# coding: gbk
"""QMT built-in Python 3.6 strategy wrapper.  Keep this source ASCII-only."""

# __PORTABLE_CORE_BUNDLE_START__
# Inlined by tools/bundle_platforms.py; do not edit this region.
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",
}


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 _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 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_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",
    "capped_target_weights",
    "convert_symbol",
    "momentum_score",
    "normalize_symbol",
    "order_deltas",
    "rank_momentum",
    "round_board_lot",
    "target_quantities",
]
# __PORTABLE_CORE_BUNDLE_END__

import datetime
import math


class PriceHistoryUnavailable(RuntimeError):
    pass


class UnmanagedPositionError(RuntimeError):
    pass


class UnsupportedStrategyPeriod(RuntimeError):
    pass


class G(object):
    pass


# QMT explicitly documents that custom attributes written to ContextInfo can
# roll back before the next handlebar call.  Keep all user-owned mutable state
# in a module global and use ContextInfo only for platform-owned fields/APIs.
g = G()


# __BASELINE_CONFIG_START__
# Generated from configs/baseline.json; do not edit this region.
CONFIG = {'account_id': 'test',
 'universe_mode': 'pit_index',
 'index_symbol': '000905.XSHG',
 'qmt_sector_name': '\u4e2d\u8bc1500',
 'dedicated_account_required': True,
 'exclude_st': True,
 'universe': ['600000.SH', '000001.SZ', '300750.SZ', '000333.SZ'],
 '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__


def _param(ContextInfo, name, default=None):
    params = getattr(ContextInfo, "_param", {})
    if isinstance(params, dict) and name in params:
        return params[name]
    return default


def init(ContextInfo):
    period = str(
        getattr(ContextInfo, "period", _param(ContextInfo, "period", "1d"))
    ).strip().lower()
    if period not in ("1d", "day"):
        raise UnsupportedStrategyPeriod(
            "quant60 QMT wrapper requires a 1d driving period"
        )
    g.config = dict(CONFIG)
    for key in CONFIG:
        override = _param(ContextInfo, "q60_" + key, None)
        if override is not None:
            g.config[key] = override
    if (
        g.config.get("rebalance_schedule")
        != "weekly_first_close"
    ):
        raise UnsupportedStrategyPeriod(
            "unsupported rebalance_schedule"
        )
    if g.config.get("universe_mode") not in ("pit_index", "fixed"):
        raise UnsupportedStrategyPeriod("unsupported universe_mode")
    if (
        g.config.get("universe_mode") == "pit_index"
        and not g.config.get("dedicated_account_required")
    ):
        raise UnsupportedStrategyPeriod(
            "pit_index mode requires a dedicated account"
        )
    set_commission = getattr(ContextInfo, "set_commission", None)
    set_slippage = getattr(ContextInfo, "set_slippage", None)
    if not callable(set_commission) or not callable(set_slippage):
        raise UnsupportedStrategyPeriod(
            "QMT backtest commission/slippage APIs are unavailable"
        )
    commission = (
        float(g.config["commission_rate"])
        + float(g.config["transfer_fee_rate"])
    )
    set_commission(
        0,
        [
            0.0,
            float(g.config["stamp_duty_rate"]),
            commission,
            commission,
            0.0,
            float(g.config["minimum_commission"]),
        ],
    )
    set_slippage(
        2,
        float(g.config["slippage_bps"]) / 10000.0,
    )
    g.last_week = None
    g.last_plan = None
    g.last_universe = None
    g.last_universe_as_of = None
    if (
        g.config.get("universe_mode") == "fixed"
        and hasattr(ContextInfo, "set_universe")
    ):
        ContextInfo.set_universe(g.config["universe"])


def _bar_datetime(ContextInfo):
    timetag = ContextInfo.get_bar_timetag(ContextInfo.barpos)
    return datetime.datetime.fromtimestamp(float(timetag) / 1000.0)


def _series_close(frame):
    if frame is None:
        return []
    if hasattr(frame, "__getitem__"):
        try:
            values = frame["close"]
            if hasattr(values, "tolist"):
                values = values.tolist()
            return list(values)
        except (KeyError, TypeError):
            pass
    if isinstance(frame, dict):
        values = frame.get("close", [])
        return list(values)
    return []


