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fix(web): 自动刷新保留 K 线视窗；威科夫与图表增量更新

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自动刷新改用 tail update 与 scrollToPosition 恢复视窗，避免 setData 后跳到最右；拆分 chart_tv 模块并扩展 analyze/recent API。同步威科夫分析、pipeline 增量构建及相关策略与配置。

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
jackyu66git
2026-08-25 22:57:43 +08:00
co-authored by Cursor
parent 1e60ab3bfa
commit 8ee11317d3
104 changed files with 21452 additions and 4988 deletions
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crypto_wyckoff/features.py
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"""Feature Engine — pure function over OHLCVFrame → EngineResult(FeatureSnapshot)."""
from __future__ import annotations
from typing import Any
import numpy as np
from crypto_wyckoff.domain_models import EngineResult, OHLCVFrame
def _sma(arr: np.ndarray, n: int) -> float:
if len(arr) < n:
return float(arr[-1]) if len(arr) else 0.0
return float(np.mean(arr[-n:]))
def _atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, n: int = 14) -> float:
if len(close) < 2:
return 0.0
prev_close = close[:-1]
tr = np.maximum(high[1:] - low[1:], np.maximum(np.abs(high[1:] - prev_close), np.abs(low[1:] - prev_close)))
if len(tr) < n:
return float(np.mean(tr)) if len(tr) else 0.0
return float(np.mean(tr[-n:]))
def _adx(high: np.ndarray, low: np.ndarray, close: np.ndarray, n: int = 14) -> float:
"""Simplified ADX approximation."""
if len(close) < n + 2:
return 15.0
up = high[1:] - high[:-1]
down = low[:-1] - low[1:]
plus_dm = np.where((up > down) & (up > 0), up, 0.0)
minus_dm = np.where((down > up) & (down > 0), down, 0.0)
tr = np.maximum(high[1:] - low[1:], np.maximum(np.abs(high[1:] - close[:-1]), np.abs(low[1:] - close[:-1])))
atr = np.mean(tr[-n:]) or 1e-9
plus_di = 100 * np.mean(plus_dm[-n:]) / atr
minus_di = 100 * np.mean(minus_dm[-n:]) / atr
denom = plus_di + minus_di
if denom < 1e-9:
return 10.0
dx = 100 * abs(plus_di - minus_di) / denom
return float(min(60.0, dx))
def compute_feature_snapshot(frame: OHLCVFrame) -> dict[str, Any]:
"""Compute technical snapshot dict from OHLCV (no I/O)."""
if frame.empty or len(frame) < 5:
return {"ts_code": frame.ts_code, "timeframe": frame.timeframe, "bars": len(frame)}
close = np.asarray(frame.close, dtype=float)
high = np.asarray(frame.high, dtype=float)
low = np.asarray(frame.low, dtype=float)
volume = np.asarray(frame.volume, dtype=float)
open_ = np.asarray(frame.open, dtype=float)
ma20 = _sma(close, 20)
ma60 = _sma(close, 60)
ma120 = _sma(close, min(120, len(close)))
atr = _atr(high, low, close, 14)
vol_ma20 = _sma(volume, 20) or 1e-9
volume_ratio = float(volume[-1] / vol_ma20)
look = min(60, len(close))
window_h = high[-look:]
window_l = low[-look:]
range_high = float(np.max(window_h))
range_low = float(np.min(window_l))
rng = max(range_high - range_low, 1e-9)
range_pct_60 = float(rng / close[-1]) if close[-1] else 0.0
range_position = float((close[-1] - range_low) / rng)
# Spring / UTAD hints
pierce_below = max(0.0, (range_low - low[-1]) / close[-1]) if close[-1] else 0.0
# if previous bars broke below and last close back in range
prior_low = float(np.min(low[-6:-1])) if len(low) >= 6 else float(low[-2])
pierce_below = max(pierce_below, max(0.0, (range_low - prior_low) / close[-1]))
close_back_in_range = 1.0 if close[-1] >= range_low else 0.0
reclaim_speed = 0.0
if pierce_below > 0 and close[-1] >= range_low:
reclaim_speed = min(1.0, (close[-1] - low[-1]) / max(atr, 1e-9) / 2)
pierce_above = max(0.0, (high[-1] - range_high) / close[-1])
fail_back = 1.0 if pierce_above > 0 and close[-1] <= range_high else 0.0
breakout_above = 1.0 if close[-1] > range_high and volume_ratio >= 1.0 else -1.0
