yield']:.2f}%."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_credit_compression(hy: pd.DataFrame, ig: pd.DataFrame) -> list[dict]:
    """Detect credit compression/expansion (HY spread - IG spread)."""
    if hy.empty or ig.empty:
        return []
    merged = pd.merge(hy, ig, on="date", suffixes=("_hy", "_ig")).sort_values("date")
    if merged.empty:
        return []
    merged["gap"] = merged["value_hy"] - merged["value_ig"]
    window = config.SCENARIO_CREDIT_COMPRESSION_WINDOW
    if len(merged) < window:
        return []
    merged["change"] = merged["gap"] - merged["gap"].shift(window)
    delta = config.SCENARIO_CREDIT_COMPRESSION_DELTA
    large = merged[merged["change"].abs() >= delta].dropna(subset=["change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        if row["change"] > 0:
            desc = "widened (risk aversion)"
        else:
            desc = "compressed (risk appetite)"
        events.append({
            "event_type": "credit_compression",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the HY-IG credit spread gap {desc} "
                f"by {abs(row['change'])*100:.0f}bps over {window} days "
                f"to {row['gap']*100:.0f}bps."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_term_premium_shocks(dgs30: pd.DataFrame, dgs2: pd.DataFrame) -> list[dict]:
    """Detect term premium (DGS30 - DGS2) large moves."""
    if dgs30.empty or dgs2.empty:
        return []
    merged = pd.merge(dgs30, dgs2, on="date", suffixes=("_30", "_2")).sort_values("date")
    if merged.empty:
        return []
    merged["spread"] = merged["value_30"] - merged["value_2"]
    window = config.SCENARIO_TERM_PREMIUM_WINDOW
    if len(merged) < window:
        return []
    merged["change"] = merged["spread"] - merged["spread"].shift(window)
    delta = config.SCENARIO_TERM_PREMIUM_DELTA
    large = merged[merged["change"].abs() >= delta].dropna(subset=["change"])
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window:
            continue
        direction = "steepened" if row["change"] > 0 else "flattened"
        events.append({
            "event_type": "term_premium_shock",
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, the 30Y-2Y term premium {direction} "
                f"by {abs(row['change'])*100:.0f}bps over {window} days "
                f"to {row['spread']*100:.0f}bps "
                f"(30Y={row['value_30']:.2f}%, 2Y={row['value_2']:.2f}%)."
            ),
        })
        prev_date = row["date"]
    return events


# ------------------------------------------------------------------
# Short-term (5-day) shock detectors for daily series
# ------------------------------------------------------------------

def _detect_short_term_shocks(df: pd.DataFrame, event_type: str, label: str,
                               pct_threshold: float, window: int,
                               unit: str = "", fmt: str = ".0f") -> list[dict]:
    """Generic short-term percentage shock detector."""
    if len(df) < window:
        return []
    df = df.copy()
    df["pct_change"] = df["value"].pct_change(periods=window)
    large = df[df["pct_change"].abs() >= pct_threshold].copy()
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window * 2:
            continue
        direction = "surged" if row["pct_change"] > 0 else "plunged"
        events.append({
            "event_type": event_type,
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, {label} {direction} "
                f"{abs(row['pct_change'])*100:.1f}% over just {window} trading days "
                f"to {row['value']:{fmt}}{unit} — an acute short-term shock."
            ),
        })
        prev_date = row["date"]
    return events


def _detect_short_term_level_shocks(df: pd.DataFrame, event_type: str, label: str,
                                     delta: float, window: int,
                                     unit: str = "%") -> list[dict]:
    """Generic short-term absolute-level shock detector."""
    if len(df) < window:
        return []
    df = df.copy()
    df["change"] = df["value"] - df["value"].shift(window)
    large = df[df["change"].abs() >= delta].dropna(subset=["change"])
    if large.empty:
        return []
    events = []
    prev_date = None
    for _, row in large.iterrows():
        if prev_date is not None and (row["date"] - prev_date).days < window * 2:
            continue
        direction = "surged" if row["change"] > 0 else "plunged"
        events.append({
            "event_type": event_type,
            "event_date": row["date"],
            "event_description": (
                f"On {row['date'].date()}, {label} {direction} "
                f"{abs(row['change'])*100:.0f}bps over just {window} trading days "
                f"to {row['value']:.2f}{unit} — a rapid rate move."
            ),
        })
        prev_date = row["date"]
    return events


# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------

def run(granularity: str | None = None) -> pd.DataFrame:
    """Detect all scenario events and save to benchmark directory.

