# coding: utf-8
"""JoinQuant hosted wrapper.

Upload the generated bundle, not this source file, to JoinQuant.  The bundle
tool replaces the marked import block with the exact portable core source.
"""

# __PORTABLE_CORE_BUNDLE_START__
# Inlined by tools/bundle_platforms.py; do not edit this region.
import hashlib
import json
import math
import re


_CANONICAL_EXCHANGES = {
    "SH": "XSHG",
    "SSE": "XSHG",
    "XSHG": "XSHG",
    "SZ": "XSHE",
    "SZE": "XSHE",
    "SZSE": "XSHE",
    "XSHE": "XSHE",
    "BJ": "XBSE",
    "BSE": "XBSE",
    "XBSE": "XBSE",
}

_SHORT_EXCHANGES = {
    "XSHG": "SH",
    "XSHE": "SZ",
    "XBSE": "BJ",
}

_TARGET_PACKAGE_FIELDS = {
    "schema_version",
    "package_type",
    "package_id",
    "decision_id",
    "release",
    "evidence_class",
    "model_id",
    "signal_as_of",
    "next_session",
    "model_sha256",
    "data_sha256",
    "feature_sha256",
    "risk_sha256",
    "cost_sha256",
    "optimizer_sha256",
    "config_sha256",
    "source_sha256",
    "target_weights",
    "universe",
    "constraints",
    "package_sha256",
}

_TARGET_PACKAGE_HASH_FIELDS = (
    "model_sha256",
    "data_sha256",
    "feature_sha256",
    "risk_sha256",
    "cost_sha256",
    "optimizer_sha256",
    "config_sha256",
    "source_sha256",
)


class TargetPackageLookupMiss(ValueError, LookupError):
    """An already verified tape has no exact requested decision."""


def _sum_numeric(values):
    """Sum numerics without trusting a hosted runtime's global ``sum`` name."""

    total = 0.0
    for value in values:
        total += value
    return total


def _any_true(values):
    """Return built-in ``any`` semantics without trusting hosted globals."""

    for value in values:
        if value:
            return True
    return False


def _infer_exchange(code):
    if code.startswith(("4", "8")):
        return "XBSE"
    if code.startswith(("5", "6", "9")):
        return "XSHG"
    return "XSHE"


def _split_symbol(symbol):
    if not isinstance(symbol, str):
        raise TypeError("symbol must be a string")
    value = symbol.strip().upper().replace(" ", "")
    if not value:
        raise ValueError("symbol must not be empty")

    match = re.match(r"^(\d{6})\.(XSHG|XSHE|XBSE|SH|SZ|BJ)$", value)
    if match:
        return match.group(1), _CANONICAL_EXCHANGES[match.group(2)]

    match = re.match(r"^(XSHG|XSHE|XBSE|SH|SZ|BJ)[\.:]?(\d{6})$", value)
    if match:
        return match.group(2), _CANONICAL_EXCHANGES[match.group(1)]

    match = re.match(r"^(\d{6})[\.:](SSE|SZE|SZSE|BSE)$", value)
    if match:
        return match.group(1), _CANONICAL_EXCHANGES[match.group(2)]

    if re.match(r"^\d{6}$", value):
        return value, _infer_exchange(value)
    raise ValueError("unsupported A-share symbol: %s" % symbol)


def normalize_symbol(symbol, target="canonical"):
    """Convert common A-share codes to canonical/JQ, QMT, or Qlib format."""

    code, exchange = _split_symbol(symbol)
    target_name = str(target).strip().lower()
    if target_name in ("canonical", "joinquant", "jq"):
        return "%s.%s" % (code, exchange)
    if target_name in ("qmt", "xtquant"):
        return "%s.%s" % (code, _SHORT_EXCHANGES[exchange])
    if target_name == "qlib":
        return "%s%s" % (_SHORT_EXCHANGES[exchange], code)
    raise ValueError("unsupported target platform: %s" % target)


def convert_symbol(symbol, target="canonical"):
    """Alias kept as the stable adapter-facing name."""

    return normalize_symbol(symbol, target)


def _canonical_json(value):
    """Match ``quant60.ledger.canonical_json`` for plain JSON values."""

    return json.dumps(
        value,
        ensure_ascii=False,
        sort_keys=True,
        separators=(",", ":"),
        allow_nan=False,
    )


def _json_copy(value, name):
    try:
        return json.loads(_canonical_json(value))
    except (TypeError, ValueError) as error:
        raise ValueError("%s must contain finite JSON values only" % name) from error


def _canonical_mapping(value, name, value_validator):
    if not hasattr(value, "items"):
        raise TypeError("%s must be a mapping" % name)
    output = {}
    for raw_symbol, raw_value in value.items():
        symbol = normalize_symbol(raw_symbol)
        if symbol in output:
            raise ValueError(
                "%s contains duplicate normalized symbol %s" % (name, symbol)
            )
        output[symbol] = value_validator(raw_value, "%s.%s" % (name, symbol))
    return dict((symbol, output[symbol]) for symbol in sorted(output))


def _finite_non_negative(value, name):
    if isinstance(value, bool):
        raise ValueError("%s must be a finite non-negative number" % name)
    try:
        numeric = float(value)
    except (TypeError, ValueError):
        raise ValueError("%s must be a finite non-negative number" % name)
    if not math.isfinite(numeric) or numeric < 0.0:
        raise ValueError("%s must be a finite non-negative number" % name)
    return numeric


def _non_negative_quantity(value, name):
    numeric = _finite_non_negative(value, name)
    if numeric != math.floor(numeric):
        raise ValueError("%s must be a whole-share quantity" % name)
    return int(numeric)


def _positive_price(value, name):
    numeric = _finite_non_negative(value, name)
    if numeric <= 0.0:
        raise ValueError("%s must be positive" % name)
    return numeric


def _optional_non_empty_string(value, name):
    if value is None:
        return None
    if not isinstance(value, str) or not value.strip():
        raise ValueError("%s must be a non-empty string when provided" % name)
    return value


def _sha256_hex(value, name, allow_none=False):
    if value is None and allow_none:
        return None
    if (
        not isinstance(value, str)
        or len(value) != 64
        or _any_true(
            character not in "0123456789abcdef"
            for character in value
        )
    ):
        raise ValueError(
            "%s must be a 64-character lowercase SHA-256 digest" % name
        )
    if value == "0" * 64:
        raise ValueError("%s cannot be an all-zero placeholder" % name)
    return value


def _reject_quantity_fields(value, path="$"):
    if hasattr(value, "items"):
        for raw_key, child in value.items():
            key = str(raw_key)
            normalized = key.lower().replace("-", "_")
            if "quantity" in normalized or "quantities" in normalized:
                raise ValueError(
                    "%s.%s: quantities do not belong in TargetPackageV1"
                    % (path, key)
                )
            _reject_quantity_fields(child, "%s.%s" % (path, key))
    elif isinstance(value, list):
        for index, child in enumerate(value):
            _reject_quantity_fields(child, "%s[%d]" % (path, index))


def _verify_identifier(value, name):
    if (
        not isinstance(value, str)
        or re.match(r"^[A-Za-z0-9][A-Za-z0-9._:-]{0,127}$", value) is None
    ):
        raise ValueError("%s is not a canonical identifier" % name)
    return value


def _verify_target_package_clock(signal_as_of, next_session):
    if (
        not isinstance(signal_as_of, str)
        or re.match(
            r"^[0-9]{4}-[0-9]{2}-[0-9]{2}T15:00:00\+08:00$",
            signal_as_of,
        )
        is None
    ):
        raise ValueError("signal_as_of must be canonical China-market close")
    if (
        not isinstance(next_session, str)
        or re.match(r"^[0-9]{4}-[0-9]{2}-[0-9]{2}$", next_session) is None
    ):
        raise ValueError("next_session must use canonical YYYY-MM-DD")
    if next_session <= signal_as_of[:10]:
        raise ValueError("next_session must follow signal_as_of")


def _target_package_content_sha256(package):
    body = dict(package)
    body.pop("package_sha256", None)
    _reject_quantity_fields(body)
    try:
        payload = _canonical_json(body).encode("utf-8")
    except (TypeError, ValueError) as error:
        raise ValueError(
            "target package must contain finite JSON values only"
        ) from error
    return hashlib.sha256(payload).hexdigest()


def verify_target_package(
    package,
    expected_signal_as_of=None,
    expected_next_session=None,
    expected_decision_id=None,
    expected_package_sha256=None,
):
    """Verify the platform-critical subset of a sealed ``TargetPackageV1``.