# pullback hold: close near ma20 from above after being higher
pullback_hold = 0.0
if len(close) >= 5 and close[-1] > ma20 and close[-3] > close[-1] and (close[-1] - ma20) / max(atr, 1e-9) < 1.5:
pullback_hold = 0.8
ma60_prev = _sma(close[:-5], 60) if len(close) > 65 else ma60
ma60_slope = (ma60 - ma60_prev) / max(abs(ma60_prev), 1e-9)
# volume trend: recent 10 vs prior 10
if len(volume) >= 20:
volume_trend = float(np.mean(volume[-10:]) / (np.mean(volume[-20:-10]) + 1e-9) - 1.0)
else:
volume_trend = 0.0
bar_range_atr = float((high[-1] - low[-1]) / max(atr, 1e-9))
bounce_from_low = float((close[-1] - float(np.min(low[-10:]))) / close[-1]) if close[-1] else 0.0
gap_up_pct = float((open_[-1] - close[-2]) / close[-2]) if len(close) >= 2 and close[-2] else 0.0
after_strength = 0.0
if len(close) >= 4 and close[-3] > close[-4]:
after_strength = 0.7
spring_score_hint = 0.0
if pierce_below >= 0.002 and close_back_in_range:
spring_score_hint = min(90.0, 50 + pierce_below * 1500 + reclaim_speed * 20)
utad_score_hint = min(90.0, 50 + pierce_above * 1500) if pierce_above >= 0.002 and fail_back else 0.0
# swing
swing_high = float(np.max(high[-20:])) if len(high) >= 5 else float(high[-1])
swing_low = float(np.min(low[-20:])) if len(low) >= 5 else float(low[-1])
return {
"ts_code": frame.ts_code,
"timeframe": frame.timeframe,
"bars": len(frame),
"close": float(close[-1]),
"open": float(open_[-1]),
"high": float(high[-1]),
"low": float(low[-1]),
"volume": float(volume[-1]),
"ma20": ma20,
"ma60": ma60,
"ma120": ma120,
"ma60_slope": float(ma60_slope),
"atr": atr,
"adx": _adx(high, low, close),
"volume_ma20": float(vol_ma20),
"volume_ratio": volume_ratio,
"volume_trend": volume_trend,
"range_high": range_high,
"range_low": range_low,
"range_pct_60": range_pct_60,
"range_position": range_position,
"pierce_below_range": pierce_below,
"pierce_above_range": pierce_above,
"close_back_in_range": close_back_in_range,
"reclaim_speed": reclaim_speed,
"fail_back_into_range": fail_back,
"breakout_above_range": breakout_above,
"pullback_hold": pullback_hold,
"bar_range_atr": bar_range_atr,
"bounce_from_low": bounce_from_low,
"gap_up_pct": gap_up_pct,
"after_strength": after_strength,
"spring_score_hint": spring_score_hint,
"utad_score_hint": utad_score_hint,
"swing_high": swing_high,
"swing_low": swing_low,
"trade_date": str(frame.trade_dates[-1]) if frame.trade_dates else None,
}
# Minimum bars before a timeframe is considered usable (no cross-TF borrow)
_MIN_BARS = {"1d": 40, "1w": 26, "1M": 18}
class FeatureEngine:
"""Pure Feature Engine — no database access."""
name = "Feature"
version = "1.0.0"
def run(self, frame: OHLCVFrame | None, timeframe: str | None = None) -> EngineResult:
tf = timeframe or (frame.timeframe if frame else "1d")
min_bars = _MIN_BARS.get(tf, 30)
if frame is None or frame.empty or len(frame) < min_bars:
bars = 0 if frame is None or frame.empty else len(frame)
return EngineResult(
name=self.name,
version=self.version,
confidence=10.0,
score=10.0,
reasons=[f"{tf} bars={bars} < min={min_bars}，标记 insufficient"],
warnings=["insufficient_features"],
metrics={"bars": bars, "min_bars": min_bars},
payload={
"ts_code": getattr(frame, "ts_code", ""),
"timeframe": tf,
"bars": bars,
"insufficient": True,
},
)
snap = compute_feature_snapshot(frame)
snap["insufficient"] = False
conf = 90.0 if snap.get("bars", 0) >= 60 else 50.0 + min(40.0, snap.get("bars", 0) * 0.5)
warnings = []
if snap.get("bars", 0) < 60:
warnings.append("bars偏少，特征可靠性中等")
return EngineResult(
name=self.name,
version=self.version,
confidence=conf,
score=conf,
reasons=[f"computed {snap.get('bars', 0)} bars {tf}"],
warnings=warnings,
metrics={"bars": snap.get("bars", 0)},
payload=snap,
)