def _decision_universe(ContextInfo, membership_timetag):
    config = g.config
    if config.get("universe_mode") == "fixed":
        values = list(config["universe"])
    else:
        function = getattr(
            ContextInfo,
            "get_stock_list_in_sector",
            None,
        )
        if not callable(function):
            raise PriceHistoryUnavailable(
                "historical sector membership API is unavailable"
            )
        values = function(
            config["qmt_sector_name"],
            int(membership_timetag),
        )
        if not values:
            raise PriceHistoryUnavailable(
                "no PIT index members for %s on %s"
                % (config["index_symbol"], membership_timetag)
            )
    normalized = sorted(
        set(convert_symbol(symbol, "qmt") for symbol in values)
    )
    if not normalized:
        raise PriceHistoryUnavailable("decision universe is empty")
    g.last_universe = list(normalized)
    g.last_universe_as_of = int(membership_timetag)
    return normalized


def _history(ContextInfo, end_time, universe):
    config = g.config
    count = int(config["lookback"]) + int(config["skip"]) + 1
    raw = ContextInfo.get_market_data_ex(
        fields=["close"],
        stock_code=universe,
        period="1d",
        start_time="",
        end_time=end_time,
        count=count,
        dividend_type="front_ratio",
        fill_data=False,
        subscribe=False,
    )
    output = {}
    for symbol in universe:
        values = _series_close(raw.get(symbol))
        if len(values) != count:
            raise PriceHistoryUnavailable(
                "%s needs %d completed daily closes, got %d"
                % (symbol, 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 _filter_st_universe(ContextInfo, universe, decision_date):
    if not g.config.get("exclude_st", True):
        return list(universe)
    function = getattr(ContextInfo, "get_his_st_data", None)
    if not callable(function):
        raise PriceHistoryUnavailable(
            "historical ST API is unavailable; QMT VIP ST data is required"
        )
    output = []
    for symbol in universe:
        raw = function(symbol)
        if not isinstance(raw, dict):
            raise PriceHistoryUnavailable(
                "%s historical ST query returned an invalid payload" % symbol
            )
        excluded = False
        for status in ("ST", "*ST", "PT"):
            periods = raw.get(status, [])
            if not isinstance(periods, (list, tuple)):
                raise PriceHistoryUnavailable(
                    "%s historical %s periods are invalid" % (symbol, status)
                )
            for period in periods:
                if (
                    not isinstance(period, (list, tuple))
                    or len(period) != 2
                ):
                    raise PriceHistoryUnavailable(
                        "%s historical %s period is invalid"
                        % (symbol, status)
                    )
                start = str(period[0])
                end = str(period[1])
                if (
                    len(start) != 8
                    or len(end) != 8
                    or not start.isdigit()
                    or not end.isdigit()
                    or start > end
                ):
                    raise PriceHistoryUnavailable(
                        "%s historical %s date range is invalid"
                        % (symbol, status)
                    )
                if start <= decision_date <= end:
                    excluded = True
        if not excluded:
            output.append(symbol)
    if not output:
        raise PriceHistoryUnavailable(
            "PIT ST exclusion removed the entire decision universe"
        )
    g.last_universe = list(output)
    return output


def _get_attr(obj, names, default=0):
    for name in names:
        if hasattr(obj, name):
            value = getattr(obj, name)
            if value is not None:
                return value
    return default


def _trade_details(ContextInfo, kind):
    account_id = g.config["account_id"]
    if hasattr(ContextInfo, "get_trade_detail_data"):
        return ContextInfo.get_trade_detail_data(account_id, "STOCK", kind)
    fn = globals().get("get_trade_detail_data")
    if fn is None:
        return []
    return fn(account_id, "STOCK", kind)


def _portfolio(ContextInfo, managed):
    current = {}
    sellable = {}
    managed = set(
        convert_symbol(symbol, "canonical")
        for symbol in managed
    )
    dynamic = g.config.get("universe_mode") == "pit_index"
    for position in _trade_details(ContextInfo, "position") or []:
        code = str(
            _get_attr(position, ["stock_code", "m_strInstrumentID"], "")
        )
        market = str(_get_attr(position, ["market", "m_strExchangeID"], ""))
        if "." not in code and market:
            code = code + "." + market
        if not code:
            continue
        canonical = convert_symbol(code, "canonical")
        quantity = int(
            _get_attr(position, ["volume", "m_nVolume", "total_amount"], 0)
        )
        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] = int(
            _get_attr(
                position,
                ["can_use_volume", "m_nCanUseVolume", "closeable_amount"],
                current[canonical],
            )
        )