    This intentionally duplicates a small, Python-3.6-compatible safety
    boundary from the richer local validator.  Hosted runtimes must verify the
    embedded package again instead of trusting the bundling process.
    """

    if not hasattr(package, "items"):
        raise TypeError("target package must be a mapping")
    candidate = _json_copy(package, "target package")
    _reject_quantity_fields(candidate)
    fields = set(candidate)
    if fields != _TARGET_PACKAGE_FIELDS:
        missing = sorted(_TARGET_PACKAGE_FIELDS - fields)
        unknown = sorted(fields - _TARGET_PACKAGE_FIELDS)
        raise ValueError(
            "target package fields mismatch; missing=%s, unknown=%s"
            % (missing, unknown)
        )
    if candidate["schema_version"] != "1.0":
        raise ValueError("unsupported target package schema_version")
    if candidate["package_type"] != "TargetPackageV1":
        raise ValueError("unsupported target package type")
    for name in (
        "package_id",
        "decision_id",
        "release",
        "evidence_class",
        "model_id",
    ):
        _verify_identifier(candidate[name], name)
    _verify_target_package_clock(
        candidate["signal_as_of"], candidate["next_session"]
    )
    for name in _TARGET_PACKAGE_HASH_FIELDS:
        _sha256_hex(candidate[name], name)
    _sha256_hex(candidate["package_sha256"], "package_sha256")

    raw_weights = candidate["target_weights"]
    if not hasattr(raw_weights, "items") or not raw_weights:
        raise ValueError("target_weights must be a non-empty mapping")
    weights = {}
    for raw_symbol, raw_weight in raw_weights.items():
        symbol = normalize_symbol(raw_symbol)
        if symbol != raw_symbol:
            raise ValueError(
                "target_weights symbol must be canonical: %s" % raw_symbol
            )
        if symbol in weights:
            raise ValueError("duplicate target weight for %s" % symbol)
        weight = _finite_non_negative(
            raw_weight, "target_weights.%s" % symbol
        )
        if weight > 1.0:
            raise ValueError("target weight exceeds one for %s" % symbol)
        weights[symbol] = weight
    gross = math.fsum(weights.values())
    if gross > 1.0 + 1e-12:
        raise ValueError("target_weights gross exceeds one")

    universe = candidate["universe"]
    if not hasattr(universe, "items") or not universe:
        raise ValueError("universe must be a non-empty mapping")
    canonical_universe = {}
    for raw_symbol, raw_state in universe.items():
        symbol = normalize_symbol(raw_symbol)
        if symbol != raw_symbol:
            raise ValueError("universe symbol must be canonical: %s" % raw_symbol)
        if symbol in canonical_universe:
            raise ValueError("duplicate universe symbol %s" % symbol)
        if not hasattr(raw_state, "items"):
            raise ValueError("universe state must be a mapping for %s" % symbol)
        state = raw_state.get("state")
        if state not in (
            "openable",
            "hold_only",
            "sell_only",
            "frozen",
            "excluded",
        ):
            raise ValueError("unsupported universe state for %s" % symbol)
        canonical_universe[symbol] = state
    missing_universe = sorted(set(weights) - set(canonical_universe))
    if missing_universe:
        raise ValueError(
            "target symbols are absent from universe: %s" % missing_universe
        )
    for symbol, weight in weights.items():
        if canonical_universe[symbol] == "excluded" and weight > 1e-12:
            raise ValueError("excluded symbol has positive weight: %s" % symbol)

    constraints = candidate["constraints"]
    if not hasattr(constraints, "items"):
        raise ValueError("constraints must be a mapping")
    if constraints.get("long_only") is not True:
        raise ValueError("TargetPackageV1 must be long-only")
    declared_gross = _finite_non_negative(
        constraints.get("actual_gross_weight"),
        "constraints.actual_gross_weight",
    )
    max_gross = _finite_non_negative(
        constraints.get("max_gross_weight"),
        "constraints.max_gross_weight",
    )
    max_single = _finite_non_negative(
        constraints.get("max_single_weight"),
        "constraints.max_single_weight",
    )
    if abs(declared_gross - gross) > 1e-12:
        raise ValueError("declared gross does not equal target_weights gross")
    if gross > max_gross + 1e-12 or max_gross > 1.0:
        raise ValueError("target_weights exceed max_gross_weight")
    if max_single <= 0.0 or max_single > 1.0:
        raise ValueError("invalid max_single_weight")
    if _any_true(
        weight > max_single + 1e-12 for weight in weights.values()
    ):
        raise ValueError("target_weights exceed max_single_weight")
    model_claim = " ".join(
        (
            candidate["release"],
            candidate["evidence_class"],
        )
    ).lower()
    if (
        "baseline" in model_claim
        and "portable-momentum" in candidate["model_id"].lower()
    ):
        raise ValueError(
            "portable-momentum cannot claim a baseline target package"
        )

    expected_hash = _target_package_content_sha256(candidate)
    if candidate["package_sha256"] != expected_hash:
        raise ValueError("target package content hash mismatch")
    if (
        expected_signal_as_of is not None
        and candidate["signal_as_of"] != expected_signal_as_of
    ):
        raise ValueError("target package signal_as_of does not match request")
    if (
        expected_next_session is not None
        and candidate["next_session"] != expected_next_session
    ):
        raise ValueError("target package next_session does not match request")
    if (
        expected_decision_id is not None
        and candidate["decision_id"] != expected_decision_id
    ):
        raise ValueError("target package decision_id does not match request")
    if expected_package_sha256 is not None:
        _sha256_hex(
            expected_package_sha256,
            "expected_package_sha256",
        )
        if candidate["package_sha256"] != expected_package_sha256:
            raise ValueError("target package digest does not match request")
    return candidate


def target_package_tape_sha256(packages):
    """Hash the exact ordered canonical JSON package array."""

    if not isinstance(packages, (list, tuple)):
        raise TypeError("target package tape must be a list or tuple")
    try:
        payload = _canonical_json(list(packages)).encode("utf-8")
    except (TypeError, ValueError) as error:
        raise ValueError(
            "target package tape must contain finite JSON values only"
        ) from error
    return hashlib.sha256(payload).hexdigest()


def lookup_target_package_exact(
    packages,
    signal_as_of,
    next_session,
    decision_id=None,
    expected_tape_sha256=None,
    expected_count=None,
):
    """Verify a complete tape, then return one exact dual-clock decision."""

