fix(web): 自动刷新保留 K 线视窗；威科夫与图表增量更新
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>
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jackyu66git
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"""Scan pipeline: load local frames → engines → store (per TF combo)."""
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from __future__ import annotations
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import json
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import logging
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from datetime import date, datetime, timezone
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from crypto_wyckoff.combos import ROLE_HIGH, ROLE_LOW, ROLE_MID, get_combo, lookback_for
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from crypto_wyckoff.cycle import CycleEngine
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from crypto_wyckoff.decision import DecisionEngine
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from crypto_wyckoff.domain_models import WyckoffScanRow
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from crypto_wyckoff.event import EventEngine
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from crypto_wyckoff.features import FeatureEngine
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from crypto_wyckoff.io import load_frame
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from crypto_wyckoff.phase import PhaseEngine
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from crypto_wyckoff.plan import PlanEngine
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from crypto_wyckoff.signal import SignalEngine
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from crypto_wyckoff.store import upsert_row
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from crypto_wyckoff.symbols_cn import display_name_cn
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from crypto_wyckoff.version import WYCKOFF_ENGINE_VERSION
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logger = logging.getLogger(__name__)
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def analyze_symbol(
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low_frame,
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mid_frame,
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high_frame,
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*,
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feature_eng: FeatureEngine,
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cycle_eng: CycleEngine,
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phase_eng: PhaseEngine,
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event_eng: EventEngine,
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signal_eng: SignalEngine,
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decision_eng: DecisionEngine,
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plan_eng: PlanEngine,
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) -> dict:
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"""Run engines with D/W/M *role* aliases so existing rules match.
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Frames may be any TF combo (e.g. 1h/4h/8h); rules still see 1d/1w/1M roles.
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"""
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f_d = feature_eng.run(low_frame, ROLE_LOW)
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f_w = feature_eng.run(mid_frame, ROLE_MID)
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f_m = feature_eng.run(high_frame, ROLE_HIGH)
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c_m = cycle_eng.run(f_m, ROLE_HIGH)
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c_w = cycle_eng.run(f_w, ROLE_MID)
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p_w = phase_eng.run(c_w, f_w, ROLE_MID)
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p_d = phase_eng.run(c_w, f_d, ROLE_LOW)
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e_w = event_eng.run(c_w, p_w, f_w, ROLE_MID)
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e_d = event_eng.run(c_w, p_d, f_d, ROLE_LOW)
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s_d = signal_eng.run(e_d, p_d)
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decision = decision_eng.run(c_m, c_w, p_w, e_w, e_d, s_d)
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plan = plan_eng.run(f_d, decision)
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return {
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"f_d": f_d, "f_w": f_w, "f_m": f_m,
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"c_m": c_m, "c_w": c_w, "p_w": p_w,
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"e_w": e_w, "e_d": e_d, "s_d": s_d,
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"decision": decision, "plan": plan,
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}
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def _to_row(
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trade_date: date,
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symbol: str,
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result: dict,
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*,
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combo_id: str,
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combo_label: str,
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) -> WyckoffScanRow:
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d = result["decision"]
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p = result["plan"]
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c_m, c_w, p_w = result["c_m"], result["c_w"], result["p_w"]
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e_w, e_d, s_d = result["e_w"], result["e_d"], result["s_d"]
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f_d, f_w, f_m = result["f_d"], result["f_w"], result["f_m"]
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snapshot = {
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"combo_id": combo_id,
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"combo_label": combo_label,
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"daily": {k: f_d.payload.get(k) for k in (
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"ma20", "ma60", "ma120", "atr", "adx", "volume_ratio",
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"range_high", "range_low", "swing_high", "swing_low", "close",
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)},
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"weekly": {k: f_w.payload.get(k) for k in ("ma20", "ma60", "adx", "close")},
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"monthly": {k: f_m.payload.get(k) for k in ("ma20", "ma60", "adx", "close")},
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}
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markers = []
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for key, typ in (("entry", "entry"), ("stop", "stop"), ("target1", "target1"), ("target2", "target2")):
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if p.payload.get(key) is not None:
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markers.append({"type": typ, "price": p.payload[key]})
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return WyckoffScanRow(
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trade_date=trade_date,
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ts_code=symbol,
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name=display_name_cn(symbol),
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industry="crypto",
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engine_version=WYCKOFF_ENGINE_VERSION,
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m_cycle=c_m.payload.get("cycle", "Unknown"),
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cycle_confidence=c_m.confidence,
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trend_score=float(d.payload.get("trend_score", c_m.score)),
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w_cycle=c_w.payload.get("cycle", "Unknown"),
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w_phase=p_w.payload.get("phase", "None"),
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w_current_event=e_w.payload.get("current_event", "None"),
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w_recent_events_json=json.dumps(
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e_w.payload.get("active_events") or e_w.payload.get("recent_events") or [],
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ensure_ascii=False,
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),
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phase_confidence=p_w.confidence,
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structure_score=float(d.payload.get("structure_score", p_w.score)),
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d_current_event=e_d.payload.get("current_event", "None"),
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d_recent_events_json=json.dumps(
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e_d.payload.get("active_events") or e_d.payload.get("recent_events") or [],
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ensure_ascii=False,
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),
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event_confidence=e_d.confidence,
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entry_score=float(d.payload.get("entry_score", e_d.score)),
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entry=p.payload.get("entry"),
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stop=p.payload.get("stop"),
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target1=p.payload.get("target1"),
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target2=p.payload.get("target2"),
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rr=p.payload.get("rr"),
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alignment=float(d.payload.get("alignment", 0)),
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stars=int(d.payload.get("stars", 1)),
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decision_signal=d.payload.get("decision_signal", "Watch"),
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signal_confidence=s_d.confidence,
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overall_confidence=float(d.payload.get("overall_confidence", d.confidence)),
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overall_score=float(d.payload.get("overall_score", d.score)),
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risk=d.payload.get("risk", "Medium"),
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reasons_json=json.dumps(d.reasons + d.warnings, ensure_ascii=False),
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feature_snapshot_json=json.dumps(snapshot, ensure_ascii=False),
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markers_json=json.dumps(markers, ensure_ascii=False),
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scanned_at=datetime.now(timezone.utc),
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combo_id=combo_id,
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)
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_ENGINES = None
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def _engines():
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global _ENGINES
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if _ENGINES is None:
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_ENGINES = {
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"feature_eng": FeatureEngine(),
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"cycle_eng": CycleEngine(),
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"phase_eng": PhaseEngine(),
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"event_eng": EventEngine(),
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"signal_eng": SignalEngine(),
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"decision_eng": DecisionEngine(),
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"plan_eng": PlanEngine(),
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}
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return _ENGINES
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def analyze_and_store(
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symbol: str,
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trade_date: date | None = None,
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*,
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combo_id: str | None = None,
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) -> WyckoffScanRow | None:
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eng = _engines()
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combo = get_combo(combo_id)
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low_tf, mid_tf, high_tf = combo["low"], combo["mid"], combo["high"]
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low = load_frame(symbol, low_tf, lookback_for(low_tf))
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mid = load_frame(symbol, mid_tf, lookback_for(mid_tf))
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high = load_frame(symbol, high_tf, lookback_for(high_tf))
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if low is None or len(low) < 40:
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return None
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result = analyze_symbol(low, mid, high, **eng)
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td = trade_date or (
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low.trade_dates[-1] if low.trade_dates else datetime.now(timezone.utc).date()
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)
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row = _to_row(td, symbol, result, combo_id=combo["id"], combo_label=combo["label"])
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upsert_row(row)
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return row
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