refactor: 缠论引擎包化与 Web 分层（ECR-001）
将根目录引擎迁入 chanlun/ 并保留兼容 shim；拆分 TF_DF 与 web 服务； 前端模块化；strategies 改用 chanlun 导入；补充 ESS 文档与 golden 回归。 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
jackyu66git
+2
-566
@@ -1,566 +1,2 @@
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"""
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结构价值区 (Structure Zone) 系统
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将多时间周期的 Chan 中枢边界 (ZD/ZG/GG/DD) 和 EMA52 统一表示为带强度评分的价值区对象。
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"""
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from dataclasses import dataclass, field
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from typing import List, Dict, Optional, Any
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from datetime import datetime
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# ============================================================
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# Dataclasses
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# ============================================================
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@dataclass
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class RawZonePoint:
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"""内部中间结构：从 Chan 中枢提取的单个价格点"""
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price: float
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timeframe: str # '5m', '1h', '4h' 等
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structure_type: str # 'bi_zhongshu' | 'xd_zhongshu' | 'ema52'
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boundary_type: str # 'ZD' | 'ZG' | 'GG' | 'DD' | 'EMA52'
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source_zs_id: int # 来源 ZS 在列表中的 index（调试用）
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is_sure: bool # 来源 ZS 是否已完成
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candle_time: Optional[str] = None # 来源 ZS 的 end_time（用于 recency 计算）
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@dataclass
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class StructureZone:
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"""统一的价值区对象"""
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id: int
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lower: float
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upper: float
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center: float # (lower + upper) / 2
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width_pct: float # (upper - lower) / center * 100
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zone_type: str # 'support' | 'resistance' | 'neutral'
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timeframes: List[str] # 参与形成此区间的时间周期
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structure_types: List[str] # 参与形成的结构类型
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boundary_types: List[str] # 参与形成的边界类型
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overlap_count: int # 聚类中的原始点数
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touch_count: int # MVP: 等于 overlap_count
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recency_score: float # 0.0 - 1.0, 1.0 = 最近
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ema52_distance_pct: float # 到最近 EMA52 的距离百分比
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ema52_aligned: bool # 是否有 EMA52 落在区间内
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strength_score: float # 0-100 综合评分
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confidence: float # 0.0 - 1.0
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first_seen: Optional[str] # 最早的 candle_time
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last_seen: Optional[str] # 最晚的 candle_time
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metadata: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class StructureZoneConfig:
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"""StructureZone 提取与评分配置"""
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cluster_radius_pct: float = 0.5 # 价格聚类半径（百分比）
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min_overlap_for_zone: int = 2 # 最少重叠点数才能形成区间
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max_zones: int = 20 # 返回的最大区间数
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recency_halflife_bars: int = 50 # recency 衰减半衰期（K线数）
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zone_timeframes: List[str] = field(default_factory=lambda: ['4h', '1h', '30m', '15m', '5m'])
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kl_lines_per_tf: int = 500 # 每个时间周期使用最近多少根K线
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structure_weights: Dict[str, float] = field(default_factory=lambda: {
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'bi_zhongshu': 1.0, # 笔中枢 — 最直接的价格行为
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'xd_zhongshu': 0.8, # 线段中枢 — 较高级别但粒度较粗
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'ema52': 0.4, # EMA — 趋势参考，弱于结构
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})
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# ============================================================
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# Extraction
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# ============================================================
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def extract_raw_points_from_tf_df(
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tf_df_dict: Dict[str, Any],
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ema_symbols: List[str],
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config: StructureZoneConfig,
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) -> List[RawZonePoint]:
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"""
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从 ChanLun.tf_df_dict 中提取所有原始价格点。
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仅处理 config.zone_timeframes 中存在的时间周期。
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"""
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points: List[RawZonePoint] = []
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for tf_name in config.zone_timeframes:
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if tf_name not in tf_df_dict:
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continue
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tf_df = tf_df_dict[tf_name]
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# 1. 笔中枢 (ChanBIZS)
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try:
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if hasattr(tf_df, 'seg_list') and tf_df.seg_list:
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bi_zs_result = tf_df.cal_bi_zs(tf_df.seg_list)
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if bi_zs_result:
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_extract_from_zs_objects(