    if not isinstance(packages, (list, tuple)) or not packages:
        raise ValueError("target package tape must be a non-empty list or tuple")
    _verify_target_package_clock(signal_as_of, next_session)
    decision_id = _optional_non_empty_string(decision_id, "decision_id")
    if expected_count is not None:
        if (
            isinstance(expected_count, bool)
            or not isinstance(expected_count, int)
            or expected_count < 0
        ):
            raise ValueError("expected_count must be a non-negative integer")
        if len(packages) != expected_count:
            raise ValueError("target package tape count mismatch")
    if expected_tape_sha256 is not None:
        _sha256_hex(expected_tape_sha256, "expected_tape_sha256")
        if target_package_tape_sha256(packages) != expected_tape_sha256:
            raise ValueError("target package tape digest mismatch")

    verified = []
    package_ids = set()
    decision_ids = set()
    clock_keys = set()
    for raw_package in packages:
        package = verify_target_package(raw_package)
        clock_key = (package["signal_as_of"], package["next_session"])
        if package["package_id"] in package_ids:
            raise ValueError("duplicate target package_id")
        if package["decision_id"] in decision_ids:
            raise ValueError("duplicate target decision_id")
        if clock_key in clock_keys:
            raise ValueError("duplicate target package clock")
        package_ids.add(package["package_id"])
        decision_ids.add(package["decision_id"])
        clock_keys.add(clock_key)
        verified.append(package)

    matches = [
        package
        for package in verified
        if package["signal_as_of"] == signal_as_of
        and package["next_session"] == next_session
    ]
    if not matches:
        raise TargetPackageLookupMiss(
            "no target package for exact signal_as_of/next_session clock"
        )
    if len(matches) != 1:
        raise ValueError("ambiguous exact target package decision")
    if (
        decision_id is not None
        and matches[0]["decision_id"] != decision_id
    ):
        raise ValueError("exact target package decision_id mismatch")
    return _json_copy(matches[0], "target package")


def momentum_score(prices, lookback=20, skip=0):
    """Return trailing simple momentum, or ``None`` if history is insufficient.

    ``skip=0`` uses the latest completed decision bar.  Set ``skip=1`` only
    when intentionally implementing a 20-1 convention.  A lookback of N needs
    N+skip+1 observations.
    """

    lookback = int(lookback)
    skip = int(skip)
    if lookback <= 0 or skip < 0:
        raise ValueError("lookback must be positive and skip non-negative")
    values = list(prices)
    if len(values) < lookback + skip + 1:
        return None
    end_index = len(values) - 1 - skip
    start_index = end_index - lookback
    start = float(values[start_index])
    end = float(values[end_index])
    if (
        start <= 0.0
        or end <= 0.0
        or not math.isfinite(start)
        or not math.isfinite(end)
    ):
        return None
    return end / start - 1.0


def rank_momentum(price_history, lookback=20, skip=0, top_n=None):
    """Rank ``{symbol: closes}`` by momentum with deterministic tie-breaking."""

    ranked = []
    for symbol in sorted(price_history):
        score = momentum_score(price_history[symbol], lookback, skip)
        if score is not None and math.isfinite(score):
            ranked.append((normalize_symbol(symbol), score))
    ranked.sort(key=lambda item: (-item[1], item[0]))
    if top_n is not None:
        ranked = ranked[: max(0, int(top_n))]
    return ranked


def capped_target_weights(
    scores, max_weight=0.20, gross_target=1.0, min_score=0.0
):
    """Long-only score-proportional weights with iterative cap redistribution."""

    max_weight = float(max_weight)
    gross_target = float(gross_target)
    min_score = float(min_score)
    if not math.isfinite(max_weight) or not 0.0 < max_weight <= 1.0:
        raise ValueError("max_weight must be finite and lie in (0, 1]")
    if not math.isfinite(gross_target) or not 0.0 <= gross_target <= 1.0:
        raise ValueError("gross_target must be finite and lie in [0, 1]")
    if not math.isfinite(min_score):
        raise ValueError("min_score must be finite")

    if hasattr(scores, "items"):
        items = list(scores.items())
    else:
        items = list(scores)
    normalized = {}
    for symbol, raw_score in items:
        name = normalize_symbol(symbol)
        score = float(raw_score)
        if not math.isfinite(score):
            raise ValueError("score must be finite for %s" % name)
        normalized[name] = score

    weights = dict((symbol, 0.0) for symbol in normalized)
    active = dict(
        (symbol, score)
        for symbol, score in normalized.items()
        if score > min_score and score > 0.0
    )
    remaining = min(gross_target, len(active) * max_weight)

    while active and remaining > 1e-15:
        score_sum = _sum_numeric(active.values())
        if score_sum <= 0.0:
            break
        capped = []
        for symbol in sorted(active):
            proposed = remaining * active[symbol] / score_sum
            if proposed >= max_weight - 1e-15:
                weights[symbol] = max_weight
                capped.append(symbol)
        if not capped:
            for symbol in sorted(active):
                weights[symbol] = remaining * active[symbol] / score_sum
            remaining = 0.0
            break
        for symbol in capped:
            active.pop(symbol)
            remaining -= max_weight
        remaining = max(0.0, remaining)
    return weights


def round_board_lot(quantity, lot_size=100):
    """Round an absolute long position down to a board-lot quantity."""

    lot_size = int(lot_size)
    if lot_size <= 0:
        raise ValueError("lot_size must be positive")
    if not math.isfinite(float(quantity)):
        raise ValueError("quantity must be finite")
    quantity = max(0, int(quantity))
    return quantity // lot_size * lot_size


def _is_star_market(symbol):
    code, exchange = _split_symbol(symbol)
    return exchange == "XSHG" and code.startswith(("688", "689"))


def target_quantities(
    weights, prices, equity, lot_size=100, cash_buffer=0.02
):
    """Convert target weights to affordable board-lot long quantities."""

    equity = float(equity)
    cash_buffer = float(cash_buffer)
    if (
        not math.isfinite(equity)
        or equity < 0.0
        or not math.isfinite(cash_buffer)
        or not 0.0 <= cash_buffer < 1.0
    ):
        raise ValueError("invalid equity or cash_buffer")
    positive_weights = []
    for raw_symbol, raw_weight in weights.items():
        weight = float(raw_weight)
        if not math.isfinite(weight):
            raise ValueError(
                "weight must be finite for %s" % normalize_symbol(raw_symbol)
            )
        positive_weights.append(max(0.0, weight))
    requested_gross = _sum_numeric(positive_weights)
    gross_ceiling = 1.0 - cash_buffer
    scale = (
        min(1.0, gross_ceiling / requested_gross)
        if requested_gross > 0.0
        else 1.0
    )
    result = {}
    for raw_symbol in sorted(weights):
        symbol = normalize_symbol(raw_symbol)
        weight = float(weights[raw_symbol])
        if not math.isfinite(weight):
            raise ValueError("weight must be finite for %s" % symbol)
        weight = max(0.0, weight)
        price = prices.get(raw_symbol)
        if price is None:
            price = prices.get(symbol)
        if price is None:
            raise KeyError("missing price for %s" % symbol)
        price = float(price)
        if price <= 0.0 or not math.isfinite(price):
            raise ValueError("invalid price for %s" % symbol)
        # Weights are absolute equity weights.  cash_buffer is a ceiling, not
        # a second multiplicative haircut: gross=.95 and buffer=.02 stays .95.
        budget = equity * weight * scale
        quantity = round_board_lot(budget / price, lot_size)
        # STAR Market auction orders must contain at least 200 shares.  Keep
        # the portable planner conservative and on the configured 100-share
        # grid even though quantities above 200 may increment by one share.
        if _is_star_market(symbol) and 0 < quantity < 200:
            quantity = 0
        result[symbol] = quantity
    return result


def order_deltas(
    targets, current, sellable=None, lot_size=100
):
    """Return signed board-lot orders; negative sells respect sellable quantity."""

    canonical_targets = {}
    canonical_current = {}
    canonical_sellable = {}
    for symbol, quantity in targets.items():
        canonical_targets[normalize_symbol(symbol)] = max(0, int(quantity))
    for symbol, quantity in current.items():
        canonical_current[normalize_symbol(symbol)] = max(0, int(quantity))
    if sellable is not None:
        for symbol, quantity in sellable.items():
            canonical_sellable[normalize_symbol(symbol)] = max(0, int(quantity))

    result = {}
    symbols = sorted(set(canonical_targets) | set(canonical_current))
    for symbol in symbols:
        target = round_board_lot(canonical_targets.get(symbol, 0), lot_size)
        if _is_star_market(symbol) and 0 < target < 200:
            target = 0
        held = canonical_current.get(symbol, 0)
        delta = target - held
        if delta > 0:
            delta = round_board_lot(delta, lot_size)
            if _is_star_market(symbol) and 0 < delta < 200:
                delta = 0
        elif delta < 0:
            available = held
            if sellable is not None:
                available = min(held, canonical_sellable.get(symbol, 0))
            requested = min(-delta, available)
            # A complete liquidation may submit the entire odd-lot balance.
            # This also covers the STAR exception for a remaining balance
            # below 200 shares.  Partial orders stay on the configured grid.
            full_liquidation = (
                target == 0
                and requested == held
                and available >= held
            )
            if full_liquidation:
                delta = -held
            else:
                delta = -round_board_lot(requested, lot_size)
                if _is_star_market(symbol) and 0 < -delta < 200:
                    delta = 0
        if delta:
            result[symbol] = delta
    return result


def build_target_weight_plan(
    target_weights,
    prices,
    current,
    sellable,
    equity,
    cash_buffer=0.02,
    lot_size=100,
    decision_id=None,
    package_sha256=None,
):
    """Bind canonical post-risk weights to one account without recomputing Alpha.

