refactor: 精简仓库为 chanlun 核心与 web 分析，移除威科夫与遗留模块

删除根目录旧 Chan 模块、策略、配置、文档及 wyckoff 相关代码；更新缠论 pipeline 与笔中枢计算；补充 research 研究与 web 测试。 Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-27 01:05:12 +08:00
parent 5c10e35b76
commit 7f393b93ed
360 changed files with 140008 additions and 41167 deletions
@@ -0,0 +1,144 @@
+"""Step 6：多周期结构普查。
+
+核查三个论断：
+  1. 大周期分型/笔够用，线段滞后太大 —— 实测各级结构的确认滞后。
+  2. BTC 趋势时很难有 15 分钟以上的中枢 —— 实测各周期中枢密度。
+  3. 必须区分趋势与盘整 —— 按缠论定义（单中枢=盘整，多个同向中枢=趋势）统计占比。
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from lib.data import fetch_ohlcv
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+from chanlun import TF_DF
+from chanlun.core.ChanEnum import Chan_BI_DIR, Chan_ZS_DIR
+
+pd.set_option("display.width", 240)
+
+SYMBOL = "BTC/USDT:USDT"
+TFS = ["15m", "1h", "4h", "1d"]
+BARS_PER_DAY = {"15m": 96, "1h": 24, "4h": 6, "1d": 1}
+
+
+def lag_stats(items, idx_of, attr_end="end_time") -> tuple[float, float, int]:
+    """结构确认滞后：sure_time 与结构终点之间隔了几根K线。"""
+    lags = []
+    for it in items:
+        if not getattr(it, "is_sure", False) or getattr(it, "sure_time", None) is None:
+            continue
+        e, s = str(getattr(it, attr_end, "")), str(it.sure_time)
+        if e in idx_of and s in idx_of:
+            lags.append(idx_of[s] - idx_of[e])
+    if not lags:
+        return np.nan, np.nan, 0
+    a = np.array(lags)
+    return float(np.median(a)), float(a.mean()), len(a)
+
+
+def classify_regime(bi_zs_list) -> list[str]:
+    """缠论口径：相邻同向且不重叠的中枢构成趋势，否则为盘整。"""
+    if len(bi_zs_list) < 2:
+        return ["盘整"] * len(bi_zs_list)
+    out = ["盘整"]
+    for i in range(1, len(bi_zs_list)):
+        prev, cur = bi_zs_list[i - 1], bi_zs_list[i]
+        if cur.zd > prev.zg:
+            out.append("上涨趋势")
+        elif cur.zg < prev.zd:
+            out.append("下跌趋势")
+        else:
+            out.append("盘整")
+    return out
+
+
+def main() -> None:
+    rows_struct, rows_zs, rows_regime = [], [], []
+
+    for tf in TFS:
+        df = fetch_ohlcv(SYMBOL, tf, 10**9)
+        idx_of = {t: i for i, t in enumerate(df["date"].dt.strftime("%Y-%m-%d %H:%M:%S"))}
+        days = len(df) / BARS_PER_DAY[tf]
+
+        chan = TF_DF(df, 1, tf)
+        fx_count = sum(1 for k in chan.klc_list if getattr(k, "fx_confirmed", False))
+        bi_lag_med, bi_lag_mean, bi_n = lag_stats(chan.bi_list, idx_of)
+        seg_lag_med, seg_lag_mean, seg_n = lag_stats(chan.seg_list, idx_of)
+
+        zs_pure = chan.cal_bi_zs_list_pure(chan.bi_list)
+        zs_seg = chan.cal_bi_zs(chan.seg_list)
+
+        rows_struct.append({
+            "周期": tf, "K线数": len(df), "跨度(天)": round(days),
+            "KLC": len(chan.klc_list), "分型": fx_count,
+            "笔": len(chan.bi_list), "线段": len(chan.seg_list),
+            "笔滞后(中位)": bi_lag_med, "线段滞后(中位)": seg_lag_med,
+            "线段滞后(均值)": round(seg_lag_mean, 1) if seg_lag_mean == seg_lag_mean else np.nan,
+        })
+
+        rows_zs.append({
+            "周期": tf,
+            "pure中枢": len(zs_pure), "seg中枢": len(zs_seg),
+            "pure每百天": round(len(zs_pure) / days * 100, 1),
+            "seg每百天": round(len(zs_seg) / days * 100, 1),
+            "笔/中枢": round(len(chan.bi_list) / max(len(zs_pure), 1), 1),
+        })
+
+        regimes = classify_regime(zs_pure)
+        cnt = pd.Series(regimes).value_counts()
+        total = max(len(regimes), 1)
+        rows_regime.append({
+            "周期": tf,
+            "中枢数": len(zs_pure),
+            "盘整": cnt.get("盘整", 0),
+            "上涨趋势": cnt.get("上涨趋势", 0),
+            "下跌趋势": cnt.get("下跌趋势", 0),
+            "趋势占比": f"{(cnt.get('上涨趋势', 0) + cnt.get('下跌趋势', 0)) / total * 100:.0f}%",
+        })
+
+    print("########## 1. 各周期结构数量与确认滞后（单位：K线）##########")
+    print(pd.DataFrame(rows_struct).to_string(index=False))
+    print("\n  说明：笔滞后 = 笔终点到笔被确认之间的K线数；线段同理。")
+
+    print("\n########## 2. 中枢密度 ##########")
+    print(pd.DataFrame(rows_zs).to_string(index=False))
+
+    print("\n########## 3. 趋势 / 盘整分布（pure 中枢，缠论口径）##########")
+    print(pd.DataFrame(rows_regime).to_string(index=False))
+
+    # 中枢的时间跨度：BTC 趋势中「难有大级别中枢」的直接证据
+    print("\n########## 4. 中枢持续时间分布（pure 中枢，单位：K线）##########")
+    rows = []
+    for tf in TFS:
+        df = fetch_ohlcv(SYMBOL, tf, 10**9)
+        idx_of = {t: i for i, t in enumerate(df["date"].dt.strftime("%Y-%m-%d %H:%M:%S"))}
+        chan = TF_DF(df, 1, tf)
+        zs_list = chan.cal_bi_zs_list_pure(chan.bi_list)
+        spans = []
+        for zs in zs_list:
+            bis = getattr(zs, "bi_list", [])
+            if not bis:
+                continue
+            s, e = str(bis[0].start_time), str(bis[-1].end_time)
+            if s in idx_of and e in idx_of:
+                spans.append(idx_of[e] - idx_of[s])
+        if spans:
+            a = np.array(spans)
+            rows.append({
+                "周期": tf, "中枢数": len(a),
+                "跨度中位": int(np.median(a)), "跨度均值": round(a.mean(), 1),
+                "最短": int(a.min()), "最长": int(a.max()),
+                "覆盖K线占比": f"{a.sum() / len(df) * 100:.0f}%",
+            })
+    print(pd.DataFrame(rows).to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()