feat: 第四类买卖点（B4/S4）融入缠论引擎与 web 展示
研究侧的 fast_bsp3 一直只活在 research/lib/ 里，web 端看不到，回测与目视 两条线对不上。这次把它搬进引擎，作为独立的第四类买卖点。 之所以单独立类而不是当作 B3/S3 的低滞后版：step30/31 显示引擎原生的 B3/S3 统计上呈逆势、显著亏损（胜率 27.4%、PF 0.66、t −18.76），而同一组 过滤器把 B4 从 PF 1.59 提到 2.26 却对它无效（0.66→0.71）。两者选的是 不同的交易群体，不是同一信号的早晚两版。 - chanlun/analysis/fast_bsp.py 原样搬入 find_fast_bsp3 与 build_htf_zones， 另加 add_zone_ladder / htf_fx_timeline / attach_htf_agree - research/lib/ 两个模块改为转发，所有 step 脚本导入不变，信号逐条比对一致 - 大级别上下文用 resample 从同一份 df 构建，不额外拉数据，因此与界面上选的 周期和时间范围无关 - 前端三个复选框 + 过滤模式下拉；未过滤的原始信号用浅色，避免与主口径混淆 Co-authored-by: Cursor <cursoragent@cursor.com>
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jackyu66git
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"""第四类买卖点（B4/S4）接入 TF_DF。
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判定逻辑全在 chanlun/analysis/fast_bsp.py，这里只负责把引擎的中枢/K线喂进去，
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再把结果包成 ChanFastBSP。
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刻意不在 init_TF_DF 里默认计算：现有构造路径的开销保持不变，由调用方按需触发。
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"""
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from __future__ import annotations
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import re
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import pandas as pd
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from chanlun.analysis.fast_bsp import (
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add_zone_ladder,
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attach_htf_agree,
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attach_zone_ladder,
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ensure_timestamp,
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find_fast_bsp3,
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htf_fx_timeline,
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zones_from_zs_list,
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)
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from chanlun.core.ChanEnum import Chan_BSP_DIR
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from chanlun.core.ChanFastBSP import ChanFastBSP
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# 区间套配对：小级别出中枢与买卖点，大级别只出分型定方向。
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# 取值来自 research/HANDOFF.md §1.5，是回测里实际用过的组合。
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FAST_BSP_HTF_PAIR = {
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'1m': '5m',
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'5m': '30m',
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'15m': '1h',
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'30m': '2h',
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}
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# 未列入配对表的周期回落到这个倍数
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FAST_BSP_HTF_FALLBACK_RATIO = 4
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_TF_UNIT_MINUTES = {'m': 1, 'h': 60, 'd': 1440, 'w': 10080}
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def timeframe_minutes(tf: str) -> int | None:
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"""'30m' -> 30，'2h' -> 120。无法解析时返回 None。"""
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if not tf:
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return None
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m = re.fullmatch(r'(\d+)\s*([mhdw])', str(tf).strip().lower())
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if not m:
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return None
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return int(m.group(1)) * _TF_UNIT_MINUTES[m.group(2)]
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def resolve_htf(tf: str) -> tuple[str, int] | None:
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"""给小级别找配套的大级别，返回 (标签, 分钟数)。"""
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minutes = timeframe_minutes(tf)
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if minutes is None:
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return None
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paired = FAST_BSP_HTF_PAIR.get(str(tf).strip().lower())
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if paired:
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return paired, timeframe_minutes(paired)
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return f'{minutes * FAST_BSP_HTF_FALLBACK_RATIO}m', minutes * FAST_BSP_HTF_FALLBACK_RATIO
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class FastBspBuilderMixin:
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def build_fast_bsp_htf(self, df, timeframe=None):
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"""对同一份 df 重采样得到大级别，不额外拉数据。
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大级别只用来取分型方向，样本太少就没有过滤意义，故重采样后不足 60 根时放弃。
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"""
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tf = timeframe or getattr(self, 'timeframe', None)
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htf = resolve_htf(tf)
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ltf_minutes = timeframe_minutes(tf)
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if htf is None or not ltf_minutes:
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return None
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label, minutes = htf
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if not minutes or len(df) * ltf_minutes < minutes * 60:
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return None
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try:
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from chanlun.pipeline.timeframe import TF_DF
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return TF_DF(df, minutes, label)
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except Exception:
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return None
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def cal_fast_bsp(self, df=None, bi_zs_list=None, htf_chan=None, with_htf=True,
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timeframe=None, **kw):
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"""算第四类买卖点，返回 ChanFastBSP 列表。
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bi_zs_list 传入已算好的 pure 笔中枢可免去重复计算。
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with_htf=False 时跳过大级别构建，只留 ladder_ok 这一个过滤标志。
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kw 透传给 find_fast_bsp3（scan / pullback_win / tol / require_touch 等）。
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"""
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src = df if df is not None else getattr(self, 'dataframe', None)
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if src is None or len(src) == 0:
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self.fast_bsp_list = []
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return self.fast_bsp_list
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src = ensure_timestamp(src)
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if bi_zs_list is None:
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bi_zs_list = getattr(self, 'bi_zs_list', None)