    ``prices`` must cover exactly every target or currently managed symbol.
    Missing prices fail closed even for a held symbol whose desired weight is
    zero.  A missing ``sellable`` entry is safe and means zero sellable shares.
    """

    weights = _canonical_mapping(
        target_weights,
        "target_weights",
        _finite_non_negative,
    )
    gross = math.fsum(weights.values())
    if _any_true(weight > 1.0 for weight in weights.values()):
        raise ValueError("target weight must not exceed one")
    if gross > 1.0 + 1e-12:
        raise ValueError("target weight gross must not exceed one")

    canonical_current = _canonical_mapping(
        current,
        "current",
        _non_negative_quantity,
    )
    canonical_sellable = _canonical_mapping(
        sellable,
        "sellable",
        _non_negative_quantity,
    )
    unknown_sellable = sorted(set(canonical_sellable) - set(canonical_current))
    if unknown_sellable:
        raise ValueError(
            "sellable contains symbols absent from current: %s"
            % unknown_sellable
        )
    for symbol, quantity in canonical_sellable.items():
        if quantity > canonical_current[symbol]:
            raise ValueError(
                "sellable exceeds current quantity for %s" % symbol
            )

    canonical_prices = _canonical_mapping(
        prices,
        "prices",
        _positive_price,
    )
    managed_symbols = set(weights) | set(canonical_current)
    price_symbols = set(canonical_prices)
    missing_prices = sorted(managed_symbols - price_symbols)
    extra_prices = sorted(price_symbols - managed_symbols)
    if missing_prices or extra_prices:
        if missing_prices:
            raise KeyError(
                "prices do not cover managed symbols: missing=%s"
                % missing_prices
            )
        raise ValueError(
            "prices contain unmanaged symbols: extra=%s" % extra_prices
        )

    complete_weights = dict(
        (symbol, weights.get(symbol, 0.0))
        for symbol in sorted(managed_symbols)
    )
    targets = target_quantities(
        complete_weights,
        canonical_prices,
        equity,
        lot_size,
        cash_buffer,
    )
    orders = order_deltas(
        targets,
        canonical_current,
        canonical_sellable,
        lot_size,
    )
    decision_id = _optional_non_empty_string(decision_id, "decision_id")
    package_sha256 = _sha256_hex(
        package_sha256,
        "package_sha256",
        allow_none=True,
    )
    return {
        "decision_id": decision_id,
        "package_sha256": package_sha256,
        "weights": complete_weights,
        "targets": targets,
        "orders": orders,
    }


def build_rebalance_plan(
    price_history,
    current,
    sellable,
    equity,
    lookback=20,
    skip=0,
    top_n=10,
    max_weight=0.20,
    gross_target=1.0,
    cash_buffer=0.02,
    lot_size=100,
):
    """Build the complete portable signal -> weight -> quantity -> order plan."""

    ranked = rank_momentum(price_history, lookback, skip, top_n)
    scores = dict(ranked)
    weights = capped_target_weights(scores, max_weight, gross_target, 0.0)
    for raw_symbol in price_history:
        symbol = normalize_symbol(raw_symbol)
        if symbol not in scores:
            scores[symbol] = momentum_score(
                price_history[raw_symbol], lookback, skip
            )
        if symbol not in weights:
            weights[symbol] = 0.0
    prices = {}
    for raw_symbol, history in price_history.items():
        if not history:
            continue
        prices[normalize_symbol(raw_symbol)] = history[-1]
    targets = target_quantities(
        weights, prices, equity, lot_size, cash_buffer
    )
    orders = order_deltas(targets, current, sellable, lot_size)
    return {
        "scores": scores,
        "weights": weights,
        "targets": targets,
        "orders": orders,
    }


__all__ = [
    "build_rebalance_plan",
    "build_target_weight_plan",
    "capped_target_weights",
    "convert_symbol",
    "lookup_target_package_exact",
    "momentum_score",
    "normalize_symbol",
    "order_deltas",
    "rank_momentum",
    "round_board_lot",
    "TargetPackageLookupMiss",
    "target_package_tape_sha256",
    "target_quantities",
    "verify_target_package",
]
# __PORTABLE_CORE_BUNDLE_END__

import base64 as _q60_base64
import json as _q60_json
import math


class PriceHistoryUnavailable(RuntimeError):
    pass


class UnmanagedPositionError(RuntimeError):
    pass


class ClockStateError(RuntimeError):
    pass


# __BASELINE_CONFIG_START__
# Generated from configs/baseline.json; do not edit this region.
CONFIG = {'decision_mode': 'target_package',
 'target_package_count': 1,
 'target_package_tape_sha256': '5383c376951637b8d4b1ca42fc6db80347abda74c0181c0f70d6c411fb97ea64',
 'universe_mode': 'fixed',
 'index_symbol': '000905.XSHG',
 'qmt_sector_name': '\u4e2d\u8bc1500',
 'dedicated_account_required': True,
 'exclude_st': True,
 'universe': ['000001.XSHE',
              '000333.XSHE',
              '000651.XSHE',
              '002415.XSHE',
              '002594.XSHE',
              '300059.XSHE',
              '300750.XSHE',
              '600000.XSHG',
              '600519.XSHG',
              '601012.XSHG',
              '601318.XSHG',
              '688981.XSHG'],
 'lookback': 20,
 'skip': 0,
 'top_n': 20,
 'max_weight': 0.05,
 'gross_target': 0.95,
 'cash_buffer': 0.02,
 'lot_size': 100,
 'participation_rate': 0.1,
 'slippage_bps': 2.0,
 'commission_rate': 0.0002,
 'minimum_commission': 5.0,
 'stamp_duty_rate': 0.0005,
 'transfer_fee_rate': 1e-05,
 'rebalance_schedule': 'weekly_first_close'}
# __BASELINE_CONFIG_END__

