"""Historical backtest engine for Bitcoin Accumulation Zone scoring."""

import json
import logging
import os
import sys
from collections import defaultdict
from datetime import datetime, timedelta

from scoring.policy import SCORE_BRACKETS, SCORE_VERSION, score_in_bracket
from ml.artifacts import validate_ml_artifact

log = logging.getLogger(__name__)

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, BASE_DIR)

HISTORY_PATH = os.path.join(BASE_DIR, "data", "history.json")
CACHE_PATH = os.path.join(BASE_DIR, "data", "cache.json")

# Score brackets matching the dashboard assessment levels
BRACKETS = SCORE_BRACKETS

# Scoring thresholds — load from config/thresholds.json (single source of truth)
import os as _os
import json as _json

_THRESH_PATH = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))), "config", "thresholds.json")
try:
    with open(_THRESH_PATH) as _f:
        _THRESH = _json.load(_f)
except Exception:
    _THRESH = {}

METRIC_SCORERS = {
    "fear_greed": {"ranges": _THRESH.get("fear_greed", {}).get("ranges", [[0, 15, 10], [15, 30, 8], [30, 45, 5], [45, 55, 3], [55, 75, 1], [75, None, 0]])},
    "puell_multiple": {"ranges": _THRESH.get("puell_multiple", {}).get("ranges", [[None, 0.4, 10], [0.4, 0.7, 8], [0.7, 1.0, 5], [1.0, 1.5, 3], [1.5, 2.0, 1], [2.0, None, 0]])},
    "mvrv_zscore": {"ranges": _THRESH.get("mvrv_zscore", {}).get("ranges", [[None, 0, 10], [0, 1.0, 8], [1.0, 2.0, 5], [2.0, 3.0, 3], [3.0, 5.0, 1], [5.0, None, 0]])},
    "reserve_risk": {"ranges": _THRESH.get("reserve_risk", {}).get("ranges", [[None, 0.002, 10], [0.002, 0.005, 7], [0.005, 0.01, 4], [0.01, 0.02, 2], [0.02, None, 0]])},
    "rhodl_ratio": {"ranges": _THRESH.get("rhodl_ratio", {}).get("ranges", [[None, 200, 10], [200, 1000, 7], [1000, 5000, 4], [5000, 20000, 1], [20000, None, 0]])},
    "nupl": {"ranges": _THRESH.get("nupl", {}).get("ranges", [[None, 0, 10], [0, 0.3, 8], [0.3, 0.5, 4], [0.5, 0.75, 1], [0.75, None, 0]])},
}

RATIO_SCORERS = {
    "price_vs_200w_sma": {
        "ranges": _THRESH.get("price_vs_200w_sma", {}).get("ranges", [[None, 0, 10], [0, 30, 7], [30, 60, 5], [60, 100, 2], [100, None, 0]]),
        "price_key": "btc_price",
        "ref_key": "200w_sma",
    },
    "lth_realized_price": {
        "ranges": _THRESH.get("lth_realized_price", {}).get("ranges", [[None, 0, 10], [0, 30, 7], [30, 80, 5], [80, 150, 3], [150, None, 1]]),
        "price_key": "btc_price",
        "ref_key": "lth_realized_price",
    },
}

BACKTEST_METRIC_PANEL = tuple(METRIC_SCORERS) + tuple(RATIO_SCORERS) + ("drawdown",)
METRIC_MAX_AGE_DAYS = {
    "fear_greed": 2,
    "puell_multiple": 7,
    "mvrv_zscore": 7,
    "reserve_risk": 7,
    "rhodl_ratio": 7,
    "nupl": 7,
    "btc_price": 3,
    "btc_price_coingecko": 3,
    "btc_price_sma": 3,
    "btc_price_lth": 3,
    "200w_sma": 7,
    "lth_realized_price": 7,
}

