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>
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jackyu66git
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"""Step 28：小级别 × 大级别 全矩阵扫描。
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此前配对都是拍脑袋定的（5m配15m、15m配1h），而远距实验里 5m 配 1h 明显好于配 15m，
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说明「隔几级去挂分型」本身是个从没调过的参数。本步把它扫完。
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效率关键：一笔交易的入场与出场只由小级别决定，大级别只改变过滤标签。
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所以每个小级别只回测一次，各大级别的同向标记以列的形式附加上去，
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18 个组合的成本接近 4 个。
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"""
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from __future__ import annotations
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import argparse
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import os
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import sys
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import warnings
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from pathlib import Path
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import numpy as np
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import pandas as pd
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warnings.filterwarnings("ignore")
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for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
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os.environ.setdefault(v, "1")
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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sys.path.insert(0, str(HERE.parent))
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pd.set_option("display.width", 320)
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SL, TP, MAXB = 1.5, 3.0, 48
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FEE, SLIP = 0.0004, 0.0001
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LTFS = ["5m", "15m", "30m", "1h"]
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HTFS = ["15m", "30m", "1h", "2h", "4h", "1d", "1w"]
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ORDER = {t: i for i, t in enumerate(["1m", "5m", "15m", "30m", "1h", "2h", "4h", "1d", "1w"])}
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def run_symbol(sym: str) -> dict | None:
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"""一个品种跑全部级别对。小级别结构与大级别分型各算一次后交叉组合。"""
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import warnings as _w
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_w.filterwarnings("ignore")
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sys.path.insert(0, str(HERE))
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sys.path.insert(0, str(HERE.parent))
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from chanlun import TF_DF
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from lib.breakout import run_trades
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from lib.data import fetch_ohlcv
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from lib.fast_bsp3 import find_fast_bsp3
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from lib.fx_signal import extract_fx_signals, signals_to_frame
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from lib.nested_bsp import attach_htf_context, htf_fx_timeline
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from lib.nested_level import build_htf_zones
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pair = f"{sym}/USDT:USDT"
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frames = []
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try:
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# 大级别分型时间线：每个周期只算一次
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timelines = {}
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for tf in HTFS:
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df_h = fetch_ohlcv(pair, tf, 10**9)
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if df_h is None or len(df_h) < 300:
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continue
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chan_h = TF_DF(df_h, 1, tf)
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s = signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe))
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timelines[tf] = htf_fx_timeline(s, chan_h.dataframe)
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for ltf in LTFS:
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df_l = fetch_ohlcv(pair, ltf, 10**9)
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if df_l is None or len(df_l) < 3000:
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continue
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chan_l = TF_DF(df_l, 1, ltf)
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cdf = chan_l.dataframe
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zones = build_htf_zones(cdf, ltf, chan=chan_l).reset_index(drop=True)
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if zones.empty:
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continue
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sig = find_fast_bsp3(cdf, zones)
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if sig.empty or len(sig) < 20:
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continue
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# 中枢阶梯方向（只依赖小级别，与大级别无关）
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z = zones.copy()
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pg, pdn = z["zg"].shift(), z["zd"].shift()
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z["z_above"] = z["zd"] > pg
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z["z_below"] = z["zg"] < pdn
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z["zone_i"] = np.arange(len(z))
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sig = sig.merge(z[["zone_i", "z_above", "z_below"]], on="zone_i", how="left")
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# 各大级别的同向标记，逐列附加
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for tf in HTFS:
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if ORDER[tf] <= ORDER[ltf] or tf not in timelines:
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continue
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sig = attach_htf_context(sig, cdf, timelines[tf], f"x{tf}")
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entries = list(zip(sig["entry_idx"].astype(int), sig["direction"].astype(int)))
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tr = run_trades(cdf, entries, SL, TP, MAXB, fee=0.0, entry_delay=1)
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if tr.empty:
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continue
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m = sig.drop_duplicates("entry_idx").set_index("entry_idx")
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tr["symbol"], tr["ltf"] = sym, ltf
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tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()]
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for c in m.columns:
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if c.endswith("_agree") or c in ("z_above", "z_below", "direction"):
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tr[c if c != "direction" else "dir_sig"] = tr["entry_idx"].map(m[c])
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frames.append(tr)
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if not frames:
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return None
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return {"sym": sym, "trades": pd.concat(frames, ignore_index=True)}
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except Exception as e:
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return {"sym": sym, "error": repr(e)[:300]}
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def stat(r: np.ndarray) -> dict:
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if len(r) < 30:
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return {}
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w, o = r[r > 0], r[r <= 0]