# __TARGET_PACKAGE_TAPE_START__
# Generated by tools/bundle_platforms.py; do not edit this region.
TARGET_PACKAGES = [{'config_sha256': 'e4edf1eb730597e220266c1f8007ddb5e1ab4d26a490cd10738823527d030470',
  'constraints': {'actual_gross_weight': 0.4,
                  'constraint_state': 'FEASIBLE',
                  'long_only': True,
                  'max_gross_weight': 0.9,
                  'max_one_way_turnover': 0.45,
                  'max_single_weight': 0.1},
  'cost_sha256': 'e2a2d8e6d3829c105a9f6c9ec80d9df18c2dee0553aafeb761abb79d378769a7',
  'data_sha256': 'f5b3e66b0a94eeccb44a114bf3a0bbfad44b5d5da9d5b698c81e83a3e0007d03',
  'decision_id': 'RD-20240311',
  'evidence_class': 'synthetic-research',
  'feature_sha256': '24e671cebc35afe7497b69c59d48bb5bad9401e30c1607f0fc41a69f59082aa1',
  'model_id': 'deterministic-ridge-alpha-5',
  'model_sha256': 'e14e8df7e2f86e6b3178a6e500d1d2c0427bb02d187b13cb4ec672bbf5f9f334',
  'next_session': '2024-03-12',
  'optimizer_sha256': 'b6aba6ab22fc375e89e4bfbdffb10c7261a2fae01b0183265caacb589643c31e',
  'package_id': 'TP-20240311-795dcfba',
  'package_sha256': 'baa15c8156aa6cb54d3775d0cd562613a9d0a4f15b0c39f2a60e0f8b8c6f1f3a',
  'package_type': 'TargetPackageV1',
  'release': 'quant60-research-candidate-v1',
  'risk_sha256': 'a3d95aa6cd569d2452856b148df053b040b2acc2e6b993bbd1072b48b46cde5a',
  'schema_version': '1.0',
  'signal_as_of': '2024-03-11T15:00:00+08:00',
  'source_sha256': '86c1e0514f608b2012b4084dbc947eada90e8015fccbdf8ae13ae99340ede178',
  'target_weights': {'000001.XSHE': 0.0,
                     '000333.XSHE': 0.0,
                     '000651.XSHE': 0.0,
                     '002415.XSHE': 0.0,
                     '002594.XSHE': 0.0,
                     '300059.XSHE': 0.0,
                     '300750.XSHE': 0.0,
                     '600000.XSHG': 0.0,
                     '600519.XSHG': 0.1,
                     '601012.XSHG': 0.1,
                     '601318.XSHG': 0.1,
                     '688981.XSHG': 0.1},
  'universe': {'000001.XSHE': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '000333.XSHE': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '000651.XSHE': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '002415.XSHE': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '002594.XSHE': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '300059.XSHE': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '300750.XSHE': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '600000.XSHG': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '600519.XSHG': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '601012.XSHG': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '601318.XSHG': {'reasons': ['ELIGIBLE'], 'state': 'openable'},
               '688981.XSHG': {'reasons': ['ELIGIBLE'], 'state': 'openable'}}}]
# __TARGET_PACKAGE_TAPE_END__


MOMENTUM_SMOKE_MODE = "portable_momentum_smoke"
TARGET_PACKAGE_MODE = "target_package"
EVIDENCE_PREFIX = "QUANT60_JOINQUANT_EVIDENCE_V1 "
EVIDENCE_CHUNK_CHARACTERS = 1800
TARGET_PRICE_SOURCE = "CURRENT_DATA_DAY_OPEN"


def _evidence_value(value, depth=0):
    """Return stable JSON data without allowing observation to stop trading."""
    if depth > 8:
        return "<max-depth>"
    if value is None or isinstance(value, (bool, int, str)):
        return value
    if isinstance(value, float):
        return value if math.isfinite(value) else None
    if isinstance(value, dict):
        return {
            str(key): _evidence_value(item, depth + 1)
            for key, item in sorted(
                value.items(),
                key=lambda pair: str(pair[0]),
            )
        }
    if isinstance(value, (list, tuple)):
        return [_evidence_value(item, depth + 1) for item in value]
    isoformat = getattr(value, "isoformat", None)
    if callable(isoformat):
        try:
            return str(isoformat())
        except Exception:
            return "<isoformat-error>"
    try:
        return str(value)
    except Exception:
        return "<string-error>"


def _evidence_context_time(context):
    try:
        return _evidence_value(getattr(context, "current_dt", None))
    except Exception:
        return None


def _emit_evidence(
    event,
    payload=None,
    context=None,
    chunk_index=None,
    chunk_count=None,
):
    """Print one prefixed canonical JSON record and always fail soft."""
    try:
        sequence = int(
            getattr(g, "quant60_evidence_sequence", 0) or 0
        ) + 1
        g.quant60_evidence_sequence = sequence
        record = {
            "context_time": _evidence_context_time(context),
            "decision_mode": getattr(
                g,
                "quant60_decision_mode",
                CONFIG.get("decision_mode"),
            ),
            "event": str(event),
            "payload": _evidence_value(payload or {}),
            "schema_version": "1.0",
            "sequence": sequence,
            "source": "joinquant_hosted",
        }
        if chunk_index is not None or chunk_count is not None:
            if (
                type(chunk_index) is not int
                or type(chunk_count) is not int
                or chunk_index <= 0
                or chunk_count <= 0
                or chunk_index > chunk_count
            ):
                return None
            record["chunk_index"] = chunk_index
            record["chunk_count"] = chunk_count
        encoded = _q60_json.dumps(
            record,
            ensure_ascii=True,
            sort_keys=True,
            separators=(",", ":"),
            allow_nan=False,
        )
        print(EVIDENCE_PREFIX + encoded)
    except Exception:
        # Evidence is observational. A logging/serialization failure must
        # never alter an already verified decision or hosted order path.
        return None
    return record


def _emit_chunked_evidence(event, payload, context=None):
    """Emit reconstructable base64 chunks of one canonical JSON payload."""
    try:
        canonical = _q60_json.dumps(
            _evidence_value(payload),
            ensure_ascii=True,
            sort_keys=True,
            separators=(",", ":"),
            allow_nan=False,
        ).encode("ascii")
        encoded = _q60_base64.b64encode(canonical).decode("ascii")
        chunks = [
            encoded[index : index + EVIDENCE_CHUNK_CHARACTERS]
            for index in range(0, len(encoded), EVIDENCE_CHUNK_CHARACTERS)
        ] or [""]
        count = len(chunks)
        for index, content in enumerate(chunks, 1):
            _emit_evidence(
                event,
                {
                    "chunk_encoding": "base64-canonical-json",
                    "content": content,
                    "encoded_length": len(encoded),
                },
                context,
                chunk_index=index,
                chunk_count=count,
            )
    except Exception:
        return None
    return count


def _observe_object(value, fields):
    if value is None:
        return None
    output = {"object_type": type(value).__name__}
    for name in fields:
        try:
            item = getattr(value, name)
        except Exception:
            continue
        if callable(item):
            continue
        output[name] = _evidence_value(item)
    return output


def _observe_order(value):
    return _observe_object(
        value,
        (
            "order_id",
            "add_time",
            "security",
            "amount",
            "filled",
            "price",
            "avg_cost",
            "side",
            "action",
            "status",
            "target_quantity",
        ),
    )


def _observe_trade(value):
    return _observe_object(
        value,
        (
            "trade_id",
            "order_id",
            "time",
            "security",
            "amount",
            "price",
        ),
    )


def _observe_platform_collection(api_name, observer):
    """Read an optional hosted collection without assuming API availability."""
    function = globals().get(api_name)
    if not callable(function):
        return {
            "available": False,
            "reason": "API_UNAVAILABLE",
        }
    try:
        raw = function()
    except Exception as exc:
        return {
            "available": False,
            "error_type": type(exc).__name__,
            "reason": "API_CALL_FAILED",
        }
    if hasattr(raw, "items"):
        items = [
            {
                "key": str(key),
                "value": observer(value),
            }
            for key, value in sorted(
                raw.items(),
                key=lambda pair: str(pair[0]),
            )
        ]
    elif isinstance(raw, (list, tuple)):
        items = [observer(value) for value in raw]
    elif raw is None:
        items = []
    else:
        items = [observer(raw)]
    return {
        "available": True,
        "count": len(items),
        "items": items,
    }