DRAWDOWN_RANGES = _THRESH.get("drawdown", {}).get("ranges", [[60, None, 10], [40, 60, 8], [25, 40, 6], [15, 25, 4], [5, 15, 2], [None, 5, 0]])


def _score_range(value, ranges):
    """Score a value using range-based thresholds."""
    if value is None:
        return None
    for low, high, score in ranges:
        low_ok = low is None or value >= low
        high_ok = high is None or value < high
        if low_ok and high_ok:
            return score
    return 0


def _build_daily_index(history):
    """Build a dict mapping metric_key -> {date_str: value} for fast lookup."""
    index = {}
    for key, data in history.items():
        if key.startswith("_") or not isinstance(data, dict) or "dates" not in data:
            continue
        lookup = {}
        for d, v in zip(data["dates"], data["values"]):
            lookup[d] = v
        index[key] = lookup
    return index


def _get_all_dates(index):
    """Get sorted union of all dates across all metrics."""
    all_dates = set()
    for lookup in index.values():
        all_dates.update(lookup.keys())
    return sorted(all_dates)


def _last_known_value(lookup, date, max_lookback=0):
    """Get value for date, or a prior value within an explicit lookback."""
    if date in lookup:
        return lookup[date]
    d = datetime.strptime(date, "%Y-%m-%d")
    for i in range(1, max_lookback + 1):
        prev = (d - timedelta(days=i)).strftime("%Y-%m-%d")
        if prev in lookup:
            return lookup[prev]
    return None


def _metric_observation(lookup, date, metric_key):
    """Return value, source date, and age under a metric-specific freshness rule."""
    max_age = METRIC_MAX_AGE_DAYS.get(metric_key, 0)
    target = datetime.strptime(date, "%Y-%m-%d")
    for age in range(max_age + 1):
        source_date = (target - timedelta(days=age)).strftime("%Y-%m-%d")
        if source_date in lookup:
            return lookup[source_date], source_date, age
    return None, None, None


def _compute_ath_series(price_lookup, dates):
    """Compute running ATH and drawdown for each date."""
    ath = 0
    drawdowns = {}
    for d in dates:
        p = price_lookup.get(d)
        if p is None:
            continue
        if p > ath:
            ath = p
        if ath > 0:
            drawdowns[d] = ((ath - p) / ath) * 100
    return drawdowns


def _load_ml_artifact():
    """Load an ML artifact and return it with validation status."""
    ml_path = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))), "config", "ml_weights.json")
    try:
        with open(ml_path) as f:
            data = _json.load(f)
        status = validate_ml_artifact(data)
        if not status["valid"]:
            log.error("Rejected invalid ML artifact: %s", ", ".join(status["errors"]))
            return None, status
        return data, status
    except Exception as exc:
        return None, {"valid": False, "errors": [f"load_error:{exc}"]}


def _build_ml_backtest_plan(artifact):
    """Choose OOS fold weights when available; otherwise mark final weights in-sample."""
    status = validate_ml_artifact(artifact)
    if not status["valid"]:
        raise ValueError("invalid ML artifact: " + ", ".join(status["errors"]))

    if status["has_oos_fold_weights"]:
        folds = []
        for fold in artifact["cv_results"]["folds"]:
            start, separator, end = fold["date_ranges"]["validation"].partition(" to ")
            if not separator:
                raise ValueError("invalid validation date range")
            folds.append({
                "fold": fold.get("fold"),
                "start": start,
                "end": end,
                "weights": fold["weights"],
            })
        return {
            "evaluation_scope": "out_of_sample_validation_folds",
            "is_out_of_sample": True,
            "weighting_source": "fold_specific_weights",
            "folds": folds,
            "weights": None,
        }

    return {
        "evaluation_scope": "in_sample_full_history_weights",
        "is_out_of_sample": False,
        "weighting_source": "final_full_history_weights",
        "folds": [],
        "weights": artifact["weights"],
    }


def _weights_for_backtest_date(date, plan):
    """Return date-appropriate weights and fold number for an ML plan."""
    if plan["is_out_of_sample"]:
        for fold in plan["folds"]:
            if fold["start"] <= date <= fold["end"]:
                return fold["weights"], fold["fold"]
        return None, None
    return plan["weights"], None


def _load_ml_weights():
    """Compatibility helper returning valid final weights only."""
    artifact, _ = _load_ml_artifact()
    return artifact.get("weights", {}) if artifact else {}

# ML weight key mapping (backtest metric keys -> ML weight keys)
_BT_ML_KEY_MAP = {
    "fear_greed": "fear_greed",
    "puell_multiple": "puell_multiple",
    "mvrv_zscore": "mvrv_zscore",
    "reserve_risk": "reserve_risk",
    "rhodl_ratio": "rhodl_ratio",
    "nupl": "nupl",
    "price_vs_200w_sma": "pct_above_200w_sma",
    "lth_realized_price": "pct_above_lth_rp",
    "drawdown": "drawdown",
}