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points, tf_name, 'bi_zhongshu', bi_zs_result, config.kl_lines_per_tf
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)
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except Exception:
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pass
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# 2. 线段中枢 (ChanZS)
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try:
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zs_list = getattr(tf_df, 'zs_list', None)
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if zs_list:
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_extract_from_zs_objects(
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points, tf_name, 'xd_zhongshu', zs_list, config.kl_lines_per_tf
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)
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except Exception:
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pass
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# 3. EMA52 值
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for tf_name in config.zone_timeframes:
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if tf_name in tf_df_dict:
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try:
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ema_val = tf_df_dict[tf_name].get_ema52()
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if ema_val is not None and ema_val > 0:
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points.append(RawZonePoint(
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price=float(ema_val),
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timeframe=tf_name,
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structure_type='ema52',
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boundary_type='EMA52',
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source_zs_id=-1,
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is_sure=True,
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candle_time=None,
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))
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except Exception:
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pass
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return points
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def _extract_from_zs_objects(
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points: List[RawZonePoint],
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tf_name: str,
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structure_type: str,
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zs_list,
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kl_limit: int,
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):
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"""从 ZS 链表中提取 ZD/ZG/GG/DD 点"""
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count = 0
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node = zs_list
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while hasattr(node, 'next'):
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node = node.next
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# 从链表头开始遍历
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head = zs_list
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# 收集所有节点
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all_nodes = []
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cur = head
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while cur is not None and hasattr(cur, 'next'):
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all_nodes.append(cur)
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cur = cur.next
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# 只取最近 kl_limit 根K线内的 ZS
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all_nodes = all_nodes[-kl_limit:] if len(all_nodes) > kl_limit else all_nodes
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for idx, zs in enumerate(all_nodes):
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if not getattr(zs, 'is_sure', False):
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continue
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try:
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zg = float(zs.zg)
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zd = float(zs.zd)
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gg = float(zs.gg) if getattr(zs, 'gg', 0) else zg
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dd = float(zs.dd) if getattr(zs, 'dd', 0) else zd
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end_time = str(zs.end_time) if hasattr(zs, 'end_time') and zs.end_time else None
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except (ValueError, TypeError, AttributeError):
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continue
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if zg <= 0 or zd <= 0:
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continue
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zs_id = getattr(zs, 'index', idx)
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points.append(RawZonePoint(price=zg, timeframe=tf_name, structure_type=structure_type,
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boundary_type='ZG', source_zs_id=zs_id, is_sure=True,
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candle_time=end_time))
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points.append(RawZonePoint(price=zd, timeframe=tf_name, structure_type=structure_type,
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boundary_type='ZD', source_zs_id=zs_id, is_sure=True,
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candle_time=end_time))
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points.append(RawZonePoint(price=gg, timeframe=tf_name, structure_type=structure_type,
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boundary_type='GG', source_zs_id=zs_id, is_sure=True,
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candle_time=end_time))
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points.append(RawZonePoint(price=dd, timeframe=tf_name, structure_type=structure_type,
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boundary_type='DD', source_zs_id=zs_id, is_sure=True,
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candle_time=end_time))
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def extract_raw_points_from_serialized(
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analyses: Dict[str, Dict],
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ema52_dict: Dict[str, Optional[float]],
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config: StructureZoneConfig,
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) -> List[RawZonePoint]:
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"""