def _observe_portfolio(context):
    try:
        portfolio = context.portfolio
    except Exception as exc:
        return {
            "available": False,
            "error_type": type(exc).__name__,
        }
    output = {
        "available": True,
        "fields": {},
        "positions": [],
    }
    for name in (
        "total_value",
        "cash",
        "available_cash",
        "positions_value",
        "returns",
    ):
        try:
            value = getattr(portfolio, name)
        except Exception:
            continue
        output["fields"][name] = _evidence_value(value)
    try:
        positions = getattr(portfolio, "positions", {})
        if hasattr(positions, "items"):
            for symbol, position in sorted(
                positions.items(),
                key=lambda pair: str(pair[0]),
            ):
                item = _observe_object(
                    position,
                    (
                        "total_amount",
                        "closeable_amount",
                        "price",
                        "avg_cost",
                        "value",
                    ),
                )
                item["symbol"] = str(symbol)
                output["positions"].append(item)
    except Exception as exc:
        output["positions_error_type"] = type(exc).__name__
    return output


def _plan_evidence(plan):
    if not isinstance(plan, dict):
        return None
    return {
        "decision_mode": plan.get("decision_mode"),
        "execution_contract": plan.get("execution_contract"),
        "lineage": plan.get("lineage"),
        "orders": plan.get("orders"),
        "targets": plan.get("targets"),
        "universe": plan.get("universe"),
        "weights": plan.get("weights"),
    }


def _validated_target_packages(config):
    mode = config.get("decision_mode")
    if mode not in (MOMENTUM_SMOKE_MODE, TARGET_PACKAGE_MODE):
        raise ValueError("unsupported decision_mode")
    if not isinstance(TARGET_PACKAGES, list):
        raise ValueError("TARGET_PACKAGES must be a list")
    expected_count = config.get("target_package_count")
    expected_sha256 = config.get("target_package_tape_sha256")
    if mode == MOMENTUM_SMOKE_MODE:
        if (
            type(expected_count) is not int
            or expected_count != 0
            or expected_sha256 is not None
            or TARGET_PACKAGES
        ):
            raise ValueError(
                "portable_momentum_smoke cannot carry target packages"
            )
        return []
    if type(expected_count) is not int or expected_count <= 0:
        raise ValueError(
            "target_package mode requires a positive target_package_count"
        )
    if expected_count != len(TARGET_PACKAGES):
        raise ValueError("target package count mismatch")
    actual_sha256 = target_package_tape_sha256(TARGET_PACKAGES)
    if expected_sha256 != actual_sha256:
        raise ValueError("target package tape sha256 mismatch")
    verified = []
    package_ids = set()
    decision_ids = set()
    clocks = set()
    for raw_package in TARGET_PACKAGES:
        package = verify_target_package(raw_package)
        package_id = package["package_id"]
        decision_id = package["decision_id"]
        clock = (package["signal_as_of"], package["next_session"])
        if (
            package_id in package_ids
            or decision_id in decision_ids
            or clock in clocks
        ):
            raise ValueError("duplicate target package decision identity")
        package_ids.add(package_id)
        decision_ids.add(decision_id)
        clocks.add(clock)
        verified.append(package)
    return verified


def initialize(context):
    """Configure the selected daily-open decision mode."""
    set_option("avoid_future_data", True)
    set_option("use_real_price", True)
    g.quant60_evidence_sequence = 0
    g.quant60_order_observations = []
    g.quant60_config = dict(CONFIG)
    g.quant60_target_packages = _validated_target_packages(
        g.quant60_config
    )
    g.quant60_decision_mode = g.quant60_config["decision_mode"]
    if g.quant60_config["decision_mode"] == MOMENTUM_SMOKE_MODE:
        if g.quant60_config.get("rebalance_schedule") != "weekly_first_close":
            raise ValueError("unsupported rebalance_schedule")
        if g.quant60_config.get("universe_mode") not in ("pit_index", "fixed"):
            raise ValueError("unsupported universe_mode")
        if (
            g.quant60_config.get("universe_mode") == "pit_index"
            and not g.quant60_config.get("dedicated_account_required")
        ):
            raise ValueError("pit_index mode requires a dedicated account")
    set_benchmark(g.quant60_config["index_symbol"])
    commission = (
        float(g.quant60_config["commission_rate"])
        + float(g.quant60_config["transfer_fee_rate"])
    )
    set_order_cost(
        OrderCost(
            open_tax=0.0,
            close_tax=float(g.quant60_config["stamp_duty_rate"]),
            open_commission=commission,
            close_commission=commission,
            close_today_commission=0.0,
            min_commission=float(
                g.quant60_config["minimum_commission"]
            ),
        ),
        type="stock",
    )
    # JoinQuant applies half of PriceRelatedSlippage on each side. The local
    # slippage_bps setting is one-sided, hence the explicit factor of two.
    set_slippage(
        PriceRelatedSlippage(
            2.0 * float(g.quant60_config["slippage_bps"]) / 10000.0
        ),
        type="stock",
    )
    set_option(
        "order_volume_ratio",
        float(g.quant60_config["participation_rate"]),
    )
    g.quant60_last_plan = None
    g.quant60_last_universe = None
    g.quant60_last_universe_as_of = None
    g.quant60_skipped_orders = []
    g.quant60_pending_first_session = None
    g.quant60_last_callback_date = None
    run_daily(rebalance, time="open")
    _emit_evidence(
        "INIT",
        {
            "benchmark": g.quant60_config["index_symbol"],
            "package_count": len(g.quant60_target_packages),
            "package_ids": [
                package["package_id"]
                for package in g.quant60_target_packages
            ],
            "rebalance_callback": "run_daily_open",
            "target_package_tape_sha256": g.quant60_config.get(
                "target_package_tape_sha256"
            ),
        },
        context,
    )


def _decision_mode():
    configured = g.quant60_config.get("decision_mode")
    frozen = getattr(g, "quant60_decision_mode", configured)
    if configured != frozen:
        raise ValueError("decision_mode cannot change after initialize")
    return frozen


def _close_history(symbol, count):
    raw = attribute_history(
        symbol,
        count,
        unit="1d",
        fields=["close"],
        skip_paused=False,
        df=False,
        fq="pre",
    )
    values = list(raw.get("close", [])) if hasattr(raw, "get") else []
    if len(values) != int(count):
        raise PriceHistoryUnavailable(
            "%s needs %d completed daily closes, got %d"
            % (symbol, int(count), len(values))
        )
    output = []
    for value in values:
        try:
            number = float(value)
        except (TypeError, ValueError):
            raise PriceHistoryUnavailable(
                "%s contains a missing/non-numeric close" % symbol
            )
        if not math.isfinite(number) or number <= 0.0:
            raise PriceHistoryUnavailable(
                "%s contains a missing/non-positive close" % symbol
            )
        output.append(number)
    return output


def _close_histories(symbols, count):
    raw = history(
        int(count),
        unit="1d",
        field="close",
        security_list=list(symbols),
        df=False,
        skip_paused=False,
        fq="pre",
    )
    if not hasattr(raw, "get"):
        raise PriceHistoryUnavailable("history returned an invalid payload")
    output = {}
    for symbol in symbols:
        values = list(raw.get(symbol, []))
        if len(values) != int(count):
            raise PriceHistoryUnavailable(
                "%s needs %d completed daily closes, got %d"
                % (symbol, int(count), len(values))
            )
        clean = []
        for value in values:
            try:
                number = float(value)
            except (TypeError, ValueError):
                raise PriceHistoryUnavailable(
                    "%s contains a missing/non-numeric close" % symbol
                )
            if not math.isfinite(number) or number <= 0.0:
                raise PriceHistoryUnavailable(
                    "%s contains a missing/non-positive close" % symbol
                )
            clean.append(number)
        output[convert_symbol(symbol, "canonical")] = clean
    return output


def _decision_universe(context):
    config = g.quant60_config
    if config.get("universe_mode") == "fixed":
        values = list(config["universe"])
    else:
        as_of = _as_date(
            getattr(context, "previous_date", None),
            "previous_date",
        )
        values = get_index_stocks(
            config["index_symbol"],
            date=as_of,
        )
        if not values:
            raise PriceHistoryUnavailable(
                "no PIT index members for %s on %s"
                % (config["index_symbol"], as_of.isoformat())
            )
        g.quant60_last_universe_as_of = as_of.isoformat()
    normalized = sorted(
        set(convert_symbol(symbol, "joinquant") for symbol in values)
    )
    if not normalized:
        raise PriceHistoryUnavailable("decision universe is empty")
    g.quant60_last_universe = list(normalized)
    return normalized