def _common_panel_current_score(scored, ml_weights=None):
    """Recompute the current score using only metrics present historically."""
    by_key = {
        metric.get("key"): metric.get("score")
        for metric in scored.get("metrics", [])
        if metric.get("key") in BACKTEST_METRIC_PANEL and metric.get("score") is not None
    }
    available_keys = [key for key in BACKTEST_METRIC_PANEL if key in by_key]
    coverage = {
        "available_count": len(available_keys),
        "panel_count": len(BACKTEST_METRIC_PANEL),
        "available_keys": available_keys,
    }
    if not available_keys:
        return None, coverage

    if ml_weights:
        weighted = [
            (by_key[key], ml_weights.get(_BT_ML_KEY_MAP[key], 0.0))
            for key in available_keys
        ]
        weight_total = sum(weight for _, weight in weighted)
        if weight_total > 0:
            return round(sum(score * weight for score, weight in weighted) / weight_total * 10, 1), coverage

    return round(sum(by_key[key] for key in available_keys) / len(available_keys) * 10, 1), coverage


def _backtest_data_quality_metadata(metric_counts):
    """Describe historical panel, coverage, and freshness assumptions."""
    coverage = {
        "minimum_metrics": min(metric_counts),
        "maximum_metrics": max(metric_counts),
        "average_metrics": round(sum(metric_counts) / len(metric_counts), 1),
        "panel_count": len(BACKTEST_METRIC_PANEL),
    } if metric_counts else {
        "minimum_metrics": 0,
        "maximum_metrics": 0,
        "average_metrics": 0,
        "panel_count": len(BACKTEST_METRIC_PANEL),
    }
    return {
        "metric_panel": {
            "id": "historical-common-v1",
            "keys": list(BACKTEST_METRIC_PANEL),
            "count": len(BACKTEST_METRIC_PANEL),
        },
        "coverage": coverage,
        "staleness_days": dict(METRIC_MAX_AGE_DAYS),
    }


def score_day(date, index, drawdowns, ml_weights=None):
    """Score a single day using all available metrics. Returns (composite_score, details, n_metrics).

    If ml_weights is provided, uses ML-optimized weighting instead of equal weights.
    details includes both "score" and "raw" (the actual metric value before scoring).
    """
    scores = []
    details = {}

    # Simple range-based metrics
    for metric_key, cfg in METRIC_SCORERS.items():
        val, observed_date, age_days = _metric_observation(
            index.get(metric_key, {}), date, metric_key
        )
        if val is not None:
            s = _score_range(val, cfg["ranges"])
            if s is not None:
                scores.append(s)
                details[metric_key] = {
                    "value": val,
                    "score": s,
                    "raw": val,
                    "observed_date": observed_date,
                    "age_days": age_days,
                }

    # Ratio-based metrics (price vs reference)
    for metric_key, cfg in RATIO_SCORERS.items():
        price_val, price_date, price_age = _metric_observation(
            index.get(cfg["price_key"], {}), date, cfg["price_key"]
        )
        # Try alternate price keys, each with an explicit freshness rule.
        if price_val is None:
            for pk in ["btc_price_coingecko", "btc_price_sma", "btc_price_lth"]:
                price_val, price_date, price_age = _metric_observation(index.get(pk, {}), date, pk)
                if price_val is not None:
                    break
        ref_val, ref_date, ref_age = _metric_observation(
            index.get(cfg["ref_key"], {}), date, cfg["ref_key"]
        )
        if price_val is not None and ref_val is not None and ref_val > 0:
            pct_above = ((price_val - ref_val) / ref_val) * 100
            s = _score_range(pct_above, cfg["ranges"])
            if s is not None:
                scores.append(s)
                details[metric_key] = {
                    "value": pct_above,
                    "score": s,
                    "raw": pct_above,
                    "observed_date": min(price_date, ref_date),
                    "age_days": max(price_age, ref_age),
                }

    # Drawdown
    dd = drawdowns.get(date)
    if dd is not None:
        s = _score_range(dd, DRAWDOWN_RANGES)
        if s is not None:
            scores.append(s)
            details["drawdown"] = {"value": dd, "score": s, "raw": dd}

    if not scores:
        return None, details, 0

    if ml_weights:
        # ML-weighted composite
        weighted_sum = 0.0
        weight_total = 0.0
        for metric_key, info in details.items():
            ml_key = _BT_ML_KEY_MAP.get(metric_key, metric_key)
            w = ml_weights.get(ml_key, 0.0)
            weighted_sum += info["score"] * w
            weight_total += w
        if weight_total > 0:
            composite = weighted_sum / weight_total * 10
        else:
            composite = sum(scores) / len(scores) * 10
    else:
        composite = sum(scores) / len(scores) * 10
    return round(composite, 1), details, len(scores)