    Returns the scenarios DataFrame.
    """
    if granularity is None:
        granularity = config.GRANULARITY

    out_dir = config.DATA_DIR / "benchmark" / granularity
    out_dir.mkdir(parents=True, exist_ok=True)

    all_events: list[dict] = []

    # Fed rate changes
    fed = _load_fred("FEDFUNDS")
    fed_events = _detect_fed_rate_changes(fed)
    all_events.extend(fed_events)
    logger.info("Fed rate changes: %d events.", len(fed_events))

    # VIX spikes
    vix = _load_fred("VIXCLS")
    vix_events = _detect_vix_spikes(vix)
    all_events.extend(vix_events)
    logger.info("VIX spikes: %d events.", len(vix_events))

    # Oil shocks
    crude = _load_crude_spot()
    if not crude.empty:
        oil_events = _detect_oil_shocks(crude)
        all_events.extend(oil_events)
        logger.info("Oil shocks: %d events.", len(oil_events))
    else:
        logger.warning("No crude oil spot data available; skipping oil shock detection.")

    # Natural gas shocks
    natgas = _load_natgas_spot()
    if not natgas.empty:
        ng_events = _detect_natgas_shocks(natgas)
        all_events.extend(ng_events)
        logger.info("Natural gas shocks: %d events.", len(ng_events))
    else:
        logger.warning("No natural gas spot data available; skipping.")

    # Market drawdowns (S&P 500)
    sp500 = _load_fred("SP500")
    dd_events = _detect_market_drawdowns(sp500)
    all_events.extend(dd_events)
    logger.info("Market drawdowns: %d events.", len(dd_events))

    # NASDAQ large moves
    nasdaq = _load_fred("NASDAQCOM")
    nasdaq_events = _detect_nasdaq_moves(nasdaq)
    all_events.extend(nasdaq_events)
    logger.info("NASDAQ moves: %d events.", len(nasdaq_events))

    # Yield curve events (DGS10 - DGS2)
    dgs10 = _load_fred("DGS10")
    dgs2 = _load_fred("DGS2")
    yc_events = _detect_yield_curve_events(dgs10, dgs2)
    all_events.extend(yc_events)
    logger.info("Yield curve events: %d events.", len(yc_events))

    # Treasury rate shocks (10-year yield)
    tr_events = _detect_treasury_rate_shocks(dgs10)
    all_events.extend(tr_events)
    logger.info("Treasury rate shocks: %d events.", len(tr_events))

    # USD index shocks
    usd = _load_fred("DTWEXBGS")
    usd_events = _detect_usd_shocks(usd)
    all_events.extend(usd_events)
    logger.info("USD shocks: %d events.", len(usd_events))

    # 30-year Treasury shocks
    dgs30 = _load_fred("DGS30")
    if not dgs30.empty:
        dgs30_events = _detect_dgs30_shocks(dgs30)
        all_events.extend(dgs30_events)
        logger.info("30Y Treasury shocks: %d events.", len(dgs30_events))

    # CPI inflation shocks
    cpi = _load_fred("CPIAUCSL")
    if not cpi.empty:
        cpi_events = _detect_cpi_shocks(cpi)
        all_events.extend(cpi_events)
        logger.info("CPI inflation shocks: %d events.", len(cpi_events))

    # PPI shocks
    ppi = _load_fred("PPIACO")
    if not ppi.empty:
        ppi_events = _detect_ppi_shocks(ppi)
        all_events.extend(ppi_events)
        logger.info("PPI shocks: %d events.", len(ppi_events))

    # Unemployment shocks
    unrate = _load_fred("UNRATE")
    if not unrate.empty:
        un_events = _detect_unemployment_shocks(unrate)
        all_events.extend(un_events)
        logger.info("Unemployment shocks: %d events.", len(un_events))

    # Jobless claims spikes
    icsa = _load_fred("ICSA")
    if not icsa.empty:
        icsa_events = _detect_jobless_claims_spikes(icsa)
        all_events.extend(icsa_events)
        logger.info("Jobless claims spikes: %d events.", len(icsa_events))

    # Payroll shocks
    payems = _load_fred("PAYEMS")
    if not payems.empty:
        pay_events = _detect_payroll_shocks(payems)
        all_events.extend(pay_events)
        logger.info("Payroll shocks: %d events.", len(pay_events))

    # High-yield credit spread
    hy = _load_fred("BAMLH0A0HYM2")
    if not hy.empty:
        hy_events = _detect_hy_spread_events(hy)
        all_events.extend(hy_events)
        logger.info("HY spread events: %d events.", len(hy_events))

