"""Small strategy core that also runs on Python 3.6 platform runtimes.

Keep this module deliberately boring: no dataclasses, no third-party imports,
no modern annotation syntax.  JoinQuant/QMT/Qlib adapters can vendor this
single file when importing the complete ``quant60`` package is not possible.
"""

from __future__ import division

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",
]