def compute_forward_returns(price_lookup, dates_sorted):
    """Precompute forward returns for all dates."""
    periods = [30, 90, 180, 365]
    returns = {}
    for d in dates_sorted:
        p0 = price_lookup.get(d)
        if p0 is None or p0 <= 0:
            continue
        r = {}
        dt = datetime.strptime(d, "%Y-%m-%d")
        for days in periods:
            future = (dt + timedelta(days=days)).strftime("%Y-%m-%d")
            pf = price_lookup.get(future)
            if pf is not None:
                r[f"{days}d"] = round(((pf - p0) / p0) * 100, 2)
        if r:
            returns[d] = r
    return returns


def compute_max_drawdown_forward(price_lookup, date, window=90):
    """Compute max drawdown within N days after a given date."""
    dt = datetime.strptime(date, "%Y-%m-%d")
    p0 = price_lookup.get(date)
    if p0 is None or p0 <= 0:
        return None
    peak = p0
    max_dd = 0
    for i in range(1, window + 1):
        future = (dt + timedelta(days=i)).strftime("%Y-%m-%d")
        pf = price_lookup.get(future)
        if pf is None:
            continue
        if pf > peak:
            peak = pf
        dd = ((peak - pf) / peak) * 100
        if dd > max_dd:
            max_dd = dd
    return round(max_dd, 2) if max_dd > 0 else 0


def run_backtest(ml_mode=False):
    """Run the full backtest and return comprehensive results.

    If ml_mode=True, uses ML-optimized metric weights instead of equal weights.
    """
    log.info("Loading historical data... (ml_mode=%s)", ml_mode)
    if not os.path.exists(HISTORY_PATH):
        return {"error": "No historical data found. Run history collector first."}

    with open(HISTORY_PATH) as f:
        history = json.load(f)

    index = _build_daily_index(history)

    # Build price lookup (prefer coingecko for completeness)
    price_lookup = {}
    for pk in ["btc_price_coingecko", "btc_price", "btc_price_sma", "btc_price_lth"]:
        if pk in index:
            for d, v in index[pk].items():
                if d not in price_lookup:
                    price_lookup[d] = v

    all_dates = _get_all_dates(index)
    if not all_dates:
        return {"error": "No date data available."}

    log.info("Date range: %s to %s (%d days)", all_dates[0], all_dates[-1], len(all_dates))

    # Compute drawdowns
    drawdowns = _compute_ath_series(price_lookup, all_dates)

    # Precompute forward returns
    log.info("Computing forward returns...")
    fwd_returns = compute_forward_returns(price_lookup, all_dates)

    # Build an explicit evaluation plan. Fold-specific validation weights are OOS;
    # final weights fitted on full history are never represented as OOS.
    ml_plan = None
    ml_artifact = None
    ml_artifact_status = None
    if ml_mode:
        ml_artifact, ml_artifact_status = _load_ml_artifact()
        if ml_artifact:
            ml_plan = _build_ml_backtest_plan(ml_artifact)
        else:
            log.warning("ML mode requested with invalid artifact — falling back to equal weights")

    # Score each day
    log.info("Scoring %d days...", len(all_dates))
    daily_scores = []
    for d in all_dates:
        ml_weights = None
        ml_fold = None
        if ml_plan:
            ml_weights, ml_fold = _weights_for_backtest_date(d, ml_plan)
            if ml_plan["is_out_of_sample"] and ml_weights is None:
                continue
        composite, details, n_metrics = score_day(d, index, drawdowns, ml_weights=ml_weights)
        if composite is not None and n_metrics >= 3:  # Require at least 3 metrics
            price = price_lookup.get(d)
            # Collect raw metric values for per-metric historical exploration
            metric_values = {}
            for mk, info in details.items():
                raw = info.get("raw")
                if raw is not None:
                    metric_values[mk] = round(raw, 6) if isinstance(raw, float) else raw
            entry = {
                "date": d,
                "score": composite,
                "n_metrics": n_metrics,
                "price": price,
                "forward_returns": fwd_returns.get(d, {}),
                "metric_values": metric_values,
            }
            if ml_fold is not None:
                entry["ml_fold"] = ml_fold
            daily_scores.append(entry)

    if not daily_scores:
        return {"error": "No scored days (insufficient metric overlap)."}

    log.info("Scored %d days with 3+ metrics", len(daily_scores))

