844 lines
26 KiB
Python
844 lines
26 KiB
Python
# coding: gbk
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"""QMT built-in Python 3.6 strategy wrapper. Keep this source ASCII-only."""
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# __PORTABLE_CORE_BUNDLE_START__
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# Inlined by tools/bundle_platforms.py; do not edit this region.
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import math
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import re
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_CANONICAL_EXCHANGES = {
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"SH": "XSHG",
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"SSE": "XSHG",
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"XSHG": "XSHG",
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"SZ": "XSHE",
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"SZE": "XSHE",
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"SZSE": "XSHE",
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"XSHE": "XSHE",
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"BJ": "XBSE",
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"BSE": "XBSE",
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"XBSE": "XBSE",
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}
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_SHORT_EXCHANGES = {
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"XSHG": "SH",
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"XSHE": "SZ",
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"XBSE": "BJ",
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}
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def _sum_numeric(values):
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"""Sum numerics without trusting a hosted runtime's global ``sum`` name."""
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total = 0.0
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for value in values:
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total += value
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return total
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def _infer_exchange(code):
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if code.startswith(("4", "8")):
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return "XBSE"
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if code.startswith(("5", "6", "9")):
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return "XSHG"
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return "XSHE"
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def _split_symbol(symbol):
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if not isinstance(symbol, str):
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raise TypeError("symbol must be a string")
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value = symbol.strip().upper().replace(" ", "")
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if not value:
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raise ValueError("symbol must not be empty")
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match = re.match(r"^(\d{6})\.(XSHG|XSHE|XBSE|SH|SZ|BJ)$", value)
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if match:
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return match.group(1), _CANONICAL_EXCHANGES[match.group(2)]
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match = re.match(r"^(XSHG|XSHE|XBSE|SH|SZ|BJ)[\.:]?(\d{6})$", value)
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if match:
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return match.group(2), _CANONICAL_EXCHANGES[match.group(1)]
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match = re.match(r"^(\d{6})[\.:](SSE|SZE|SZSE|BSE)$", value)
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if match:
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return match.group(1), _CANONICAL_EXCHANGES[match.group(2)]
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if re.match(r"^\d{6}$", value):
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return value, _infer_exchange(value)
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raise ValueError("unsupported A-share symbol: %s" % symbol)
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def normalize_symbol(symbol, target="canonical"):
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"""Convert common A-share codes to canonical/JQ, QMT, or Qlib format."""
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code, exchange = _split_symbol(symbol)
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target_name = str(target).strip().lower()
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if target_name in ("canonical", "joinquant", "jq"):
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return "%s.%s" % (code, exchange)
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if target_name in ("qmt", "xtquant"):
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return "%s.%s" % (code, _SHORT_EXCHANGES[exchange])
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if target_name == "qlib":
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return "%s%s" % (_SHORT_EXCHANGES[exchange], code)
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raise ValueError("unsupported target platform: %s" % target)
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def convert_symbol(symbol, target="canonical"):
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"""Alias kept as the stable adapter-facing name."""
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return normalize_symbol(symbol, target)
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def momentum_score(prices, lookback=20, skip=0):
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"""Return trailing simple momentum, or ``None`` if history is insufficient.
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``skip=0`` uses the latest completed decision bar. Set ``skip=1`` only
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when intentionally implementing a 20-1 convention. A lookback of N needs
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N+skip+1 observations.
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"""
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lookback = int(lookback)
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skip = int(skip)
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if lookback <= 0 or skip < 0:
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raise ValueError("lookback must be positive and skip non-negative")
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values = list(prices)
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if len(values) < lookback + skip + 1:
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return None
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end_index = len(values) - 1 - skip
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start_index = end_index - lookback
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start = float(values[start_index])
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end = float(values[end_index])
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if (
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start <= 0.0
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or end <= 0.0
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or not math.isfinite(start)
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or not math.isfinite(end)
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):
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return None
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return end / start - 1.0
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def rank_momentum(price_history, lookback=20, skip=0, top_n=None):
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"""Rank ``{symbol: closes}`` by momentum with deterministic tie-breaking."""
