jackyu66gitandCursor
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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>
206 lines
7.6 KiB
Python
206 lines
7.6 KiB
Python
"""Step 19：区间套在不同级别对上的一致性。
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15m/1h+4h 只有 57 笔核心样本，无法定论。但区间套是级别无关的结构，
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把整套上移一级（1h 小级别有 2524 天，是 15m 的两倍）既能扩样本，
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又能验证它究竟是普适结构还是只在某一个级别对上凑巧成立。
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级别对：
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5m / 15m + 1h
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15m / 1h + 4h （step16~17 已测）
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1h / 4h + 1d
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"""
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from __future__ import annotations
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import argparse
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import sys
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import warnings
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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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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from lib.breakout import run_trades, summarize_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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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from chanlun import TF_DF
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pd.set_option("display.width", 260)
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SYMBOLS = ["BTC/USDT:USDT", "ETH/USDT:USDT", "SOL/USDT:USDT"]
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DEFAULT_PAIRS = "5m:15m:1h,15m:1h:4h,1h:4h:1d"
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SL, TP, MAXB = 1.5, 3.0, 48
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def E(g):
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return list(zip(g["entry_idx"].astype(int), g["direction"].astype(int)))
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# 同一 (品种, 周期) 的 pipeline 在多个级别对之间反复出现，缓存后能省掉大部分耗时
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_engine_cache: dict = {}
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_timeline_cache: dict = {}
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def _engine(symbol: str, tf: str, min_rows: int):
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key = (symbol, tf)
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if key not in _engine_cache:
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try:
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df = fetch_ohlcv(symbol, tf, 10**9)
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except Exception:
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df = None
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if df is None or len(df) < min_rows:
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_engine_cache[key] = None
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else:
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_engine_cache[key] = TF_DF(df, 1, tf)
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return _engine_cache[key]
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def _timeline(symbol: str, tf: str):
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key = (symbol, tf)
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if key not in _timeline_cache:
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chan = _engine(symbol, tf, 300)
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if chan is None:
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_timeline_cache[key] = None
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else:
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s = signals_to_frame(extract_fx_signals(chan, chan.dataframe))
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_timeline_cache[key] = htf_fx_timeline(s, chan.dataframe)
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return _timeline_cache[key]
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def build(symbol: str, ltf: str, htf1: str, htf2: str):
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chan_l = _engine(symbol, ltf, 3000)
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if chan_l is None:
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return None, None
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cdf_l = chan_l.dataframe
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sig = find_fast_bsp3(cdf_l, build_htf_zones(cdf_l, ltf, chan=chan_l))
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if sig.empty:
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return cdf_l, None
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for tf, pref in ((htf1, "h1"), (htf2, "h2")):
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tl = _timeline(symbol, tf)
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if tl is None or tl.empty:
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continue
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sig = attach_htf_context(sig, cdf_l, tl, pref)
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return cdf_l, sig
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--core-only", action="store_true",
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help="只输出核心组合（大级别同向 + 浅回抽）")
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ap.add_argument("--pairs", default=DEFAULT_PAIRS,
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help="级别对，格式 小级别:大级别1:大级别2，逗号分隔")
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ap.add_argument("--symbols", default="BTC,ETH,SOL")
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args = ap.parse_args()
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global SYMBOLS
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SYMBOLS = [f"{s.strip()}/USDT:USDT" for s in args.symbols.split(",") if s.strip()]
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pairs = [tuple(p.split(":")) for p in args.pairs.split(",") if p]
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all_rows, pool = [], []
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for ltf, h1, h2 in pairs:
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print(f"\n{'=' * 100}")
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print(f"级别对：小级别 {ltf} 大级别 {h1} + {h2}")
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rows = []
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pair_pool = []
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for sym in SYMBOLS:
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cdf, sig = build(sym, ltf, h1, h2)
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if sig is None or sig.empty or len(sig) < 10:
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continue
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tr = run_trades(cdf, E(sig), SL, TP, MAXB)
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if tr.empty:
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continue
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s = summarize_trades(tr, f"{sym.split('/')[0]:>4} 全部")
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s["滞后"] = f"{sig['lag'].median():.0f}"
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rows.append(s)
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m = sig.set_index("entry_idx")
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tr = tr.copy()
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tr["symbol"] = sym.split("/")[0]
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tr["pair"] = f"{ltf}/{h1}"
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tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()]
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tr["h1_agree"] = tr["entry_idx"].map(m["h1_agree"]) if "h1_agree" in m else np.nan
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tr["depth"] = tr["entry_idx"].map(m["depth"])
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pair_pool.append(tr)
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if not pair_pool:
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print(" 样本不足")
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continue
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if rows and not args.core_only:
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print(pd.DataFrame(rows).to_string(index=False))
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pt = pd.concat(pair_pool, ignore_index=True)
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pool.append(pt)
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a1 = pt["h1_agree"] == 1
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dq = pt["depth"].quantile(0.66)
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core = pt[a1 & (pt["depth"] >= dq)]
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sub = [summarize_trades(pt, f"{ltf} 全部")]
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if len(pt[a1]) >= 20:
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sub.append(summarize_trades(pt[a1], f"{ltf} +{h1}同向"))
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if len(core) >= 15:
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sub.append(summarize_trades(core, f"{ltf} 核心(同向+浅回抽)"))
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if len(pt[~a1]) >= 15:
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sub.append(summarize_trades(pt[~a1], f"{ltf} 反向(对照)"))
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print(pd.DataFrame(sub).to_string(index=False))
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if len(core) >= 15:
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r = core["ret"].to_numpy()
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all_rows.append({
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"级别对": f"{ltf} / {h1}+{h2}", "核心笔数": len(r),
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"胜率": f"{(r > 0).mean() * 100:.1f}%",
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"均收益": f"{r.mean() * 100:+.3f}%",
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"中位数": f"{np.median(r) * 100:+.3f}%",
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"PF": f"{r[r > 0].sum() / abs(r[r <= 0].sum()):.2f}",
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"偏度": f"{pd.Series(r).skew():.2f}",
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"t值": f"{r.mean() / (r.std(ddof=1) / np.sqrt(len(r))):+.2f}",
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})
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print(f"\n{'=' * 100}")
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print("########## 跨级别对：核心组合汇总 ##########")
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if all_rows:
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print(pd.DataFrame(all_rows).to_string(index=False))
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if pool:
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allp = pd.concat(pool, ignore_index=True)
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a1 = allp["h1_agree"] == 1
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cores = []
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for p, g in allp.groupby("pair"):
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dq = g["depth"].quantile(0.66)
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cores.append(g[(g["h1_agree"] == 1) & (g["depth"] >= dq)])
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core_all = pd.concat(cores, ignore_index=True)
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r = core_all["ret"].to_numpy()
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print(f"\n 三个级别对合并核心样本 {len(r)} 笔：")
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print(f" 胜率 {(r > 0).mean() * 100:.1f}% 均收益 {r.mean() * 100:+.3f}% "
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f"中位数 {np.median(r) * 100:+.3f}%")
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print(f" PF {r[r > 0].sum() / abs(r[r <= 0].sum()):.2f} "
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f"偏度 {pd.Series(r).skew():.2f} "
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f"t值 {r.mean() / (r.std(ddof=1) / np.sqrt(len(r))):+.2f}")
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for k in (2, 5, 10):
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v = r[r <= np.quantile(r, 1 - k / 100)]
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print(f" 剔除最赚{k:>2}%: PF {v[v > 0].sum() / abs(v[v <= 0].sum()):.2f} "
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f"t {v.mean() / (v.std(ddof=1) / np.sqrt(len(v))):+.2f} "
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f"中位 {np.median(v) * 100:+.3f}%")
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core_all["year"] = pd.to_datetime(core_all["date"]).dt.year
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print("\n 核心样本分年：")
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rows = [{"年份": y, "笔数": len(g), "胜率": f"{(g.ret > 0).mean() * 100:.0f}%",
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"均收益": f"{g.ret.mean() * 100:+.3f}%",
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"PF": f"{g.ret[g.ret > 0].sum() / abs(g.ret[g.ret <= 0].sum()):.2f}"
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if (g.ret <= 0).any() else "inf"}
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for y, g in core_all.groupby("year") if len(g) >= 8]
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print(pd.DataFrame(rows).to_string(index=False))
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out = Path(__file__).parent / "out" / "step19_level_pairs.csv"
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out.parent.mkdir(exist_ok=True)
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allp.to_csv(out, index=False)
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print(f"\n明细已写入 {out}")
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if __name__ == "__main__":
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main()
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