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if not bi_zs_list:
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bi_zs_list = self.cal_bi_zs_list_pure(self.cal_bi_list(self.get_klc_list(self.cal_kl_data(src))))
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zones = zones_from_zs_list(bi_zs_list, src)
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if zones.empty:
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self.fast_bsp_list = []
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return self.fast_bsp_list
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zones = add_zone_ladder(zones)
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sig = find_fast_bsp3(src, zones, **kw)
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if sig.empty:
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self.fast_bsp_list = []
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return self.fast_bsp_list
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sig = attach_zone_ladder(sig, zones)
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if with_htf:
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if htf_chan is None:
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htf_chan = self.build_fast_bsp_htf(src, timeframe)
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sig = attach_htf_agree(sig, src, htf_fx_timeline(htf_chan) if htf_chan is not None else pd.DataFrame())
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else:
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sig['htf_dir'] = None
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sig['htf_agree'] = None
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times = src['date'].dt.strftime('%Y-%m-%d %H:%M:%S').to_numpy()
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close = src['close'].to_numpy(dtype=float)
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out = []
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for r in sig.itertuples(index=False):
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entry_idx = int(r.entry_idx)
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agree = getattr(r, 'htf_agree', None)
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htf_dir = getattr(r, 'htf_dir', None)
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out.append(ChanFastBSP(
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time=times[entry_idx],
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price=close[entry_idx],
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ddir=Chan_BSP_DIR.BUY if r.direction == 1 else Chan_BSP_DIR.SELL,
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entry_idx=entry_idx,
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bo_time=times[int(r.bo_idx)],
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pb_time=times[int(r.pb_idx)] if r.pb_idx == r.pb_idx else None,
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lag=r.lag,
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depth=r.depth,
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zg=r.zg,
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zd=r.zd,
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occ=r.occ,
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htf_dir=None if htf_dir is None or htf_dir != htf_dir else int(htf_dir),
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htf_agree=None if agree is None or agree != agree else bool(agree),
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ladder_ok=bool(r.ladder_ok),
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))
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self.fast_bsp_list = out
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return out
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@@ -169,6 +169,8 @@ class ChanLun():
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return self.tf_df.find_second_bsp(bi_list, first_bsp_list)
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def find_all_bsp(self, bi_list, bi_zs_list):
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return self.tf_df.find_all_bsp(bi_list, bi_zs_list)
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def cal_fast_bsp(self, df=None, bi_zs_list=None, htf_chan=None, with_htf=True, timeframe=None, **kw):
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return self.tf_df.cal_fast_bsp(df, bi_zs_list, htf_chan, with_htf, timeframe, **kw)
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def get_zs_list(self, bi_list, seg_list):
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return self.tf_df.get_zs_list(bi_list, seg_list)
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def cal_bi_zs(self, seg_list):
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@@ -30,13 +30,14 @@ from chanlun.core.ChanZS import ChanZS, ChanZS_Big
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from chanlun.indicators.ChanMACD import ChanMACD
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from chanlun.pipeline.builders.bi import BiBuilderMixin
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from chanlun.pipeline.builders.bsp import BspBuilderMixin
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from chanlun.pipeline.builders.fast_bsp import FastBspBuilderMixin
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from chanlun.pipeline.builders.incremental import IncrementalBuilderMixin
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from chanlun.pipeline.builders.indicators import IndicatorsBuilderMixin
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from chanlun.pipeline.builders.kline import KlineBuilderMixin
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from chanlun.pipeline.builders.seg import SegBuilderMixin
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from chanlun.pipeline.builders.zs import ZsBuilderMixin
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class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilderMixin, ZsBuilderMixin, BspBuilderMixin, IncrementalBuilderMixin):
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class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilderMixin, ZsBuilderMixin, BspBuilderMixin, FastBspBuilderMixin, IncrementalBuilderMixin):
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def __init__(self, df=None, interval=0, timeframe=None):
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if df is not None:
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self.init_TF_DF(df, interval, timeframe)
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@@ -61,6 +62,7 @@ class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilde
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self.zs_list = []
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self.bi_zs_list = []
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self.bsp_list = []
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self.fast_bsp_list = []
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self.seg_list = []
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self.klc_fx_list = []
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self.klu_list = self.cal_kl_data(self.dataframe)
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