jackyu66gitandCursor
081a57a90e
独立 wyckoff 引擎 + 按需 include_wyckoff；主站 Lightweight 绘制区间/阶段/事件/VP。 Co-authored-by: Cursor <cursoragent@cursor.com>
241 lines
6.6 KiB
Python
241 lines
6.6 KiB
Python
"""威科夫阶段与事件（启发式）。"""
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from __future__ import annotations
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from typing import Any, Dict, List, Tuple
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import numpy as np
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import pandas as pd
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def _bar_time(df: pd.DataFrame, i: int):
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row = df.iloc[i]
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if "date" in df.columns and pd.notna(row["date"]):
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return row["date"]
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if "timestamp" in df.columns:
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return row["timestamp"]
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return i
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def _avg_vol(df: pd.DataFrame, i: int, win: int = 20) -> float:
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a = max(0, i - win + 1)
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v = df["volume"].astype(float).iloc[a : i + 1]
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m = float(v.mean()) if len(v) else 0.0
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return m if m > 0 else 1.0
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def detect_bias_and_events(
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df: pd.DataFrame,
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tr: Dict[str, Any],
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) -> Tuple[str, List[Dict[str, Any]], Dict[str, Any]]:
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"""
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返回 bias、events、volume_confirm。
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"""
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hi = float(tr["high"])
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lo = float(tr["low"])
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mid = float(tr["mid"])
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tol = float(tr.get("tol") or (hi - lo) * 0.05)
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s = int(tr["abs_start_idx"])
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e = int(tr["abs_end_idx"])
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events: List[Dict[str, Any]] = []
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# 扫描区间内及之后（含 tail_reserve）
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scan_end = int(tr.get("abs_scan_end_idx", min(len(df) - 1, e + 15)))
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scan_end = min(len(df) - 1, max(scan_end, e))
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spring = None
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utad = None
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sos = None
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sod = None # sign of weakness / distribution breakdown
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lps = None
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lpsy = None
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for i in range(s + 2, scan_end + 1):
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row = df.iloc[i]
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low = float(row["low"])
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high = float(row["high"])
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close = float(row["close"])
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vol = float(row["volume"]) if "volume" in df.columns else 0.0
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avg_v = _avg_vol(df, i)
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ratio = vol / avg_v if avg_v else 0.0
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# Spring: pierce below low then close back above low
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if spring is None and low < lo - tol * 0.5 and close >= lo - tol * 0.2:
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vol_ok = ratio <= 1.35 or (i + 1 <= scan_end and float(df.iloc[min(i + 1, scan_end)]["volume"]) / avg_v < 1.2)
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spring = {
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"type": "Spring",
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"time": _bar_time(df, i),
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"price": low,
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"note": "假破下沿后收回",
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"volume_ratio": round(ratio, 3),
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"volume_ok": bool(vol_ok),
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"idx": i,
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}
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# UTAD: pierce above high then close back below
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if utad is None and high > hi + tol * 0.5 and close <= hi + tol * 0.2:
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vol_ok = ratio >= 0.8
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utad = {
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"type": "UTAD",
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"time": _bar_time(df, i),
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"price": high,
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"note": "假破上沿后跌回",
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"volume_ratio": round(ratio, 3),
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"volume_ok": bool(vol_ok),
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"idx": i,
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}
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# SOS: close above high with volume
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if sos is None and close > hi + tol * 0.15:
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vol_ok = ratio >= 1.15
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sos = {
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"type": "SOS",
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"time": _bar_time(df, i),
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"price": close,
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"note": "放量上破交易区间",
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"volume_ratio": round(ratio, 3),
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"volume_ok": bool(vol_ok),
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"idx": i,
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}
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# SOW / breakdown
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if sod is None and close < lo - tol * 0.15:
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vol_ok = ratio >= 1.15
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sod = {
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"type": "SOW",
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"time": _bar_time(df, i),
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"price": close,
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"note": "放量下破交易区间",
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"volume_ratio": round(ratio, 3),
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"volume_ok": bool(vol_ok),
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"idx": i,
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}
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# LPS after SOS: pullback that holds above mid/high-band with lighter volume
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if sos is not None:
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si = int(sos["idx"])
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for i in range(si + 1, min(len(df), si + 25)):
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row = df.iloc[i]
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low = float(row["low"])
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close = float(row["close"])
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vol = float(row["volume"]) if "volume" in df.columns else 0.0
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avg_v = _avg_vol(df, i)
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ratio = vol / avg_v if avg_v else 0.0
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if low >= mid - tol and close >= hi - tol * 2:
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vol_ok = ratio <= 1.05
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lps = {
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"type": "LPS",
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"time": _bar_time(df, i),
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"price": low,
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"note": "突破后缩量回踩不破",
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"volume_ratio": round(ratio, 3),
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"volume_ok": bool(vol_ok),
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"idx": i,
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}
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break
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if sod is not None:
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si = int(sod["idx"])
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for i in range(si + 1, min(len(df), si + 25)):
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row = df.iloc[i]
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high = float(row["high"])
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close = float(row["close"])
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vol = float(row["volume"]) if "volume" in df.columns else 0.0
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avg_v = _avg_vol(df, i)
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ratio = vol / avg_v if avg_v else 0.0
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if high <= mid + tol and close <= lo + tol * 2:
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vol_ok = ratio <= 1.05
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lpsy = {
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"type": "LPSY",
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"time": _bar_time(df, i),
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"price": high,
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"note": "下跌突破后缩量反抽不过",
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"volume_ratio": round(ratio, 3),
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"volume_ok": bool(vol_ok),
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"idx": i,
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}
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break
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for ev in (spring, sos, lps, utad, sod, lpsy):
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if ev:
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events.append({k: v for k, v in ev.items() if k != "idx"})
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# bias
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last_c = float(df["close"].iloc[-1])
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bias = "unknown"
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if sos and (not sod or int(sos.get("idx", 0)) >= int(sod.get("idx", 0))):
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bias = "accumulation"
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elif sod and (not sos or int(sod.get("idx", 0)) > int(sos.get("idx", 0))):
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bias = "distribution"
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elif spring and not utad:
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bias = "accumulation"
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elif utad and not spring:
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bias = "distribution"
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elif last_c >= mid:
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bias = "accumulation"
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else:
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bias = "distribution"
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avg_volume = float(df["volume"].astype(float).iloc[max(0, e - 20) : e + 1].mean()) if "volume" in df.columns else 0.0
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volume_confirm = {
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"avg_volume": avg_volume,
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"event_checks": {ev["type"]: {"volume_ok": ev.get("volume_ok"), "volume_ratio": ev.get("volume_ratio")} for ev in events},
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}
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return bias, events, volume_confirm
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def build_phases(
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df: pd.DataFrame,
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tr: Dict[str, Any],
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bias: str,
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events: List[Dict[str, Any]],
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) -> List[Dict[str, Any]]:
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"""按时间切分 A–E 粗阶段。"""
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s = int(tr["abs_start_idx"])
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e = int(tr["abs_end_idx"])
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hi = float(tr["high"])
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lo = float(tr["low"])
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mid = float(tr["mid"])
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tol = float(tr.get("tol") or (hi - lo) * 0.05)
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event_idx = {}
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for ev in events:
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# 找回 idx 近似：按时间匹配
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t = ev.get("time")
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for i in range(s, min(len(df), e + 20)):
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if _bar_time(df, i) == t:
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event_idx[ev["type"]] = i
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break
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# 分段点
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a_end = s + max(3, (e - s) // 5)
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c_anchor = event_idx.get("Spring") or event_idx.get("UTAD") or (s + (e - s) // 2)
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d_anchor = event_idx.get("SOS") or event_idx.get("SOW") or e
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e_start = d_anchor
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def _lab(phase: str) -> str:
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if bias == "distribution":
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m = {"A": "A停止上涨", "B": "B筑顶", "C": "C测试", "D": "D派发", "E": "E下跌"}
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else:
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m = {"A": "A停止下跌", "B": "B筑底", "C": "C测试", "D": "D拉升", "E": "E离开"}
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return m.get(phase, phase)
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cuts = [
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("A", s, a_end),
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("B", a_end, max(a_end + 1, c_anchor)),
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("C", max(a_end + 1, c_anchor), max(c_anchor + 1, d_anchor)),
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("D", max(c_anchor + 1, d_anchor), max(d_anchor + 1, min(len(df) - 1, e_start + max(3, (e - s) // 6)))),
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("E", max(d_anchor, e_start), min(len(df) - 1, max(e, e_start + 5))),
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]
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phases = []
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for phase, a, b in cuts:
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a = int(np.clip(a, 0, len(df) - 1))
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b = int(np.clip(b, a, len(df) - 1))
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phases.append(
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{
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"phase": phase,
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"label": _lab(phase),
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"start_time": _bar_time(df, a),
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"end_time": _bar_time(df, b),
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}
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)
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return phases
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