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ranked = []
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for symbol in sorted(price_history):
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score = momentum_score(price_history[symbol], lookback, skip)
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if score is not None and math.isfinite(score):
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ranked.append((normalize_symbol(symbol), score))
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ranked.sort(key=lambda item: (-item[1], item[0]))
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if top_n is not None:
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ranked = ranked[: max(0, int(top_n))]
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return ranked
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def capped_target_weights(
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scores, max_weight=0.20, gross_target=1.0, min_score=0.0
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):
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"""Long-only score-proportional weights with iterative cap redistribution."""
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max_weight = float(max_weight)
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gross_target = float(gross_target)
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min_score = float(min_score)
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if not math.isfinite(max_weight) or not 0.0 < max_weight <= 1.0:
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raise ValueError("max_weight must be finite and lie in (0, 1]")
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if not math.isfinite(gross_target) or not 0.0 <= gross_target <= 1.0:
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raise ValueError("gross_target must be finite and lie in [0, 1]")
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if not math.isfinite(min_score):
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raise ValueError("min_score must be finite")
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if hasattr(scores, "items"):
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items = list(scores.items())
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else:
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items = list(scores)
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normalized = {}
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for symbol, raw_score in items:
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name = normalize_symbol(symbol)
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score = float(raw_score)
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if not math.isfinite(score):
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raise ValueError("score must be finite for %s" % name)
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normalized[name] = score
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weights = dict((symbol, 0.0) for symbol in normalized)
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active = dict(
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(symbol, score)
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for symbol, score in normalized.items()
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if score > min_score and score > 0.0
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)
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remaining = min(gross_target, len(active) * max_weight)
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while active and remaining > 1e-15:
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score_sum = _sum_numeric(active.values())
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if score_sum <= 0.0:
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break
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capped = []
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for symbol in sorted(active):
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proposed = remaining * active[symbol] / score_sum
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if proposed >= max_weight - 1e-15:
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weights[symbol] = max_weight
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capped.append(symbol)
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if not capped:
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for symbol in sorted(active):
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weights[symbol] = remaining * active[symbol] / score_sum
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remaining = 0.0
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break
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for symbol in capped:
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active.pop(symbol)
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remaining -= max_weight
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remaining = max(0.0, remaining)
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return weights
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def round_board_lot(quantity, lot_size=100):
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"""Round an absolute long position down to a board-lot quantity."""
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lot_size = int(lot_size)
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if lot_size <= 0:
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raise ValueError("lot_size must be positive")
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if not math.isfinite(float(quantity)):
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raise ValueError("quantity must be finite")
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quantity = max(0, int(quantity))
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return quantity // lot_size * lot_size
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def _is_star_market(symbol):
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code, exchange = _split_symbol(symbol)
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return exchange == "XSHG" and code.startswith(("688", "689"))
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def target_quantities(
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weights, prices, equity, lot_size=100, cash_buffer=0.02
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):
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"""Convert target weights to affordable board-lot long quantities."""
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equity = float(equity)
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cash_buffer = float(cash_buffer)
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if (
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not math.isfinite(equity)
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or equity < 0.0
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or not math.isfinite(cash_buffer)
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or not 0.0 <= cash_buffer < 1.0
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):
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raise ValueError("invalid equity or cash_buffer")
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positive_weights = []
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for raw_symbol, raw_weight in weights.items():
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weight = float(raw_weight)
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if not math.isfinite(weight):
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raise ValueError(
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"weight must be finite for %s" % normalize_symbol(raw_symbol)
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)
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positive_weights.append(max(0.0, weight))
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requested_gross = _sum_numeric(positive_weights)
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gross_ceiling = 1.0 - cash_buffer
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scale = (
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min(1.0, gross_ceiling / requested_gross)
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if requested_gross > 0.0
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else 1.0
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)
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result = {}
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for raw_symbol in sorted(weights):
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symbol = normalize_symbol(raw_symbol)
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weight = float(weights[raw_symbol])
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if not math.isfinite(weight):
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raise ValueError("weight must be finite for %s" % symbol)
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weight = max(0.0, weight)
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price = prices.get(raw_symbol)
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if price is None:
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price = prices.get(symbol)
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if price is None:
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raise KeyError("missing price for %s" % symbol)
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price = float(price)
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if price <= 0.0 or not math.isfinite(price):
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raise ValueError("invalid price for %s" % symbol)
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# Weights are absolute equity weights. cash_buffer is a ceiling, not
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# a second multiplicative haircut: gross=.95 and buffer=.02 stays .95.
