feat: 多周期牛熊判定模块 — 方案一矩阵展示 + 四法投票 + 多TF策略
- engine/indicators/regime.py: RegimeDetector(四法投票) + MultiTimeframeRegime(多周期并行) 四法: EMA200斜率 / 价格vsEMA200 / ATH回撤 / 窄幅盘整(<3%振幅) 全部 O(1)/bar 增量计算,适用于回测和实时 - engine/example/regime_display.py: 多周期牛熊矩阵展示脚本 独立加载各周期数据 → 运行判定 → 日线对齐矩阵 + 详细拆解 + 统计 输出 engine/backtest/REGIME_MATRIX_BTCUSDT.md - engine/example/regime_mtf_strategy.py: 多周期共识策略 + 四策略对比回测 MTF Consensus: 1w定方向 + 1d确认 + 4h EMA入场 vs Old Regime(单TF基线) vs Long/Short(无过滤) - engine/indicators/__init__.py: 导出 RegimeDetector, MultiTimeframeRegime
This commit is contained in:
Rekey
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"""
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多周期牛熊矩阵 — 方案一：独立多周期判定矩阵
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在统一时间轴（1d bar 边界）上对齐全周期牛熊判定，
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输出矩阵表格、统计信息和详细拆解。
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用法:
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source .venv/bin/activate && python example/regime_display.py
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定制:
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python example/regime_display.py --symbol ETHUSDT
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python example/regime_display.py --matrix-rows 50 # 输出最近 50 个时间点
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"""
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import asyncio
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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_project_root = Path(__file__).resolve().parent.parent.parent
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if str(_project_root) not in sys.path:
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sys.path.insert(0, str(_project_root))
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from engine.common.config import config
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from engine.data.service import DataService, INTERVAL_MS, INTERVAL_TO_TABLE
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from engine.indicators.regime import RegimeDetector
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# ── 默认配置 ──
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SYMBOL = "BTCUSDT"
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TIMEFRAMES = ["1h", "4h", "1d", "1w"]
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MATRIX_ROWS = 30
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OUTPUT_FILE = None # 运行时设置
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def parse_args():
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"""解析命令行参数"""
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args = sys.argv[1:]
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kwargs = {"symbol": SYMBOL, "matrix_rows": MATRIX_ROWS}
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i = 0
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while i < len(args):
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if args[i] == "--symbol" and i + 1 < len(args):
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kwargs["symbol"] = args[i + 1].upper()
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i += 2
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elif args[i] == "--matrix-rows" and i + 1 < len(args):
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kwargs["matrix_rows"] = int(args[i + 1])
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i += 2
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else:
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i += 1
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return kwargs
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# ════════════════════════════════════════════════════════
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# 1. 加载各周期数据并运行独立判定
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# ════════════════════════════════════════════════════════
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async def load_and_detect(
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ds: DataService, symbol: str, timeframe: str
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) -> tuple[list[float], list[dict], datetime, datetime]:
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"""加载指定周期全量数据，逐根运行 RegimeDetector，返回 (times, regimes, 数据起始, 数据结束)
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regimes[i] = {"time_ms": float, "close": float, "regime": str, "detail": dict}
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仅从第 220 根 bar 开始有效判定，之前为 unknown。
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"""
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start, end = await ds.fetch_symbol_date_range(symbol, timeframe)
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all_klines = await ds.fetch_klines(
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symbol=symbol,
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interval=timeframe,
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start_time=start,
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end_time=end,
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limit=10_000_000,
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)
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det = RegimeDetector()
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regimes = []
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times = []
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for k in all_klines:
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times.append(k.open_time)
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det.update(k.close)
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if det.ready:
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regime = det.detect(k.close, len(det._e200) - 1)
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detail = det.detect_detail(k.close, len(det._e200) - 1)
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else:
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regime = "unknown"
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detail = {"final": "unknown"}
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regimes.append({
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"time_ms": k.open_time,
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"time_dt": datetime.fromtimestamp(k.open_time / 1000, tz=timezone.utc),
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"close": k.close,
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"regime": regime,
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"detail": detail,
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"ema200": det.ema200,
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"ath": det.ath,
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})
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return times, regimes, start, end
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# ════════════════════════════════════════════════════════
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# 2. 时间对齐 — 以日线为基准，查找各周期的即时判定
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# ════════════════════════════════════════════════════════
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def find_regime_at(
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regimes: list[dict], target_ms: float, interval_ms: float
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) -> dict:
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"""在指定周期的 regime 序列中，找到 target_ms 时刻的判定。
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规则：取开盘时间最接近且不晚于 target_ms 的那根 bar 的判定。
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若 target_ms 在该 bar 的 [open_time, open_time + interval_ms) 范围内，返回该 bar 的判定。
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"""
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best = None
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for r in regimes:
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bar_time = r["time_ms"]
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if bar_time <= target_ms < bar_time + interval_ms:
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return r
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if bar_time > target_ms:
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break
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best = r
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return best
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def build_matrix(
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times_by_tf: dict[str, list[float]],
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regimes_by_tf: dict[str, list[dict]],
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reference_tf: str = "1d",
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num_rows: int = MATRIX_ROWS,
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) -> list[dict]:
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"""以 reference_tf 的 bar 时间点为基准，构建多周期判定矩阵。
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Returns:
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[{time_dt, time_str, 1h: regime, 4h: regime, 1d: regime, 1w: regime}, ...]