    # IG corporate spread
    ig = _load_fred("BAMLC0A0CM")
    if not ig.empty:
        ig_events = _detect_ig_spread_events(ig)
        all_events.extend(ig_events)
        logger.info("IG spread events: %d events.", len(ig_events))

    # TED spread spikes
    ted = _load_fred("TEDRATE")
    if not ted.empty:
        ted_events = _detect_ted_spread_spikes(ted)
        all_events.extend(ted_events)
        logger.info("TED spread spikes: %d events.", len(ted_events))

    # Financial stress index
    fsi = _load_fred("STLFSI2")
    if not fsi.empty:
        fsi_events = _detect_financial_stress(fsi)
        all_events.extend(fsi_events)
        logger.info("Financial stress events: %d events.", len(fsi_events))

    # Mortgage rate shocks
    mort = _load_fred("MORTGAGE30US")
    if not mort.empty:
        mort_events = _detect_mortgage_rate_shocks(mort)
        all_events.extend(mort_events)
        logger.info("Mortgage rate shocks: %d events.", len(mort_events))

    # Consumer sentiment shocks
    sent = _load_fred("UMCSENT")
    if not sent.empty:
        sent_events = _detect_sentiment_shocks(sent)
        all_events.extend(sent_events)
        logger.info("Sentiment shocks: %d events.", len(sent_events))

    # Industrial production shocks
    indpro = _load_fred("INDPRO")
    if not indpro.empty:
        ip_events = _detect_industrial_production_shocks(indpro)
        all_events.extend(ip_events)
        logger.info("Industrial production shocks: %d events.", len(ip_events))

    # Retail sales shocks
    retail = _load_fred("RSAFS")
    if not retail.empty:
        rs_events = _detect_retail_sales_shocks(retail)
        all_events.extend(rs_events)
        logger.info("Retail sales shocks: %d events.", len(rs_events))

    # Housing starts shocks
    houst = _load_fred("HOUST")
    if not houst.empty:
        hs_events = _detect_housing_starts_shocks(houst)
        all_events.extend(hs_events)
        logger.info("Housing starts shocks: %d events.", len(hs_events))

    # Home price events
    cshpi = _load_fred("CSUSHPISA")
    if not cshpi.empty:
        hp_events = _detect_home_price_events(cshpi)
        all_events.extend(hp_events)
        logger.info("Home price events: %d events.", len(hp_events))

    # M2 money supply events
    m2 = _load_fred("M2SL")
    if not m2.empty:
        m2_events = _detect_m2_events(m2)
        all_events.extend(m2_events)
        logger.info("M2 money supply events: %d events.", len(m2_events))

    # S&P vs NASDAQ divergence (sector rotation)
    if not sp500.empty and not nasdaq.empty:
        div_events = _detect_sp_nasdaq_divergence(sp500, nasdaq)
        all_events.extend(div_events)
        logger.info("Sector rotation events: %d events.", len(div_events))

    # VIX regime changes
    if not vix.empty:
        regime_events = _detect_vix_regime_change(vix)
        all_events.extend(regime_events)
        logger.info("Volatility regime events: %d events.", len(regime_events))

    # 10Y-3M yield curve (T10Y3M) — direct spread from FRED
    t10y3m = _load_fred("T10Y3M")
    if not t10y3m.empty:
        yc3m_events = _detect_yield_curve_3m10y(t10y3m)
        all_events.extend(yc3m_events)
        logger.info("Yield curve 3M-10Y events: %d events.", len(yc3m_events))

    # ── NEW: DJIA large moves ──
    djia = _load_fred("DJIA")
    if not djia.empty:
        djia_events = _detect_djia_moves(djia)
        all_events.extend(djia_events)
        logger.info("DJIA moves: %d events.", len(djia_events))

    # ── NEW: WTI crude oil from FRED daily ──
    wti = _load_fred("DCOILWTICO")
    if not wti.empty:
        wti_events = _detect_wti_oil_shocks(wti)
        all_events.extend(wti_events)
        logger.info("WTI oil shocks (FRED): %d events.", len(wti_events))

    # ── NEW: Henry Hub natural gas from FRED daily ──
    hh = _load_fred("DHHNGSP")
    if not hh.empty:
        hh_events = _detect_henry_hub_shocks(hh)
        all_events.extend(hh_events)
        logger.info("Henry Hub gas shocks (FRED): %d events.", len(hh_events))