def _filter_st_universe(context, symbols):
    if not g.quant60_config.get("exclude_st", True):
        return list(symbols)
    as_of = _as_date(
        getattr(context, "previous_date", None),
        "previous_date",
    )
    raw = get_extras(
        "is_st",
        list(symbols),
        end_date=as_of,
        count=1,
        df=False,
    )
    if not hasattr(raw, "get"):
        raise PriceHistoryUnavailable(
            "get_extras is_st returned an invalid payload"
        )
    output = []
    for symbol in symbols:
        values = list(raw.get(symbol, []))
        if len(values) != 1:
            raise PriceHistoryUnavailable(
                "%s needs one PIT is_st value on %s"
                % (symbol, as_of.isoformat())
            )
        try:
            numeric = float(values[0])
        except (TypeError, ValueError):
            raise PriceHistoryUnavailable(
                "%s contains a non-boolean PIT is_st value" % symbol
            )
        if not math.isfinite(numeric) or numeric not in (0.0, 1.0):
            raise PriceHistoryUnavailable(
                "%s contains an invalid PIT is_st value" % symbol
            )
        if not bool(numeric):
            output.append(symbol)
    if not output:
        raise PriceHistoryUnavailable(
            "PIT ST exclusion removed the entire decision universe"
        )
    g.quant60_last_universe = list(output)
    return output


def _position_amount(position, name, default=0):
    value = getattr(position, name, default)
    return int(value or 0)


def _snapshot_portfolio(context, managed):
    current = {}
    sellable = {}
    managed = set(convert_symbol(symbol, "canonical") for symbol in managed)
    dynamic = (
        _decision_mode() == MOMENTUM_SMOKE_MODE
        and g.quant60_config.get("universe_mode") == "pit_index"
    )
    positions = getattr(context.portfolio, "positions", {})
    for symbol, position in positions.items():
        canonical = convert_symbol(str(symbol), "canonical")
        quantity = _position_amount(position, "total_amount")
        if quantity and canonical not in managed and not dynamic:
            raise UnmanagedPositionError(
                "account contains unmanaged position %s; use a dedicated "
                "strategy account or reconcile ownership before trading" % canonical
            )
        if canonical not in managed and not dynamic:
            continue
        current[canonical] = quantity
        sellable[canonical] = _position_amount(
            position, "closeable_amount", current[canonical]
        )
    equity = float(getattr(context.portfolio, "total_value", 0.0) or 0.0)
    return current, sellable, equity


def _is_star_board(symbol):
    code = str(symbol).split(".", 1)[0]
    return code.startswith("688") or code.startswith("689")


def _order_request(symbol, delta):
    """Build a fail-closed hosted order request from current market data.

    JoinQuant rejects an unprotected market order for STAR Market securities.
    A limit at the exchange daily bound supplies equivalent worst-price
    protection while keeping the rebalance target expressed in exact shares.
    """
    try:
        item = get_current_data()[symbol]
    except Exception as exc:
        g.quant60_skipped_orders.append(
            {"symbol": symbol, "reason": "current_data_unavailable", "detail": str(exc)}
        )
        return None
    if bool(getattr(item, "paused", False)):
        g.quant60_skipped_orders.append(
            {"symbol": symbol, "reason": "paused"}
        )
        return None
    if not _is_star_board(symbol):
        return {"style": None}

    field = "high_limit" if int(delta) > 0 else "low_limit"
    raw_price = getattr(item, field, None)
    try:
        protection_price = float(raw_price)
    except (TypeError, ValueError):
        protection_price = float("nan")
    if (
        not math.isfinite(protection_price)
        or protection_price <= 0.0
        or protection_price >= 10000.0
    ):
        g.quant60_skipped_orders.append(
            {
                "symbol": symbol,
                "reason": "star_protection_price_invalid",
                "detail": field,
            }
        )
        return None
    try:
        style = LimitOrderStyle(protection_price)
    except Exception as exc:
        g.quant60_skipped_orders.append(
            {
                "symbol": symbol,
                "reason": "star_protection_style_unavailable",
                "detail": str(exc),
            }
        )
        return None
    return {"style": style}


def _target_package_for_open(context):
    current_date = _as_date(getattr(context, "current_dt", None), "current_dt")
    previous_date = _as_date(
        getattr(context, "previous_date", None),
        "previous_date",
    )
    signal_as_of = (
        previous_date.isoformat() + "T15:00:00+08:00"
    )
    next_session = current_date.isoformat()
    config = g.quant60_config
    packages = _validated_target_packages(config)
    matches = [
        package
        for package in packages
        if package["signal_as_of"] == signal_as_of
        and package["next_session"] == next_session
    ]
    if not matches:
        _emit_evidence(
            "TARGET_PACKAGE_NOOP",
            {
                "fallback_invoked": False,
                "next_session": next_session,
                "orders_submitted": 0,
                "package_count": len(packages),
                "reason": "NO_EXACT_CLOCK_MATCH",
                "signal_as_of": signal_as_of,
                "target_package_tape_sha256": config[
                    "target_package_tape_sha256"
                ],
            },
            context,
        )
        return None
    if len(matches) != 1:
        raise ValueError("ambiguous target package clock")
    package = lookup_target_package_exact(
        packages,
        signal_as_of,
        next_session,
        decision_id=matches[0]["decision_id"],
        expected_tape_sha256=config["target_package_tape_sha256"],
        expected_count=config["target_package_count"],
    )
    _emit_evidence(
        "TARGET_PACKAGE_HIT",
        {
            "lineage": _target_lineage(package),
            "target_package_tape_sha256": config[
                "target_package_tape_sha256"
            ],
        },
        context,
    )
    return package


def _target_prices(symbols):
    try:
        current_data = get_current_data()
    except Exception as exc:
        raise PriceHistoryUnavailable(
            "current_data is unavailable: %s" % exc
        )
    prices = {}
    for canonical in sorted(symbols):
        symbol = convert_symbol(canonical, "joinquant")
        try:
            raw_price = getattr(current_data[symbol], "day_open")
            price = float(raw_price)
        except (KeyError, AttributeError, TypeError, ValueError):
            raise PriceHistoryUnavailable(
                "%s has no usable current day_open" % symbol
            )
        if not math.isfinite(price) or price <= 0.0:
            raise PriceHistoryUnavailable(
                "%s has no usable current day_open" % symbol
            )
        prices[canonical] = price
    return prices


def _target_lineage(package):
    return {
        "package_id": package["package_id"],
        "package_sha256": package["package_sha256"],
        "decision_id": package["decision_id"],
        "signal_as_of": package["signal_as_of"],
        "next_session": package["next_session"],
        "release": package["release"],
        "model_id": package["model_id"],
    }


def _compute_target_package_plan(context):
    package = _target_package_for_open(context)
    if package is None:
        return None
    managed = sorted(package["universe"])
    current, sellable, equity = _snapshot_portfolio(context, managed)
    if equity <= 0:
        raise ValueError("portfolio.total_value must be positive")
    prices = _target_prices(
        set(package["target_weights"]) | set(current)
    )
    _emit_evidence(
        "PLATFORM_BINDING_INPUT",
        {
            "current": current,
            "equity": equity,
            "lineage": _target_lineage(package),
            "price_source": TARGET_PRICE_SOURCE,
            "prices": prices,
            "sellable": sellable,
        },
        context,
    )
    plan = build_target_weight_plan(
        target_weights=package["target_weights"],
        prices=prices,
        current=current,
        sellable=sellable,
        equity=equity,
        cash_buffer=g.quant60_config["cash_buffer"],
        lot_size=g.quant60_config["lot_size"],
        decision_id=package["decision_id"],
        package_sha256=package["package_sha256"],
    )
    plan["decision_mode"] = TARGET_PACKAGE_MODE
    plan["lineage"] = _target_lineage(package)
    plan["universe"] = package["universe"]
    plan["execution_contract"] = {
        "signal_price_source": TARGET_PRICE_SOURCE,
        "submit_trigger": "DECLARED_NEXT_SESSION_OPEN_CALLBACK",
        "declared_next_session": package["next_session"],
        "real_platform_observed": False,
    }
    _emit_evidence(
        "TARGET_PACKAGE_PLAN",
        _plan_evidence(plan),
        context,
    )
    return plan