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从已序列化的分析结果中提取价格点（用于 web API，避免重复计算）。
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analyses: {'5m': {'zs_list': [...], 'bi_zs_list': [...]}, '15m': {...}, ...}
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ema52_dict: {'5m': 123.45, '15m': None, ...}
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"""
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points: List[RawZonePoint] = []
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for tf_name in config.zone_timeframes:
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if tf_name not in analyses:
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continue
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analysis = analyses[tf_name]
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# 笔中枢
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bi_zs_items = analysis.get('bi_zs_list', [])
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for idx, zs in enumerate(bi_zs_items):
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if not zs.get('is_sure', False):
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continue
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try:
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zg = float(zs['zg']); zd = float(zs['zd'])
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gg = float(zs.get('gg', zg)); dd = float(zs.get('dd', zd))
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end_time = zs.get('end_time')
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except (ValueError, KeyError):
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continue
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if zg <= 0 or zd <= 0:
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continue
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points.append(RawZonePoint(price=zg, timeframe=tf_name, structure_type='bi_zhongshu',
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boundary_type='ZG', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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points.append(RawZonePoint(price=zd, timeframe=tf_name, structure_type='bi_zhongshu',
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boundary_type='ZD', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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points.append(RawZonePoint(price=gg, timeframe=tf_name, structure_type='bi_zhongshu',
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boundary_type='GG', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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points.append(RawZonePoint(price=dd, timeframe=tf_name, structure_type='bi_zhongshu',
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boundary_type='DD', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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# 线段中枢
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zs_items = analysis.get('zs_list', [])
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for idx, zs in enumerate(zs_items):
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if not zs.get('is_sure', False):
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continue
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try:
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zg = float(zs['zg']); zd = float(zs['zd'])
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gg = float(zs.get('gg', zg)); dd = float(zs.get('dd', zd))
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end_time = zs.get('end_time')
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except (ValueError, KeyError):
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continue
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if zg <= 0 or zd <= 0:
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continue
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points.append(RawZonePoint(price=zg, timeframe=tf_name, structure_type='xd_zhongshu',
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boundary_type='ZG', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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points.append(RawZonePoint(price=zd, timeframe=tf_name, structure_type='xd_zhongshu',
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boundary_type='ZD', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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points.append(RawZonePoint(price=gg, timeframe=tf_name, structure_type='xd_zhongshu',
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boundary_type='GG', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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points.append(RawZonePoint(price=dd, timeframe=tf_name, structure_type='xd_zhongshu',
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boundary_type='DD', source_zs_id=idx, is_sure=True,
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candle_time=str(end_time) if end_time else None))
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# EMA52
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for tf_name in config.zone_timeframes:
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ema_val = ema52_dict.get(tf_name)
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if ema_val is not None and ema_val > 0:
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points.append(RawZonePoint(
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price=float(ema_val),
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timeframe=tf_name,
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structure_type='ema52',
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boundary_type='EMA52',
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source_zs_id=-1,
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is_sure=True,
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candle_time=None,
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))
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return points
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# ============================================================
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# Clustering
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# ============================================================
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def cluster_raw_points(
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points: List[RawZonePoint],
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config: StructureZoneConfig,
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) -> List[List[RawZonePoint]]:
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"""
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贪心单通聚类：将价格相近的 RawZonePoint 归为一组。
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仅在 1D 价格轴上操作，O(n log n)。
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"""
