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

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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>
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jackyu66git
2026-08-27 01:05:12 +08:00
co-authored by Cursor
parent 5c10e35b76
commit 7f393b93ed
360 changed files with 140008 additions and 41167 deletions
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research/step15_nested_bsp3.py
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"""Step 15：正确的区间套 —— 1h/4h 分型定位 + 15m 三类买卖点入场。
此前 step7~step14 一直把中枢放在大级别、并在大级别自己交易，级别关系搞反了。
本步按缠论正统结构重做：
大级别 1h / 4h 只出分型，负责方向与「预设转折点」（确认滞后 1 根）
小级别 15m 出中枢、出突破、出三类买卖点，负责精确入场
逐层验证：
A 全部 15m 三买三卖（基线）
B + 1h 分型方向一致
C + 4h 分型方向一致
D + 1h 与 4h 同时一致（完整区间套）
E + 大级别分型带背驰
"""
from __future__ import annotations
import argparse
import sys
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
sys.path.insert(0, str(Path(__file__).resolve().parent))
from lib.breakout import run_trades, summarize_trades
from lib.bsp_eval import forward_returns, run_pipeline, to_events
from lib.data import fetch_ohlcv
from lib.fx_signal import extract_fx_signals, signals_to_frame
from lib.nested_bsp import attach_htf_context, htf_fx_timeline
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from chanlun import TF_DF
pd.set_option("display.width", 260)
HORIZONS = (3, 5, 10, 20, 40)
def show(rows: list[dict]) -> None:
rows = [r for r in rows if r.get("笔数", 0) > 0]
if not rows:
print(" (样本不足)")
return
print(pd.DataFrame(rows).to_string(index=False))
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--symbol", default="BTC/USDT:USDT")
ap.add_argument("--ltf", default="15m", help="小级别：出中枢与三类买卖点")
ap.add_argument("--htf1", default="1h", help="大级别一：出分型")
ap.add_argument("--htf2", default="4h", help="大级别二：出分型")
ap.add_argument("--zs", default="pure", choices=["pure", "seg"])
ap.add_argument("--sl", type=float, default=1.5)
ap.add_argument("--tp", type=float, default=3.0)
ap.add_argument("--max-bars", type=int, default=48)
args = ap.parse_args()
# ---- 小级别：三类买卖点 ----
df_l = fetch_ohlcv(args.symbol, args.ltf, 10**9)
chan_l, bsp_list = run_pipeline(df_l, args.ltf, args.zs)
cdf_l = chan_l.dataframe
ev = forward_returns(to_events(bsp_list, cdf_l), cdf_l, HORIZONS)
if ev.empty:
print("小级别无买卖点")
return
ev3 = ev[ev["bsp_type"].isin(["B3", "S3"])].reset_index(drop=True)
print(f"[小级别 {args.ltf}] {len(cdf_l)} 根K线 "
f"{cdf_l['date'].iloc[0]} -> {cdf_l['date'].iloc[-1]}")
print(f" 买卖点合计 {len(ev)} 其中三类 {len(ev3)} "
f"(B3 {(ev3.bsp_type == 'B3').sum()} / S3 {(ev3.bsp_type == 'S3').sum()})")
if not ev3.empty:
print(f" 三类买卖点确认滞后：中位 {ev3['lag_bars'].median():.0f} 根 "
f"均值 {ev3['lag_bars'].mean():.1f} 根")
# ---- 大级别：分型时间线 ----
ctx = ev3
for tf, pref in ((args.htf1, "h1"), (args.htf2, "h2")):
df_h = fetch_ohlcv(args.symbol, tf, 10**9)
chan_h = TF_DF(df_h, 1, tf)
sig_h = signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe))
tl = htf_fx_timeline(sig_h, chan_h.dataframe)
print(f"[大级别 {tf}] 分型 {len(tl)} 个")
ctx = attach_htf_context(ctx, cdf_l, tl, pref)
a1 = ctx["h1_agree"] == 1
a2 = ctx["h2_agree"] == 1
print(f"\n[方向一致率] 1h {a1.mean() * 100:.0f}% 4h {a2.mean() * 100:.0f}% "
f"两者同时 {(a1 & a2).mean() * 100:.0f}%")
entries = lambda g: list(zip(g["entry_idx"].astype(int), g["direction"].astype(int)))
print("\n########## 逐层过滤（事件驱动，sl1.5ATR tp3ATR max48）##########")
rows = [
summarize_trades(run_trades(cdf_l, entries(ctx), args.sl, args.tp, args.max_bars),
"A 全部三类"),
summarize_trades(run_trades(cdf_l, entries(ctx[a1]), args.sl, args.tp, args.max_bars),
f"B +{args.htf1}分型同向"),
summarize_trades(run_trades(cdf_l, entries(ctx[a2]), args.sl, args.tp, args.max_bars),
f"C +{args.htf2}分型同向"),
summarize_trades(run_trades(cdf_l, entries(ctx[a1 & a2]), args.sl, args.tp, args.max_bars),
"D 双大级别同向"),
]
div1 = ctx["h1_div"] == 1
rows.append(summarize_trades(
run_trades(cdf_l, entries(ctx[a1 & div1]), args.sl, args.tp, args.max_bars),
f"E +{args.htf1}同向且背驰"))
# 反向对照：大级别分型与信号相悖时应当更差
rows.append(summarize_trades(
run_trades(cdf_l, entries(ctx[~a1]), args.sl, args.tp, args.max_bars),
f"F {args.htf1}分型反向(对照)"))
show(rows)
print("\n########## 大级别分型的新鲜度（距分型确认的15m根数）##########")
rows = []
for lo, hi, name in [(0, 8, "0-8根"), (8, 24, "8-24根"),
(24, 96, "24-96根"), (96, 10**9, ">96根")]:
g = ctx[a1 & (ctx["h1_age_bars"] >= lo) & (ctx["h1_age_bars"] < hi)]
if len(g) >= 15:
rows.append(summarize_trades(
run_trades(cdf_l, entries(g), args.sl, args.tp, args.max_bars), name))
show(rows)
print("\n########## 多空拆分（双大级别同向）##########")
rows = []
both = ctx[a1 & a2]
for d, nm in ((1, "做多 B3"), (-1, "做空 S3")):
g = both[both["direction"] == d]
if len(g) >= 10:
rows.append(summarize_trades(
run_trades(cdf_l, entries(g), args.sl, args.tp, args.max_bars), nm))
show(rows)
print("\n########## 固定持有期收益（不设止损，看原始 alpha）##########")
rows = []
for name, g in (("A 全部三类", ctx), (f"B +{args.htf1}同向", ctx[a1]),
("D 双大级别同向", ctx[a1 & a2])):
if len(g) < 15:
continue
r = {"分组": name, "n": len(g)}
for h in HORIZONS:
v = g[f"ret_{h}"].dropna().to_numpy()
if len(v) < 10:
continue
sd = v.std(ddof=1)
r[f"{h}根"] = f"{v.mean() * 100:+.2f}%"
r[f"{h}根t"] = f"{v.mean() / (sd / np.sqrt(len(v))):+.1f}" if sd else "—"
rows.append(r)
if rows:
print(pd.DataFrame(rows).to_string(index=False))
out = Path(__file__).parent / "out" / f"step15_nested_{args.symbol.split('/')[0]}.csv"
out.parent.mkdir(exist_ok=True)
ctx.to_csv(out, index=False)
print(f"\n明细已写入 {out}")
if __name__ == "__main__":
main()