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sd = r.std(ddof=1)
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return {
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"笔数": len(r), "胜率": (r > 0).mean() * 100,
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"均收益": r.mean() * 100, "中位": np.median(r) * 100,
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"PF": w.sum() / abs(o.sum()) if len(o) else np.inf,
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"偏度": pd.Series(r).skew(),
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"t值": r.mean() / (sd / np.sqrt(len(r))),
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}
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--symbols", default="BTC,ETH,SOL")
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ap.add_argument("--reuse", action="store_true")
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args = ap.parse_args()
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cache = HERE / "out" / "step28_matrix.csv"
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if args.reuse and cache.exists():
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allt = pd.read_csv(cache, parse_dates=["date"])
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print(f"[复用] {len(allt)} 笔\n")
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else:
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syms = [s.strip() for s in args.symbols.split(",") if s.strip()]
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print(f"[全矩阵] {len(syms)} 个品种 × {len(LTFS)} 小级别 × {len(HTFS)} 大级别\n",
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flush=True)
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res = []
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with ProcessPoolExecutor(max_workers=len(syms)) as ex:
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futs = {ex.submit(run_symbol, s): s for s in syms}
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for f in as_completed(futs):
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r = f.result()
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if r is None or "error" in (r or {}):
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print(f" {futs[f]} 失败 {(r or {}).get('error', '')}", flush=True)
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continue
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res.append(r)
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print(f" {r['sym']} 完成 — {len(r['trades'])} 笔", flush=True)
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if not res:
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return
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allt = pd.concat([r["trades"] for r in res], ignore_index=True)
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allt.to_csv(cache, index=False)
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allt["date"] = pd.to_datetime(allt["date"])
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allt["ret_net"] = allt["gross"] - FEE - SLIP
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allt["push"] = np.where(allt["dir_sig"] == 1, allt["z_above"], allt["z_below"])
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allt["push"] = allt["push"].fillna(False).astype(bool)
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print("=" * 118)
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print("########## 1. PF 矩阵：小级别(行) × 大级别分型(列)，仅同向 ##########")
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for metric in ("PF", "t值", "中位"):
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grid = {}
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for ltf, g in allt.groupby("ltf"):
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for tf in HTFS:
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col = f"x{tf}_agree"
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if col not in g.columns:
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continue
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s = stat(g[g[col] == 1]["ret_net"].to_numpy())
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if s:
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grid.setdefault(ltf, {})[tf] = s[metric]
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if not grid:
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continue
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df = pd.DataFrame(grid).T
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df = df.reindex(index=[t for t in LTFS if t in df.index],
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columns=[t for t in HTFS if t in df.columns])
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print(f"\n-- {metric} --")
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print(df.round(2).to_string())
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print("\n########## 2. 叠加中枢阶梯方向过滤后的 PF ##########")
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grid = {}
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for ltf, g in allt[allt.push].groupby("ltf"):
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for tf in HTFS:
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col = f"x{tf}_agree"
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if col not in g.columns:
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continue
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s = stat(g[g[col] == 1]["ret_net"].to_numpy())
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if s:
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grid.setdefault(ltf, {})[tf] = s["PF"]
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df = pd.DataFrame(grid).T
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df = df.reindex(index=[t for t in LTFS if t in df.index],
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columns=[t for t in HTFS if t in df.columns])
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print(df.round(2).to_string())
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print("\n########## 3. 全部组合排行（按 t 值，含笔数下限）##########")
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rows = []
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for ltf, g in allt.groupby("ltf"):
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for tf in HTFS:
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col = f"x{tf}_agree"
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if col not in g.columns:
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continue
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for pf_name, sub in (("同向", g[g[col] == 1]),
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("同向+阶梯", g[(g[col] == 1) & g.push])):
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s = stat(sub["ret_net"].to_numpy())
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if s and s["笔数"] >= 80:
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rows.append({"组合": f"{ltf}/{tf} {pf_name}", **s})
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r = pd.DataFrame(rows).sort_values("t值", ascending=False)
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for c in ("胜率", "均收益", "中位", "PF", "偏度", "t值"):
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r[c] = r[c].round(2)
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print(r.head(25).to_string(index=False))
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print("\n########## 4. 每个小级别的最佳搭档 ##########")
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best = []
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for ltf in LTFS:
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sub = r[r["组合"].str.startswith(f"{ltf}/")]
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if not sub.empty:
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best.append(sub.iloc[0])
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if best:
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print(pd.DataFrame(best).to_string(index=False))
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if __name__ == "__main__":
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main()
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