def compute_plan(context):
    """Return the selected portable plan without submitting orders."""
    if _decision_mode() == TARGET_PACKAGE_MODE:
        return _compute_target_package_plan(context)
    config = g.quant60_config
    required = int(config["lookback"]) + int(config["skip"]) + 1
    platform_universe = _decision_universe(context)
    platform_universe = _filter_st_universe(
        context,
        platform_universe,
    )
    price_history = _close_histories(platform_universe, required)

    current, sellable, equity = _snapshot_portfolio(
        context,
        platform_universe,
    )
    if equity <= 0:
        raise ValueError("portfolio.total_value must be positive")
    plan = build_rebalance_plan(
        price_history=price_history,
        current=current,
        sellable=sellable,
        equity=equity,
        lookback=config["lookback"],
        skip=config["skip"],
        top_n=config["top_n"],
        max_weight=config["max_weight"],
        gross_target=config["gross_target"],
        cash_buffer=config["cash_buffer"],
        lot_size=config["lot_size"],
    )
    return plan


def _submit_plan(context, plan, managed):
    """Submit a portable plan's exact executable share deltas."""
    if plan is None:
        return None
    observe_target_package = (
        plan.get("decision_mode") == TARGET_PACKAGE_MODE
    )
    order_deltas = plan["orders"]
    current, _, unused_equity = _snapshot_portfolio(
        context,
        managed,
    )
    del unused_equity
    all_symbols = set(current)
    all_symbols.update(order_deltas)

    # Exits first.  The requested target is current + executable delta, not the
    # unconstrained portfolio target; this preserves T+1 sellable caps and odd
    # lots from the portable plan.
    for canonical in sorted(
        all_symbols,
        key=lambda item: (
            int(order_deltas.get(item, 0)) > 0,
            item,
        ),
    ):
        delta = int(order_deltas.get(canonical, 0))
        if delta == 0:
            continue
        platform_symbol = convert_symbol(canonical, "joinquant")
        skipped_before = len(g.quant60_skipped_orders)
        request = _order_request(platform_symbol, delta)
        if request is None:
            skipped = g.quant60_skipped_orders[skipped_before:]
            if not skipped:
                skipped = [
                    {
                        "symbol": platform_symbol,
                        "reason": "ORDER_REQUEST_NOT_BUILT",
                    }
                ]
            if observe_target_package:
                for item in skipped:
                    _emit_evidence(
                        "SKIPPED_ORDER",
                        {
                            "canonical_symbol": canonical,
                            "delta": delta,
                            "lineage": plan.get("lineage"),
                            "skip": item,
                        },
                        context,
                    )
            continue
        target = int(current.get(canonical, 0)) + delta
        request_evidence = {
            "canonical_symbol": canonical,
            "current_quantity": int(current.get(canonical, 0)),
            "delta": delta,
            "lineage": plan.get("lineage"),
            "platform_symbol": platform_symbol,
            "style": _observe_object(
                request["style"],
                ("kind", "limit_price"),
            ),
            "target_quantity": target,
        }
        if observe_target_package:
            _emit_evidence(
                "ORDER_REQUEST",
                request_evidence,
                context,
            )
        try:
            if request["style"] is None:
                order_result = order_target(platform_symbol, target)
            else:
                order_result = order_target(
                    platform_symbol,
                    target,
                    style=request["style"],
                )
        except Exception as exc:
            if observe_target_package:
                _emit_evidence(
                    "ORDER_RETURN",
                    {
                        "error_type": type(exc).__name__,
                        "lineage": plan.get("lineage"),
                        "outcome": "EXCEPTION",
                        "platform_symbol": platform_symbol,
                        "target_quantity": target,
                    },
                    context,
                )
            raise
        observation = {
            "lineage": plan.get("lineage"),
            "order": _observe_order(order_result),
            "outcome": "RETURNED",
            "platform_symbol": platform_symbol,
            "target_quantity": target,
        }
        if observe_target_package:
            g.quant60_order_observations.append(observation)
            _emit_evidence(
                "ORDER_RETURN",
                observation,
                context,
            )

    g.quant60_last_plan = plan
    return plan


def _execute_rebalance(context):
    """Submit the selected portable plan's exact executable share deltas."""
    plan = compute_plan(context)
    if plan is None:
        return None
    managed = (
        sorted(plan["universe"])
        if "universe" in plan
        else (
            g.quant60_last_universe
            or g.quant60_config["universe"]
        )
    )
    return _submit_plan(context, plan, managed)


def _as_date(value, name):
    if hasattr(value, "date"):
        value = value.date()
    if not hasattr(value, "isocalendar"):
        raise ClockStateError("%s must be a date/datetime" % name)
    return value


def rebalance(context):
    """Execute on the session immediately after a week's first close.

    A daily state machine is used instead of ``run_weekly(..., 2)`` so a
    one-session holiday week still executes on the next available session.
    """
    current_date = _as_date(getattr(context, "current_dt", None), "current_dt")
    previous_date = _as_date(
        getattr(context, "previous_date", None),
        "previous_date",
    )
    if g.quant60_last_callback_date == current_date:
        return None
    if _decision_mode() == TARGET_PACKAGE_MODE:
        # EOD evidence is session-scoped. Clear the prior session only after
        # the duplicate-date guard so a same-day retry cannot erase evidence.
        g.quant60_last_plan = None
        g.quant60_order_observations = []
        g.quant60_skipped_orders = []
        # Consume the daily clock token before crossing the hosted order
        # boundary so callback retries cannot duplicate an accepted prefix.
        g.quant60_last_callback_date = current_date
        return _execute_rebalance(context)
    pending = g.quant60_pending_first_session
    is_first_session = (
        previous_date.isocalendar()[:2]
        != current_date.isocalendar()[:2]
    )
    if pending is not None:
        if previous_date != pending:
            raise ClockStateError(
                "pending first-session close is not the immediately "
                "previous trading session"
            )
        # Consume the clock token before crossing any hosted order boundary.
        # A partial API failure must not make a callback retry duplicate the
        # already accepted prefix of the order list.
        g.quant60_pending_first_session = (
            current_date if is_first_session else None
        )
        g.quant60_last_callback_date = current_date
        plan = _execute_rebalance(context)
        return plan
    if is_first_session:
        g.quant60_pending_first_session = current_date
    g.quant60_last_callback_date = current_date
    return None


def after_trading_end(context):
    """Emit a best-effort end-of-day observation without changing state."""
    if _decision_mode() != TARGET_PACKAGE_MODE:
        return None
    return _emit_chunked_evidence(
        "EOD_STATUS",
        {
            "last_plan": _plan_evidence(
                getattr(g, "quant60_last_plan", None)
            ),
            "order_returns": getattr(
                g,
                "quant60_order_observations",
                [],
            ),
            "orders_api": _observe_platform_collection(
                "get_orders",
                _observe_order,
            ),
            "portfolio": _observe_portfolio(context),
            "skipped_orders": getattr(
                g,
                "quant60_skipped_orders",
                [],
            ),
            "trades_api": _observe_platform_collection(
                "get_trades",
                _observe_trade,
            ),
        },
        context,
    )