refactor: ECR-002 拆分 runtime 包并加深 analyze 契约（已审）

将 web/services/runtime.py 拆为 runtime/ 子模块并保持门面兼容；补齐 ESS 文档、门面/契约/TF_DF 测试与 CODE_REVIEW Approve。

Co-authored-by: Cursor <cursoragent@cursor.com>

web/services/runtime/timeframes.py

@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+from collections import OrderedDict
+from .state import DEFAULT_TIMEFRAME_LABELS
+
+def _zone_cache_ttl(tf_name: str) -> int:
+	"""根据时间周期返回缓存过期时间（秒）"""
+	minutes = timeframe_to_minutes(tf_name) or 5
+	if minutes <= 5:
+		return 120      # 5m及以下: 2分钟
+	elif minutes <= 15:
+		return 300      # 15m: 5分钟
+	elif minutes <= 60:
+		return 600      # 1h: 10分钟
+	else:
+		return 1800     # 4h+: 30分钟
+
+
+def timeframe_to_minutes(tf: str):
+	"""将时间周期转换为分钟数，用于排序。"""
+	if not tf:
+		return None
+	unit = tf[-1]
+	try:
+		value = int(tf[:-1])
+	except (ValueError, TypeError):
+		return None
+	multiplier = {
+		'm': 1,
+		'h': 60,
+		'd': 1440,
+		'w': 10080,
+		'M': 43200,  # 30天近似
+	}.get(unit)
+	if multiplier is None:
+		return None
+	return value * multiplier
+
+
+def format_timeframe_label(tf: str) -> str:
+	"""将时间周期转换为可读标签。"""
+	if not tf:
+		return tf
+	unit = tf[-1]
+	try:
+		value = int(tf[:-1])
+	except (ValueError, TypeError):
+		return tf
+	if unit == 'm':
+		return f"{value}分钟"
+	if unit == 'h':
+		return f"{value}小时"
+	if unit == 'd':
+		return "日线" if value == 1 else f"{value}日线"
+	if unit == 'w':
+		return "周线" if value == 1 else f"{value}周线"
+	if unit == 'M':
+		return "月线" if value == 1 else f"{value}月线"
+	return tf
+
+
+def build_timeframe_labels(timeframes):
+	ordered = sorted(
+		timeframes,
+		key=lambda tf: timeframe_to_minutes(tf) if timeframe_to_minutes(tf) is not None else float('inf'),
+	)
+	labels = OrderedDict()
+	for tf in ordered:
+		labels[tf] = format_timeframe_label(tf)
+	return labels
+
+
+def compute_timeframe_defaults(labels_ordered):
+	"""
+	根据已排序的「周期 → 中文标签」映射，计算主 / 次 / 次次周期默认值。
+	labels_ordered: OrderedDict 或按插入顺序排列的 dict。
+	"""
+	if not labels_ordered:
+		labels_ordered = DEFAULT_TIMEFRAME_LABELS.copy()
+	timeframe_keys = list(labels_ordered.keys())
+	preferred_main = next((tf for tf in ['5m', '15m', '1h'] if tf in labels_ordered), None)
+	default_main = preferred_main or (timeframe_keys[0] if timeframe_keys else '1m')
+	if default_main not in labels_ordered and timeframe_keys:
+		default_main = timeframe_keys[0]
+
+	if timeframe_keys:
+		try:
+			idx = timeframe_keys.index(default_main)
+			default_element = timeframe_keys[idx - 1] if idx > 0 else timeframe_keys[0]
+		except ValueError:
+			default_element = timeframe_keys[0]
+	else:
+		default_element = default_main
+
+	if timeframe_keys:
+		try:
+			idx_el = timeframe_keys.index(default_element)
+			default_sub_sub = timeframe_keys[idx_el - 1] if idx_el > 0 else timeframe_keys[0]
+		except ValueError:
+			default_sub_sub = timeframe_keys[0]
+	else:
+		default_sub_sub = default_element
+
+	return default_main, default_element, default_sub_sub, timeframe_keys
+
+def is_smaller_timeframe(tf1, tf2):
+	"""判断时间周期tf1是否小于tf2"""
+	tf1_value = timeframe_to_minutes(tf1)
+	tf2_value = timeframe_to_minutes(tf2)
+	if tf1_value is None or tf2_value is None:
+		return False
+	return tf1_value < tf2_value
+
+def is_smaller_or_equal_timeframe(tf1, tf2):
+	"""判断时间周期tf1是否小于等于tf2"""
+	tf1_value = timeframe_to_minutes(tf1)
+	tf2_value = timeframe_to_minutes(tf2)
+	if tf1_value is None or tf2_value is None:
+		return False
+	return tf1_value <= tf2_value
+