    equity = 0.0
    assets = _trade_details(ContextInfo, "account") or []
    if assets:
        equity = float(
            _get_attr(
                assets[0],
                ["total_asset", "m_dBalance", "m_dTotalAsset", "asset"],
                0.0,
            )
        )
    if equity <= 0:
        equity = float(_param(ContextInfo, "asset", 0.0) or 0.0)
    return current, sellable, equity


def compute_plan(ContextInfo, bar_time=None):
    if bar_time is None:
        bar_time = _bar_datetime(ContextInfo)
    membership_timetag = int(
        ContextInfo.get_bar_timetag(ContextInfo.barpos)
    )
    end_time = bar_time.strftime("%Y%m%d")
    universe = _decision_universe(ContextInfo, membership_timetag)
    universe = _filter_st_universe(
        ContextInfo,
        universe,
        end_time,
    )
    history = _history(ContextInfo, end_time, universe)
    current, sellable, equity = _portfolio(ContextInfo, universe)
    if equity <= 0:
        raise ValueError("QMT account equity must be positive")
    config = g.config
    return build_rebalance_plan(
        price_history=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"],
    )


def _orders_allowed(ContextInfo):
    # This hosted wrapper is deliberately backtest-only.  Its cached account
    # query and in-memory weekly marker cannot provide crash-safe live
    # idempotency or a complete fresh reconciliation barrier.  Real/shadow
    # broker integration belongs behind the external XtTrader adapter.
    return _is_backtest(ContextInfo)


def _is_backtest(ContextInfo):
    if hasattr(ContextInfo, "do_back_test"):
        value = getattr(ContextInfo, "do_back_test")
        if callable(value):
            value = value()
        if value is True:
            return True
    mode = str(
        getattr(ContextInfo, "trade_mode", _param(ContextInfo, "trade_mode", ""))
    ).strip().lower()
    return mode == "backtest"


def _submit_delta(ContextInfo, canonical, delta, week_key):
    config = g.config
    order_code = convert_symbol(canonical, "qmt")
    operation = 23 if delta > 0 else 24
    volume = abs(int(delta))
    user_order_id = "q60-%04d%02d-%s" % (
        int(week_key[0]),
        int(week_key[1]),
        order_code.split(".")[0],
    )
    function = globals()["passorder"]
    arg_count = getattr(getattr(function, "__code__", None), "co_argcount", 11)
    if arg_count <= 8:
        # qmttools native-Python compatibility signature.
        function(
            operation,
            1101,
            config["account_id"],
            order_code,
            5,
            -1,
            volume,
            ContextInfo,
        )
    else:
        # QMT built-in hosted signature.
        function(
            operation,
            1101,
            config["account_id"],
            order_code,
            5,
            -1,
            volume,
            "quant60",
            0,
            user_order_id,
            ContextInfo,
        )


def handlebar(ContextInfo):
    if not _is_backtest(ContextInfo) and hasattr(ContextInfo, "is_last_bar"):
        if not ContextInfo.is_last_bar():
            return None

    bar_time = _bar_datetime(ContextInfo)
    year, week, unused = bar_time.isocalendar()
    del unused
    week_key = (year, week)
    if g.last_week is None:
        # The strategy may be attached to a run in the middle of a week.
        # Treat the first observed week as an initialization window; only a
        # later ISO-week transition proves that this is the first observed
        # trading session of a complete strategy week.
        g.last_week = week_key
        return None
    if g.last_week == week_key:
        return None

    plan = compute_plan(ContextInfo, bar_time)
    g.last_week = week_key
    g.last_plan = plan
    if not _orders_allowed(ContextInfo):
        return plan

    # Sells before buys.  passorder is the QMT-hosted order boundary.
    ordered = sorted(plan["orders"].items(), key=lambda item: item[1])
    for canonical, delta in ordered:
        if int(delta) != 0:
            _submit_delta(ContextInfo, canonical, delta, week_key)
    return plan