    # --- Bracket statistics ---
    bracket_stats = []
    for low, high, label in BRACKETS:
        days_in = [d for d in daily_scores if score_in_bracket(d["score"], (low, high, label))]
        if not days_in:
            bracket_stats.append({
                "range": f"{low}-{high}", "label": label, "days": 0,
            })
            continue

        stats = {"range": f"{low}-{high}", "label": label, "days": len(days_in)}
        for period in ["30d", "90d", "180d", "365d"]:
            returns = [d["forward_returns"][period] for d in days_in if period in d["forward_returns"]]
            if returns:
                returns_sorted = sorted(returns)
                stats[f"avg_{period}"] = round(sum(returns) / len(returns), 2)
                stats[f"median_{period}"] = round(returns_sorted[len(returns_sorted) // 2], 2)
                stats[f"win_rate_{period}"] = round(len([r for r in returns if r > 0]) / len(returns) * 100, 1)
                stats[f"max_gain_{period}"] = round(max(returns), 2)
                stats[f"max_loss_{period}"] = round(min(returns), 2)
                stats[f"n_{period}"] = len(returns)

        # Average max drawdown within 90 days
        dd_list = []
        for d in days_in:
            dd = compute_max_drawdown_forward(price_lookup, d["date"], 90)
            if dd is not None:
                dd_list.append(dd)
        if dd_list:
            stats["avg_max_drawdown_90d"] = round(sum(dd_list) / len(dd_list), 2)

        bracket_stats.append(stats)

    # --- Peak signal events ---
    signal_events = []
    thresholds = [90, 80, 70]
    for thresh in thresholds:
        prev_score = 0
        for d in daily_scores:
            if d["score"] >= thresh and prev_score < thresh:
                event = {
                    "date": d["date"],
                    "score": d["score"],
                    "threshold": thresh,
                    "price": d["price"],
                    "forward_returns": d["forward_returns"],
                }
                # Add future prices
                if d["price"]:
                    dt = datetime.strptime(d["date"], "%Y-%m-%d")
                    for days_ahead in [30, 90, 365]:
                        future = (dt + timedelta(days=days_ahead)).strftime("%Y-%m-%d")
                        fp = price_lookup.get(future)
                        if fp:
                            event[f"price_{days_ahead}d"] = round(fp, 2)
                signal_events.append(event)
            prev_score = d["score"]

    signal_events.sort(key=lambda e: e["date"])

    # --- Current signal context ---
    all_scores_list = [d["score"] for d in daily_scores]
    all_scores_list.sort()

    # Get current score from cache, recomputed on the common historical panel.
    current_score = None
    current_price = None
    current_coverage = None
    if os.path.exists(CACHE_PATH):
        try:
            with open(CACHE_PATH) as f:
                cache = json.load(f)
            scored = cache.get("_scored", {})
            current_ml_weights = ml_artifact.get("weights") if ml_mode and ml_artifact else None
            current_score, current_coverage = _common_panel_current_score(scored, current_ml_weights)
            current_price = cache.get("price", {}).get("price")
        except Exception:
            pass

    # If no comparable cache panel is available, use latest historical score.
    if current_score is None and daily_scores:
        current_score = daily_scores[-1]["score"]
        current_price = daily_scores[-1].get("price")
        current_coverage = {
            "available_count": daily_scores[-1]["n_metrics"],
            "panel_count": len(BACKTEST_METRIC_PANEL),
            "available_keys": list(daily_scores[-1].get("metric_values", {})),
        }

    current_context = None
    if current_score is not None:
        # Percentile
        below = len([s for s in all_scores_list if s <= current_score])
        percentile = round(below / len(all_scores_list) * 100, 1)

        # Find comparable historical periods
        comparable = []
        margin = 5
        for d in daily_scores:
            if abs(d["score"] - current_score) <= margin and d["forward_returns"]:
                comparable.append(d)

        avg_returns = {}
        if comparable:
            for period in ["30d", "90d", "180d", "365d"]:
                vals = [d["forward_returns"][period] for d in comparable if period in d["forward_returns"]]
                if vals:
                    avg_returns[period] = round(sum(vals) / len(vals), 2)
        avg_1yr = avg_returns.get("365d")