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budget = equity * weight * scale
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quantity = round_board_lot(budget / price, lot_size)
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# STAR Market auction orders must contain at least 200 shares. Keep
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# the portable planner conservative and on the configured 100-share
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# grid even though quantities above 200 may increment by one share.
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if _is_star_market(symbol) and 0 < quantity < 200:
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quantity = 0
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result[symbol] = quantity
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return result
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def order_deltas(
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targets, current, sellable=None, lot_size=100
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):
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"""Return signed board-lot orders; negative sells respect sellable quantity."""
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canonical_targets = {}
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canonical_current = {}
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canonical_sellable = {}
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for symbol, quantity in targets.items():
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canonical_targets[normalize_symbol(symbol)] = max(0, int(quantity))
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for symbol, quantity in current.items():
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canonical_current[normalize_symbol(symbol)] = max(0, int(quantity))
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if sellable is not None:
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for symbol, quantity in sellable.items():
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canonical_sellable[normalize_symbol(symbol)] = max(0, int(quantity))
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result = {}
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symbols = sorted(set(canonical_targets) | set(canonical_current))
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for symbol in symbols:
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target = round_board_lot(canonical_targets.get(symbol, 0), lot_size)
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if _is_star_market(symbol) and 0 < target < 200:
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target = 0
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held = canonical_current.get(symbol, 0)
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delta = target - held
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if delta > 0:
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delta = round_board_lot(delta, lot_size)
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if _is_star_market(symbol) and 0 < delta < 200:
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delta = 0
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elif delta < 0:
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available = held
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if sellable is not None:
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available = min(held, canonical_sellable.get(symbol, 0))
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requested = min(-delta, available)
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# A complete liquidation may submit the entire odd-lot balance.
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# This also covers the STAR exception for a remaining balance
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# below 200 shares. Partial orders stay on the configured grid.
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full_liquidation = (
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target == 0
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and requested == held
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and available >= held
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)
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if full_liquidation:
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delta = -held
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else:
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delta = -round_board_lot(requested, lot_size)
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if _is_star_market(symbol) and 0 < -delta < 200:
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delta = 0
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if delta:
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result[symbol] = delta
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return result
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def build_rebalance_plan(
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price_history,
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current,
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sellable,
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equity,
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lookback=20,
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skip=0,
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top_n=10,
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max_weight=0.20,
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gross_target=1.0,
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cash_buffer=0.02,
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lot_size=100,
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):
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"""Build the complete portable signal -> weight -> quantity -> order plan."""
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ranked = rank_momentum(price_history, lookback, skip, top_n)
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scores = dict(ranked)
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weights = capped_target_weights(scores, max_weight, gross_target, 0.0)
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for raw_symbol in price_history:
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symbol = normalize_symbol(raw_symbol)
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if symbol not in scores:
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scores[symbol] = momentum_score(
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price_history[raw_symbol], lookback, skip
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)
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if symbol not in weights:
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weights[symbol] = 0.0
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prices = {}
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for raw_symbol, history in price_history.items():
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if not history:
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continue
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prices[normalize_symbol(raw_symbol)] = history[-1]
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targets = target_quantities(
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weights, prices, equity, lot_size, cash_buffer
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)
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orders = order_deltas(targets, current, sellable, lot_size)
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return {
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"scores": scores,
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"weights": weights,
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"targets": targets,
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"orders": orders,
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}
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__all__ = [
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"build_rebalance_plan",
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"capped_target_weights",
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"convert_symbol",
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"momentum_score",
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"normalize_symbol",
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"order_deltas",
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"rank_momentum",
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"round_board_lot",
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"target_quantities",
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]
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# __PORTABLE_CORE_BUNDLE_END__
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import datetime
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import math
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class PriceHistoryUnavailable(RuntimeError):
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pass
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class UnmanagedPositionError(RuntimeError):
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pass
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class UnsupportedStrategyPeriod(RuntimeError):
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pass
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class G(object):
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pass
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# QMT explicitly documents that custom attributes written to ContextInfo can
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# roll back before the next handlebar call. Keep all user-owned mutable state
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# in a module global and use ContextInfo only for platform-owned fields/APIs.
