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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"""Decision Engine — multi-timeframe fusion and tradability (Architecture v1.0)."""
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from __future__ import annotations
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from crypto_wyckoff.domain_models import (
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DecisionSignal,
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EngineResult,
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RiskLevel,
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WyckoffCycle,
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WyckoffEvent,
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WyckoffPhase,
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)
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BULL_CYCLES = {
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WyckoffCycle.ACCUMULATION.value,
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WyckoffCycle.RE_ACCUMULATION.value,
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WyckoffCycle.MARKUP.value,
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}
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BEAR_CYCLES = {
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WyckoffCycle.DISTRIBUTION.value,
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WyckoffCycle.RE_DISTRIBUTION.value,
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WyckoffCycle.MARKDOWN.value,
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}
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class DecisionEngine:
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name = "Decision"
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version = "1.0.0"
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def run(
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self,
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monthly_cycle: EngineResult,
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weekly_cycle: EngineResult,
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weekly_phase: EngineResult,
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weekly_event: EngineResult,
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daily_event: EngineResult,
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daily_signal: EngineResult,
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) -> EngineResult:
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m_cycle = monthly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
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w_cycle = weekly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
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w_phase = weekly_phase.payload.get("phase", WyckoffPhase.NONE.value)
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w_event = weekly_event.payload.get("current_event", WyckoffEvent.NONE.value)
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d_event = daily_event.payload.get("current_event", WyckoffEvent.NONE.value)
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trend_score = float(monthly_cycle.payload.get("trend_score", monthly_cycle.score))
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structure_score = float(weekly_phase.payload.get("structure_score", weekly_phase.score))
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entry_score = float(daily_event.payload.get("entry_score", daily_event.score))
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overall_score = 0.30 * trend_score + 0.30 * structure_score + 0.40 * entry_score
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reasons: list[str] = []
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warnings: list[str] = []
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alignment = 50.0
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m_bull = m_cycle in BULL_CYCLES
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m_bear = m_cycle in BEAR_CYCLES
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w_bull = w_cycle in BULL_CYCLES
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d_bullish_event = d_event in {
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WyckoffEvent.SPRING.value,
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WyckoffEvent.TEST.value,
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WyckoffEvent.SOS.value,
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WyckoffEvent.LPS.value,
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WyckoffEvent.JUMP.value,
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WyckoffEvent.BACKUP.value,
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}
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d_bearish_event = d_event in {
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WyckoffEvent.UTAD.value,
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WyckoffEvent.SOW.value,
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WyckoffEvent.LPSY.value,
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}
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# Alignment scoring
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if m_bull and w_bull and d_bullish_event:
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alignment = 92.0
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reasons.append("✓ 月/周多头结构与日线多头事件一致")
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elif m_bull and d_bullish_event:
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alignment = 78.0
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reasons.append("✓ 月线支持，日线有入场事件")
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if not w_bull:
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warnings.append("周线结构未完全确认")
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alignment -= 8
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elif m_bear and d_bullish_event:
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alignment = 35.0
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reasons.append("✗ 月线派发/下跌，日线弹簧可能只是反弹")
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elif m_bear and d_bearish_event:
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alignment = 85.0
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reasons.append("✓ 空头多周期一致")
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else:
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alignment = 55.0
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reasons.append("○ 多周期部分一致，需观察")
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if w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value) and m_bull:
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alignment = min(98.0, alignment + 6)
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reasons.append(f"✓ 周线阶段 {w_phase} 结构成熟（{w_event}）")
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active = daily_event.payload.get("active_events") or daily_event.payload.get("recent_events") or []
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if d_event == WyckoffEvent.SPRING.value and len(active) >= 3:
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alignment = min(98.0, alignment + 4)
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reasons.append("✓ 日线多重事件同时确认")
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# Decision signal — hard gate on monthly bear + daily spring
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decision = DecisionSignal.WATCH.value
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risk = RiskLevel.MEDIUM.value
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if m_bear and d_event == WyckoffEvent.SPRING.value:
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decision = DecisionSignal.WATCH.value
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risk = RiskLevel.HIGH.value
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overall_score = min(overall_score, 55.0)
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reasons.append("→ 决策：观察（月线不支持，禁止追日线弹簧）")
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elif m_bear and d_bullish_event:
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decision = DecisionSignal.AVOID.value
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risk = RiskLevel.HIGH.value
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overall_score = min(overall_score, 48.0)
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reasons.append("→ 决策：回避（逆大周期多头事件）")
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elif (
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m_bull
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and w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value, WyckoffPhase.C.value)
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and d_event in (WyckoffEvent.SPRING.value, WyckoffEvent.LPS.value, WyckoffEvent.SOS.value)
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and alignment >= 85
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and overall_score >= 80
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):
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decision = DecisionSignal.STRONG_BUY.value
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risk = RiskLevel.LOW.value
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reasons.append("→ 决策：强烈买入（三级共振）")
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elif m_bull and d_bullish_event and overall_score >= 68 and alignment >= 70:
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decision = DecisionSignal.BUY.value
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risk = RiskLevel.LOW.value if alignment >= 80 else RiskLevel.MEDIUM.value
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reasons.append("→ 决策：买入")
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elif m_bear and d_bearish_event and overall_score >= 65:
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decision = DecisionSignal.SELL.value
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risk = RiskLevel.MEDIUM.value
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reasons.append("→ 决策：卖出")
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else:
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decision = DecisionSignal.WATCH.value
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reasons.append("→ 决策：观察")
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# Stars from score + alignment
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combo = 0.6 * overall_score + 0.4 * alignment
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if combo >= 90:
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stars = 5
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elif combo >= 80:
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stars = 4
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elif combo >= 65:
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stars = 3
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elif combo >= 50:
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stars = 2
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else:
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stars = 1
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overall_confidence = (
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0.25 * monthly_cycle.confidence
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+ 0.25 * weekly_phase.confidence
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+ 0.25 * daily_event.confidence
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+ 0.25 * daily_signal.confidence
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)
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# Weak event pulls overall down
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if daily_event.confidence < 60:
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overall_confidence = min(overall_confidence, daily_event.confidence + 15)
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return EngineResult(
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name=self.name,
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version=self.version,
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confidence=overall_confidence,
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score=overall_score,
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reasons=reasons,
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warnings=warnings,
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metrics={
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"trend_score": trend_score,
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"structure_score": structure_score,
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"entry_score": entry_score,
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"alignment": alignment,
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"stars": stars,
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},
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payload={
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"decision_signal": decision,
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"alignment": alignment,
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"stars": stars,
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"risk": risk,
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"overall_score": overall_score,
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"overall_confidence": overall_confidence,
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"trend_score": trend_score,
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"structure_score": structure_score,
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"entry_score": entry_score,
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"m_cycle": m_cycle,
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"w_cycle": w_cycle,
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"w_phase": w_phase,
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"w_event": w_event,
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"d_event": d_event,
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# Facts preserved — never overwritten
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"facts": {
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"monthly": {"cycle": m_cycle},
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"weekly": {"cycle": w_cycle, "phase": w_phase, "event": w_event},
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"daily": {"event": d_event},
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},
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},
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)
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