138 lines
5.1 KiB
Python
138 lines
5.1 KiB
Python
"""Market regime classification for strategy risk controls."""
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from __future__ import annotations
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def _safe_float(value, default: float = 0.0) -> float:
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try:
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if value is None or value == "":
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return default
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return float(value)
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except Exception:
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return default
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def _benchmark_change(overview: dict, symbol: str) -> float:
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benchmarks = overview.get("benchmarks") if isinstance(overview, dict) else {}
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item = benchmarks.get(symbol) if isinstance(benchmarks, dict) else {}
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return _safe_float((item or {}).get("change_24h"))
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def classify_market_regime(overview: dict | None) -> dict:
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"""Return a plain-language market regime from the latest market snapshot.
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The first version intentionally uses broad market facts that already exist
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in `market_overview`: BTC/ETH 24h change, alt breadth, hot/crash counts and
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funding heat. It is meant to be a guardrail, not a prediction model.
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"""
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data = overview if isinstance(overview, dict) else {}
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if not data or data.get("snapshot_missing") or _safe_float(data.get("sample_count")) <= 0:
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return {
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"regime": "unknown",
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"label": "数据不足",
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"confidence": 0.2,
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"risk_level": "medium",
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"position_multiplier": 0.75,
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"reasons": ["市场快照不足，保守运行但不直接停止交易"],
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"metrics": {},
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}
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btc_change = _benchmark_change(data, "BTC/USDT")
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eth_change = _benchmark_change(data, "ETH/USDT")
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adv_dec = _safe_float(data.get("advance_decline_ratio"))
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avg_change = _safe_float(data.get("avg_change_24h"))
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hot_count = int(_safe_float(data.get("hot_count_5pct")))
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crash_count = int(_safe_float(data.get("crash_count_5pct")))
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funding = data.get("funding") if isinstance(data.get("funding"), dict) else {}
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extreme_positive = int(_safe_float(funding.get("extreme_positive_count")))
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avg_funding = _safe_float(funding.get("avg_funding_rate"))
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metrics = {
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"btc_change_24h": btc_change,
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"eth_change_24h": eth_change,
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"advance_decline_ratio": adv_dec,
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"avg_change_24h": avg_change,
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"hot_count_5pct": hot_count,
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"crash_count_5pct": crash_count,
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"extreme_positive_funding_count": extreme_positive,
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"avg_funding_rate": avg_funding,
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}
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if btc_change <= -3 or eth_change <= -4 or adv_dec < 0.55 or crash_count >= 30:
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return {
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"regime": "risk_off",
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"label": "风险释放期",
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"confidence": 0.88,
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"risk_level": "critical",
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"position_multiplier": 0.25,
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"reasons": ["主流币或山寨广度明显走弱，新开山寨仓位必须显著降仓并提高质量门槛"],
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"metrics": metrics,
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}
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if crash_count >= 18 or adv_dec < 0.75 or (btc_change <= -1.5 and avg_change < 0):
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return {
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"regime": "risk_off",
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"label": "偏风险释放",
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"confidence": 0.75,
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"risk_level": "high",
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"position_multiplier": 0.35,
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"reasons": ["市场下跌覆盖面较大，只允许特别高质量的机会"],
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"metrics": metrics,
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}
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if hot_count >= 35 and extreme_positive >= 20 and avg_funding >= 0.00045:
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return {
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"regime": "meme_frenzy",
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"label": "情绪过热期",
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"confidence": 0.72,
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"risk_level": "high",
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"position_multiplier": 0.5,
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"reasons": ["强势币和正 funding 同时过热，追高回撤风险升高"],
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"metrics": metrics,
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}
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if avg_change >= 0.8 and adv_dec >= 1.2 and hot_count >= 15 and btc_change > -1:
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return {
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"regime": "altcoin_rotation",
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"label": "山寨轮动期",
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"confidence": 0.78,
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"risk_level": "medium",
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"position_multiplier": 1.0,
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"reasons": ["上涨覆盖面和强势币数量支持继续寻找精选机会"],
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"metrics": metrics,
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}
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if btc_change >= 1.2 and eth_change >= 0.8 and adv_dec >= 0.85:
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return {
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"regime": "btc_main_uptrend",
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"label": "主流带动期",
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"confidence": 0.7,
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"risk_level": "medium",
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"position_multiplier": 0.8,
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"reasons": ["BTC/ETH 提供方向支撑，但山寨仍要精选"],
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"metrics": metrics,
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}
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if abs(avg_change) <= 0.6 and 0.75 <= adv_dec <= 1.25:
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return {
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"regime": "sideways_chop",
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"label": "横盘震荡期",
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"confidence": 0.65,
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"risk_level": "medium",
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"position_multiplier": 0.65,
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"reasons": ["市场没有单边方向，追突破容易来回挨打，偏向等回踩"],
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"metrics": metrics,
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}
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return {
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"regime": "unknown",
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"label": "结构性行情",
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"confidence": 0.45,
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"risk_level": "medium",
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"position_multiplier": 0.75,
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"reasons": ["市场环境不够清晰，保持精选和轻仓"],
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"metrics": metrics,
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}
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__all__ = ["classify_market_regime"]
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