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if not points:
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return []
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sorted_points = sorted(points, key=lambda p: p.price)
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clusters: List[List[RawZonePoint]] = []
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for p in sorted_points:
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placed = False
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for cluster in reversed(clusters):
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# 检查是否可以放入当前聚类（与聚类均价比较）
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avg_price = sum(pt.price for pt in cluster) / len(cluster)
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if abs(p.price - avg_price) / avg_price * 100 <= config.cluster_radius_pct:
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cluster.append(p)
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placed = True
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break
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if not placed:
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clusters.append([p])
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# 过滤点数不足的聚类
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return [c for c in clusters if len(c) >= config.min_overlap_for_zone]
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# ============================================================
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# Scoring & Building
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# ============================================================
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def build_structure_zones(
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clusters: List[List[RawZonePoint]],
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current_price: float,
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ema52_values: Dict[str, Optional[float]],
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latest_candle_time: Optional[str],
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config: StructureZoneConfig,
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) -> List[StructureZone]:
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"""
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从聚类构建 StructureZone 列表，计算所有字段和评分。
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"""
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zones: List[StructureZone] = []
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# 收集所有 EMA52 值
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ema_prices = [v for v in ema52_values.values() if v is not None and v > 0]
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for zone_id, cluster in enumerate(clusters):
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prices = [p.price for p in cluster]
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lower = min(prices)
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upper = max(prices)
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center = (lower + upper) / 2
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width_pct = (upper - lower) / center * 100 if center > 0 else 0.0
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# 区间类型
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if upper < current_price:
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zone_type = 'support' # 区间在当前价格下方 → 支撑
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elif lower > current_price:
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zone_type = 'resistance' # 区间在当前价格上方 → 阻力
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else:
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zone_type = 'neutral' # 区间跨越当前价格
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timeframes = sorted(set(p.timeframe for p in cluster))
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structure_types = sorted(set(p.structure_type for p in cluster))
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boundary_types = sorted(set(p.boundary_type for p in cluster))
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overlap_count = len(cluster)
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# Recency
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times = [p.candle_time for p in cluster if p.candle_time]
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first_seen = min(times) if times else None
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last_seen = max(times) if times else None
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recency_score = _calc_recency(last_seen, latest_candle_time, config.recency_halflife_bars)
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# EMA52 alignment
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ema52_distance_pct = 999.0
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ema52_aligned = False
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if ema_prices:
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distances = [abs(center - ep) / ep * 100 for ep in ema_prices]
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ema52_distance_pct = round(min(distances), 2)
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ema52_aligned = any(lower <= ep <= upper for ep in ema_prices)
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# Strength score
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strength_score = _calc_strength(cluster, config, recency_score, ema52_aligned, ema52_distance_pct, width_pct)
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# Confidence
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confidence = _calc_confidence(overlap_count, len(timeframes), cluster)
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zones.append(StructureZone(
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id=zone_id + 1,
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lower=round(lower, 2),
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upper=round(upper, 2),
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center=round(center, 2),
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width_pct=round(width_pct, 2),
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zone_type=zone_type,
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timeframes=timeframes,
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structure_types=structure_types,
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boundary_types=boundary_types,
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overlap_count=overlap_count,
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touch_count=overlap_count, # MVP: 等于 overlap_count
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recency_score=round(recency_score, 3),
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ema52_distance_pct=ema52_distance_pct,