    # ── NEW: FX pair shocks ──
    for series_id, pair_name in [
        ("DEXUSEU", "USD/EUR"), ("DEXJPUS", "JPY/USD"),
        ("DEXUSUK", "USD/GBP"), ("DEXCHUS", "CNY/USD"),
    ]:
        fx = _load_fred(series_id)
        if not fx.empty:
            fx_events = _detect_fx_shocks(fx, pair_name)
            all_events.extend(fx_events)
            logger.info("FX shocks (%s): %d events.", pair_name, len(fx_events))

    # ── NEW: Breakeven inflation shocks ──
    for series_id, tenor in [("T10YIE", "10-year"), ("T5YIE", "5-year")]:
        bei = _load_fred(series_id)
        if not bei.empty:
            bei_events = _detect_breakeven_inflation_shocks(bei, tenor)
            all_events.extend(bei_events)
            logger.info("Breakeven inflation (%s): %d events.", tenor, len(bei_events))

    # ── NEW: PCE inflation ──
    pcepi = _load_fred("PCEPI")
    if not pcepi.empty:
        pce_events = _detect_pce_inflation_shocks(pcepi)
        all_events.extend(pce_events)
        logger.info("PCE inflation shocks: %d events.", len(pce_events))

    # ── NEW: SOFR rate shocks ──
    sofr = _load_fred("SOFR")
    if not sofr.empty:
        sofr_events = _detect_sofr_shocks(sofr)
        all_events.extend(sofr_events)
        logger.info("SOFR shocks: %d events.", len(sofr_events))

    # ── NEW: JOLTS job openings ──
    jolts = _load_fred("JTSJOL")
    if not jolts.empty:
        jolts_events = _detect_jolts_shocks(jolts)
        all_events.extend(jolts_events)
        logger.info("JOLTS shocks: %d events.", len(jolts_events))

    # ── NEW: Average hourly earnings ──
    earnings = _load_fred("CES0500000003")
    if not earnings.empty:
        earn_events = _detect_earnings_shocks(earnings)
        all_events.extend(earn_events)
        logger.info("Earnings shocks: %d events.", len(earn_events))

    # ── NEW: Total vehicle sales ──
    vehicles = _load_fred("TOTALSA")
    if not vehicles.empty:
        veh_events = _detect_vehicle_sales_shocks(vehicles)
        all_events.extend(veh_events)
        logger.info("Vehicle sales shocks: %d events.", len(veh_events))

    # ── NEW: Building permits ──
    permits = _load_fred("PERMIT")
    if not permits.empty:
        perm_events = _detect_permit_shocks(permits)
        all_events.extend(perm_events)
        logger.info("Building permit shocks: %d events.", len(perm_events))

    # ── NEW: Existing home sales ──
    ehs = _load_fred("EXHOSLUSM495S")
    if not ehs.empty:
        ehs_events = _detect_existing_home_sales_shocks(ehs)
        all_events.extend(ehs_events)
        logger.info("Existing home sales shocks: %d events.", len(ehs_events))

    # ── NEW: Chicago Fed NFCI ──
    nfci = _load_fred("NFCI")
    if not nfci.empty:
        nfci_events = _detect_nfci_events(nfci)
        all_events.extend(nfci_events)
        logger.info("NFCI events: %d events.", len(nfci_events))

    # ── NEW: Fed balance sheet ──
    walcl = _load_fred("WALCL")
    if not walcl.empty:
        bs_events = _detect_fed_balance_sheet_events(walcl)
        all_events.extend(bs_events)
        logger.info("Fed balance sheet events: %d events.", len(bs_events))

    # ── NEW: Monetary base ──
    bogm = _load_fred("BOGMBASE")
    if not bogm.empty:
        bogm_events = _detect_monetary_base_shocks(bogm)
        all_events.extend(bogm_events)
        logger.info("Monetary base shocks: %d events.", len(bogm_events))

    # ── NEW: Business / C&I loans ──
    busloans = _load_fred("BUSLOANS")
    if not busloans.empty:
        bl_events = _detect_business_loan_shocks(busloans)
        all_events.extend(bl_events)
        logger.info("Business loan shocks: %d events.", len(bl_events))

    # ── NEW: Cross-asset composites ──

    # Real yield: DGS10 - T10YIE
    bei_10y = _load_fred("T10YIE")
    if not dgs10.empty and not bei_10y.empty:
        ry_events = _detect_real_yield_shocks(dgs10, bei_10y)
        all_events.extend(ry_events)
        logger.info("Real yield shocks: %d events.", len(ry_events))