        # Best comparable examples — one per market cycle for diversity
        # Cycles: pre-2016, 2016-2017 bull, 2018-2019 bear, 2020-2021 bull, 2022-2023 bear, 2024+
        cycle_bins = [
            ("pre-2016", "2010-01-01", "2015-12-31"),
            ("2016-17 Bull", "2016-01-01", "2017-12-31"),
            ("2018-19 Bear", "2018-01-01", "2019-12-31"),
            ("2020-21 Bull", "2020-01-01", "2021-12-31"),
            ("2022-23 Bear", "2022-01-01", "2023-12-31"),
            ("2024+", "2024-01-01", "2099-12-31"),
        ]
        examples = []
        used_cycles = set()
        # Sort comparable by closest score first, then pick one per cycle
        sorted_comp = sorted(comparable, key=lambda d: abs(d["score"] - current_score))
        for d in sorted_comp:
            cycle_label = None
            for label, start, end in cycle_bins:
                if start <= d["date"] <= end:
                    cycle_label = label
                    break
            if cycle_label and cycle_label not in used_cycles:
                used_cycles.add(cycle_label)
                examples.append({
                    "date": d["date"],
                    "score": d["score"],
                    "price": d["price"],
                    "forward_returns": d["forward_returns"],
                    "cycle": cycle_label,
                })
            if len(examples) >= 6:
                break
        # Sort examples chronologically
        examples.sort(key=lambda d: d["date"])

        current_context = {
            "current_score": current_score,
            "current_price": current_price,
            "score_version": SCORE_VERSION,
            "metric_panel_id": "historical-common-v1",
            "coverage": current_coverage,
            "current_weighting_source": (
                "final_full_history_weights" if ml_mode and ml_artifact else "equal_weight"
            ),
            "percentile": percentile,
            "comparable_days": len(comparable),
            "avg_1yr_return": avg_1yr,
            "avg_30d_return": avg_returns.get("30d"),
            "avg_90d_return": avg_returns.get("90d"),
            "avg_180d_return": avg_returns.get("180d"),
            "examples": examples,
        }

    # --- Build time series for charting ---
    # Smart downsampling: daily for last 2 years, weekly before that
    # Include per-metric values so the frontend can plot any metric.
    chart_data = []
    import datetime as _dt
    try:
        last_date = _dt.datetime.strptime(daily_scores[-1]["date"], "%Y-%m-%d")
        cutoff_date = (last_date - _dt.timedelta(days=730)).strftime("%Y-%m-%d")
    except Exception:
        cutoff_date = "2024-01-01"

    # Collect all metric keys that were ever scored (for per-metric series)
    all_metric_keys = set()
    for d in daily_scores:
        all_metric_keys.update(d.get("metric_values", {}).keys())

    for i, d in enumerate(daily_scores):
        is_recent = d["date"] >= cutoff_date
        if is_recent or i % 7 == 0 or i == len(daily_scores) - 1:
            entry = {
                "date": d["date"],
                "score": d["score"],
                "price": d["price"],
            }
            # Include per-metric values (raw metric value, not score)
            metric_vals = d.get("metric_values", {})
            if metric_vals:
                entry["metrics"] = metric_vals
            chart_data.append(entry)

    if not ml_mode:
        ml_evaluation = {"requested": False, "is_out_of_sample": False}
    elif ml_plan:
        ml_evaluation = {
            "requested": True,
            "evaluation_scope": ml_plan["evaluation_scope"],
            "is_out_of_sample": ml_plan["is_out_of_sample"],
            "weighting_source": ml_plan["weighting_source"],
            "folds": len(ml_plan["folds"]),
            "artifact": ml_artifact_status,
        }
    else:
        ml_evaluation = {
            "requested": True,
            "evaluation_scope": "equal_weight_fallback",
            "is_out_of_sample": False,
            "weighting_source": "none_invalid_artifact",
            "folds": 0,
            "artifact": ml_artifact_status,
        }

    data_quality = _backtest_data_quality_metadata([day["n_metrics"] for day in daily_scores])
    result = {
        "date_range": {"start": daily_scores[0]["date"], "end": daily_scores[-1]["date"]},
        "total_days_scored": len(daily_scores),
        "metric_panel": data_quality["metric_panel"],
        "coverage": data_quality["coverage"],
        "staleness_days": data_quality["staleness_days"],
        "bracket_stats": bracket_stats,
        "signal_events": signal_events,
        "current_context": current_context,
        "chart_data": chart_data,
        "ml_mode": ml_mode,
        "ml_evaluation": ml_evaluation,
        "score_version": SCORE_VERSION,
        "computed_at": datetime.utcnow().isoformat() + "Z",
    }

    log.info("Backtest complete: %d days, %d signal events", len(daily_scores), len(signal_events))
    return result