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ema52_aligned=ema52_aligned,
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strength_score=round(strength_score, 1),
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confidence=round(confidence, 2),
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first_seen=first_seen,
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last_seen=last_seen,
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))
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# 按强度降序排列
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zones.sort(key=lambda z: z.strength_score, reverse=True)
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# 截断
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if config.max_zones > 0 and len(zones) > config.max_zones:
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zones = zones[:config.max_zones]
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return zones
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def _calc_recency(
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last_seen: Optional[str],
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latest_time: Optional[str],
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halflife_bars: int,
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) -> float:
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"""计算 recency 分数：越近越高"""
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if not last_seen or not latest_time:
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return 0.5
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try:
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# 尝试解析 ISO 格式时间
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from dateutil import parser
|
||||
t_last = parser.parse(last_seen)
|
||||
t_latest = parser.parse(latest_time)
|
||||
offset_seconds = (t_latest - t_last).total_seconds()
|
||||
if offset_seconds < 0:
|
||||
return 1.0
|
||||
# 假设每根K线平均 5 分钟
|
||||
bar_seconds = 300
|
||||
offset_bars = offset_seconds / bar_seconds
|
||||
# 指数衰减: 2 ^ (-offset / halflife)
|
||||
score = 2.0 ** (-offset_bars / halflife_bars)
|
||||
return float(score)
|
||||
except Exception:
|
||||
return 0.5
|
||||
|
||||
|
||||
def _calc_strength(
|
||||
cluster: List[RawZonePoint],
|
||||
config: StructureZoneConfig,
|
||||
recency_score: float,
|
||||
ema52_aligned: bool,
|
||||
ema52_distance_pct: float,
|
||||
width_pct: float,
|
||||
) -> float:
|
||||
"""计算综合强度评分 (0-100)"""
|
||||
|
||||
# 组件 1: 结构类型多样性 (0-40)
|
||||
structure_type_counts: Dict[str, int] = {}
|
||||
for p in cluster:
|
||||
structure_type_counts[p.structure_type] = structure_type_counts.get(p.structure_type, 0) + 1
|
||||
total = sum(structure_type_counts.values())
|
||||
structure_score = 0.0
|
||||
for st, count in structure_type_counts.items():
|
||||
weight = config.structure_weights.get(st, 0.5)
|
||||
structure_score += weight * count
|
||||
structure_score = min(structure_score / max(1, total), 1.0)
|
||||
c1 = structure_score * 40
|
||||
|
||||
# 组件 2: 多周期确认 (0-25)
|
||||
tf_set = set(p.timeframe for p in cluster)
|
||||
tf_diversity = len(tf_set)
|
||||
c2 = min(tf_diversity / 5, 1.0) * 25
|
||||
|
||||
# 组件 3: 区间紧密度 (0-15) — 越窄越强
|
||||
tightness = max(0.0, 1.0 - (width_pct / 3.0))
|
||||
c3 = tightness * 15
|
||||
|
||||
# 组件 4: Recency (0-10)
|
||||
c4 = recency_score * 10
|
||||
|
||||
# 组件 5: EMA52 共振 (0-10)
|
||||
if ema52_aligned:
|
||||
ema_proximity = max(0.0, 1.0 - (ema52_distance_pct / 2.0))
|
||||
c5 = ema_proximity * 10
|
||||
else:
|
||||
c5 = 0.0
|
||||
|
||||
return c1 + c2 + c3 + c4 + c5
|
||||
|
||||
|
||||
def _calc_confidence(
|
||||
overlap_count: int,
|
||||
tf_count: int,
|
||||
cluster: List[RawZonePoint],
|
||||
) -> float:
|
||||
"""计算置信度 (0-1)"""
|
||||
base = min(overlap_count / 6.0, 0.85)
|
||||
# 多周期加分
|
||||
tf_bonus = min(tf_count / 5.0, 0.1)
|
||||
# 是否所有点都来自 sure 的 ZS
|
||||
all_sure = all(p.is_sure for p in cluster)
|
||||
sure_bonus = 0.05 if all_sure else 0.0
|
||||
return min(base + tf_bonus + sure_bonus, 1.0)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Top-level pipeline
|
||||
# ============================================================
|
||||
|
||||
def analyze_structure_zones(
|
||||
tf_df_dict: Dict[str, Any],
|
||||
ema_symbols: List[str],
|
||||
current_price: Optional[float] = None,
|
||||
config: Optional[StructureZoneConfig] = None,
|
||||
) -> List[StructureZone]:
|
||||
"""
|
||||
一站式分析：提取 → 聚类 → 评分 → 返回排序后的 StructureZone 列表。
|
||||
"""
|
||||
if config is None:
|
||||
config = StructureZoneConfig()
|
||||
|
||||
# 提取
|
||||
raw_points = extract_raw_points_from_tf_df(tf_df_dict, ema_symbols, config)
|
||||
|
||||
if not raw_points:
|
||||
return []
|
||||
|
||||
# 获取当前价格
|
||||
if current_price is None:
|
||||
for tf_name in config.zone_timeframes:
|
||||
if tf_name in tf_df_dict:
|
||||
try:
|
||||
ema_val = tf_df_dict[tf_name].get_ema52()
|
||||
if ema_val and ema_val > 0:
|
||||
current_price = float(ema_val)
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
if current_price is None:
|
||||
current_price = 0.0
|
||||
|
||||
# EMA52 值
|
||||
ema52_values = {}
|
||||
for tf_name in config.zone_timeframes:
|
||||
if tf_name in tf_df_dict:
|
||||
try:
|
||||
ema52_values[tf_name] = tf_df_dict[tf_name].get_ema52()
|
||||
except Exception:
|
||||
ema52_values[tf_name] = None
|
||||
|
||||
# 最晚时间
|
||||
latest_time = None
|
||||
times = [p.candle_time for p in raw_points if p.candle_time]
|
||||
if times:
|
||||
latest_time = max(times)
|
||||
|
||||
# 聚类
|
||||
clusters = cluster_raw_points(raw_points, config)
|
||||
|
||||
# 构建 & 评分
|
||||
return build_structure_zones(clusters, current_price, ema52_values, latest_time, config)
|
||||
|
||||
|
||||
def analyze_structure_zones_from_serialized(
|
||||
analyses: Dict[str, Dict],
|
||||
ema52_dict: Dict[str, Optional[float]],
|
||||
current_price: float,
|
||||
config: Optional[StructureZoneConfig] = None,
|
||||
) -> List[StructureZone]:
|
||||
"""
|
||||
从已序列化的分析结果构建 StructureZone（用于 web API）。
|
||||
"""
|
||||
if config is None:
|
||||
config = StructureZoneConfig()
|
||||
|
||||
raw_points = extract_raw_points_from_serialized(analyses, ema52_dict, config)
|
||||
|
||||
if not raw_points:
|
||||
return []
|
||||
|
||||
# 最晚时间
|
||||
latest_time = None
|
||||
times = [p.candle_time for p in raw_points if p.candle_time]
|
||||
if times:
|
||||
latest_time = max(times)
|
||||
|
||||
# EMA52 值（用于 alignment 检测）
|
||||
ema_values = {tf: v for tf, v in ema52_dict.items() if v is not None and v > 0}
|
||||
|
||||
clusters = cluster_raw_points(raw_points, config)
|
||||
return build_structure_zones(clusters, current_price, ema_values, latest_time, config)
|
||||
"""兼容 shim — 请优先 from chanlun import ..."""
|
||||
from chanlun.analysis.ChanZone import * # noqa: F403
|
||||
|
||||
Reference in New Issue
Block a user