    # Credit compression: HY - IG spread gap
    if not hy.empty and not ig.empty:
        cc_events = _detect_credit_compression(hy, ig)
        all_events.extend(cc_events)
        logger.info("Credit compression events: %d events.", len(cc_events))

    # Term premium: DGS30 - DGS2
    if not dgs30.empty and not dgs2.empty:
        tp_events = _detect_term_premium_shocks(dgs30, dgs2)
        all_events.extend(tp_events)
        logger.info("Term premium shocks: %d events.", len(tp_events))

    # ── NEW: Short-term (5-day) shocks for acute market events ──

    # S&P 500 acute crash
    if not sp500.empty:
        sp_short = _detect_short_term_shocks(
            sp500, "sp500_acute_shock", "the S&P 500",
            config.SCENARIO_SP500_SHORT_DRAWDOWN,
            config.SCENARIO_SP500_SHORT_WINDOW)
        all_events.extend(sp_short)
        logger.info("S&P 500 acute shocks (5d): %d events.", len(sp_short))

    # NASDAQ acute shock
    if not nasdaq.empty:
        nq_short = _detect_short_term_shocks(
            nasdaq, "nasdaq_acute_shock", "the NASDAQ",
            config.SCENARIO_NASDAQ_SHORT_PCT,
            config.SCENARIO_NASDAQ_SHORT_WINDOW)
        all_events.extend(nq_short)
        logger.info("NASDAQ acute shocks (5d): %d events.", len(nq_short))

    # Oil acute shock
    if not wti.empty:
        oil_short = _detect_short_term_shocks(
            wti, "oil_acute_shock", "WTI crude oil",
            config.SCENARIO_OIL_SHORT_PCT,
            config.SCENARIO_OIL_SHORT_WINDOW,
            unit="$/bbl", fmt=".2f")
        all_events.extend(oil_short)
        logger.info("Oil acute shocks (5d): %d events.", len(oil_short))

    # 10Y Treasury acute rate move
    if not dgs10.empty:
        dgs10_short = _detect_short_term_level_shocks(
            dgs10, "treasury_acute_shock", "the 10Y Treasury yield",
            config.SCENARIO_DGS10_SHORT_DELTA,
            config.SCENARIO_DGS10_SHORT_WINDOW)
        all_events.extend(dgs10_short)
        logger.info("10Y Treasury acute shocks (5d): %d events.", len(dgs10_short))

    # Build DataFrame
    if all_events:
        df = pd.DataFrame(all_events)
        df["event_date"] = pd.to_datetime(df["event_date"])
        df = df.sort_values("event_date").reset_index(drop=True)
        df["scenario_id"] = [f"sc_{i:04d}" for i in range(len(df))]
        df["pre_window_start"] = df["event_date"] - pd.Timedelta(days=config.SCENARIO_PRE_WINDOW_DAYS)
        df["post_window_end"] = df["event_date"] + pd.Timedelta(days=config.SCENARIO_POST_WINDOW_DAYS)
        # Reorder columns
        df = df[["scenario_id", "event_type", "event_date", "event_description",
                  "pre_window_start", "post_window_end"]]
    else:
        df = pd.DataFrame(columns=[
            "scenario_id", "event_type", "event_date", "event_description",
            "pre_window_start", "post_window_end",
        ])

    # Filter out scenarios whose event_date falls outside the valid panel
    # window. Scenarios before START_DATE have no pre-event prices; those
    # at the very end have no post-event prices. Both produce empty ground
    # truth and should be dropped to keep scenarios.parquet = GT set.
    if not df.empty:
        panel_start = pd.Timestamp(config.START_DATE)
        panel_end = pd.Timestamp(config.END_DATE)
        # Leave at least 21 trading days after the event for post-window returns
        # AND at least 21 trading days before for pre-event baseline
        min_event = panel_start + pd.Timedelta(days=35)
        max_event = panel_end - pd.Timedelta(days=35)
        before = len(df)
        df = df[(df["event_date"] >= min_event) & (df["event_date"] <= max_event)].copy()
        # Re-number scenario_ids to keep them contiguous after filtering
        df = df.sort_values("event_date").reset_index(drop=True)
        df["scenario_id"] = [f"sc_{i:04d}" for i in range(len(df))]
        dropped = before - len(df)
        if dropped > 0:
            logger.info("Filtered %d scenarios outside valid panel window [%s, %s]",
                        dropped, panel_start.date(), max_event.date())

    df.to_parquet(out_dir / "scenarios.parquet", index=False)
    logger.info("Saved %d scenario events -> %s", len(df), out_dir / "scenarios.parquet")
    return df