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g = G()
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# __BASELINE_CONFIG_START__
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# Generated from configs/baseline.json; do not edit this region.
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CONFIG = {'account_id': 'test',
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'universe_mode': 'pit_index',
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'index_symbol': '000905.XSHG',
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'qmt_sector_name': '\u4e2d\u8bc1500',
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'dedicated_account_required': True,
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'exclude_st': True,
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'universe': ['600000.SH', '000001.SZ', '300750.SZ', '000333.SZ'],
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'lookback': 20,
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'skip': 0,
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'top_n': 20,
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'max_weight': 0.05,
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'gross_target': 0.95,
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'cash_buffer': 0.02,
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'lot_size': 100,
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'participation_rate': 0.1,
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'slippage_bps': 2.0,
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'commission_rate': 0.0002,
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'minimum_commission': 5.0,
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'stamp_duty_rate': 0.0005,
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'transfer_fee_rate': 1e-05,
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'rebalance_schedule': 'weekly_first_close'}
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# __BASELINE_CONFIG_END__
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def _param(ContextInfo, name, default=None):
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params = getattr(ContextInfo, "_param", {})
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if isinstance(params, dict) and name in params:
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return params[name]
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return default
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def init(ContextInfo):
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period = str(
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getattr(ContextInfo, "period", _param(ContextInfo, "period", "1d"))
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).strip().lower()
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if period not in ("1d", "day"):
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raise UnsupportedStrategyPeriod(
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"quant60 QMT wrapper requires a 1d driving period"
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)
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g.config = dict(CONFIG)
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for key in CONFIG:
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override = _param(ContextInfo, "q60_" + key, None)
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if override is not None:
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g.config[key] = override
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if (
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g.config.get("rebalance_schedule")
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!= "weekly_first_close"
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):
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raise UnsupportedStrategyPeriod(
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"unsupported rebalance_schedule"
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)
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if g.config.get("universe_mode") not in ("pit_index", "fixed"):
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raise UnsupportedStrategyPeriod("unsupported universe_mode")
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if (
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g.config.get("universe_mode") == "pit_index"
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and not g.config.get("dedicated_account_required")
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):
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raise UnsupportedStrategyPeriod(
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"pit_index mode requires a dedicated account"
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)
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set_commission = getattr(ContextInfo, "set_commission", None)
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set_slippage = getattr(ContextInfo, "set_slippage", None)
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if not callable(set_commission) or not callable(set_slippage):
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raise UnsupportedStrategyPeriod(
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"QMT backtest commission/slippage APIs are unavailable"
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)
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commission = (
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float(g.config["commission_rate"])
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+ float(g.config["transfer_fee_rate"])
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)
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set_commission(
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0,
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[
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0.0,
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float(g.config["stamp_duty_rate"]),
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commission,
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commission,
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0.0,
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float(g.config["minimum_commission"]),
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],
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)
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set_slippage(
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2,
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float(g.config["slippage_bps"]) / 10000.0,
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)
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g.last_week = None
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g.last_plan = None
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g.last_universe = None
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g.last_universe_as_of = None
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if (
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g.config.get("universe_mode") == "fixed"
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and hasattr(ContextInfo, "set_universe")
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):
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ContextInfo.set_universe(g.config["universe"])
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def _bar_datetime(ContextInfo):
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timetag = ContextInfo.get_bar_timetag(ContextInfo.barpos)
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return datetime.datetime.fromtimestamp(float(timetag) / 1000.0)
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def _series_close(frame):
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if frame is None:
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return []
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if hasattr(frame, "__getitem__"):
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try:
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values = frame["close"]
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if hasattr(values, "tolist"):
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values = values.tolist()
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return list(values)
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except (KeyError, TypeError):
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pass
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if isinstance(frame, dict):
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values = frame.get("close", [])
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return list(values)
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return []
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def _decision_universe(ContextInfo, membership_timetag):
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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
|