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按时间倒序，只包含各周期均有有效数据的时间点。
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"""
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ref_regimes = regimes_by_tf[reference_tf]
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# 只取有效区间（所有周期都过了 220 根 bar 之后）
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ref_valid = [
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r for r in ref_regimes
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if r["regime"] != "unknown"
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]
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matrix = []
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for ref_r in ref_valid[-num_rows:]:
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row = {
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"time_dt": ref_r["time_dt"],
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"time_str": ref_r["time_dt"].strftime("%Y-%m-%d %H:%M"),
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"time_ms": ref_r["time_ms"],
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f"{reference_tf}_close": ref_r["close"],
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}
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for tf in TIMEFRAMES:
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if tf == reference_tf:
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row[tf] = ref_r["regime"]
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row[f"{tf}_detail"] = ref_r["detail"]
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row[f"{tf}_ema200"] = ref_r.get("ema200", 0)
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row[f"{tf}_ath"] = ref_r.get("ath", 0)
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else:
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tf_regime = find_regime_at(
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regimes_by_tf[tf],
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ref_r["time_ms"],
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INTERVAL_MS[tf],
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)
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if tf_regime:
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row[tf] = tf_regime["regime"]
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row[f"{tf}_detail"] = tf_regime["detail"]
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row[f"{tf}_ema200"] = tf_regime.get("ema200", 0)
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row[f"{tf}_ath"] = tf_regime.get("ath", 0)
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else:
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row[tf] = "—"
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matrix.append(row)
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# 倒序（最新在前）
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return list(reversed(matrix))
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# ════════════════════════════════════════════════════════
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# 3. 统计分析
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# ════════════════════════════════════════════════════════
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def regime_icon(r: str) -> str:
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return {"bull": "🐂", "bear": "🐻", "sideways": "⚪", "unknown": "⏳", "—": "—"}.get(r, r)
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def calc_alignment_stats(matrix: list[dict]) -> dict:
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"""计算各周期判定对齐统计"""
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total = len(matrix)
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if total == 0:
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return {}
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stats = {}
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for tf in TIMEFRAMES:
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cnt_bull = sum(1 for r in matrix if r.get(tf) == "bull")
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cnt_bear = sum(1 for r in matrix if r.get(tf) == "bear")
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cnt_side = sum(1 for r in matrix if r.get(tf) == "sideways")
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stats[tf] = {
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"bull": cnt_bull,
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"bull_pct": cnt_bull / total * 100,
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"bear": cnt_bear,
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"bear_pct": cnt_bear / total * 100,
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"sideways": cnt_side,
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"sideways_pct": cnt_side / total * 100,
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}
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# 全周期一致（所有非 "—" 的周期判定相同）
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all_agree = 0
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for r in matrix:
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regimes = [r[tf] for tf in TIMEFRAMES if r.get(tf) not in ("—", "unknown")]
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if regimes and len(set(regimes)) == 1:
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all_agree += 1
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stats["all_agree"] = all_agree
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stats["all_agree_pct"] = all_agree / total * 100
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# 大小周期背离（1h 与 1w 相反）
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diverge = 0
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for r in matrix:
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r1h = r.get("1h")
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r1w = r.get("1w")
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if r1h and r1w and r1h != "—" and r1w != "—":
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if (r1h == "bull" and r1w == "bear") or (r1h == "bear" and r1w == "bull"):
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diverge += 1
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stats["diverge_1h_1w"] = diverge
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stats["diverge_1h_1w_pct"] = diverge / total * 100
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return stats
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# ════════════════════════════════════════════════════════
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# 4. 输出格式化
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# ════════════════════════════════════════════════════════
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def render_matrix_table(matrix: list[dict]) -> str:
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"""渲染矩阵表格为 Markdown 格式"""
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header = "| 时间 |"
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sep = "|------|"
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for tf in TIMEFRAMES:
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header += f" {tf} |"
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sep += "----|"
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header += " BTC Close |"
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sep += "-----------|"
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lines = [header, sep]
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for row in matrix:
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line = f"| {row['time_str']} |"
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for tf in TIMEFRAMES:
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r = row.get(tf, "—")
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line += f" {regime_icon(r)} {r} |"
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close_str = f"{row.get('1d_close', 0):.2f}"
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line += f" {close_str} |"
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lines.append(line)
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return "\n".join(lines)
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def render_detail_table(matrix: list[dict], tf: str, n: int = 5) -> str:
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"""渲染单个周期的详细判定表（最近 n 条）"""
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lines = [
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f"### {tf} 详细判定（最近 {n} 个时间点）",
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"",
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"| 时间 | 最终 | EMA200斜率 | 价格vsEMA200 | ATH回撤 | 窄幅盘整 | 振幅% | 牛票 | 熊票 | 震票 | EMA200 | ATH |",
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"|------|------|-----------|-------------|---------|---------|------|------|------|------|--------|-----|",
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]
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detail_key = f"{tf}_detail"
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for row in matrix[:n]:
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d = row.get(detail_key, {})
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f_icon = regime_icon(d.get("final", "—"))
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ema200_val = row.get(f"{tf}_ema200", 0)
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ath_val = row.get(f"{tf}_ath", 0)
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range_pct = d.get("range_pct", 0) * 100
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lines.append(
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f"| {row['time_str']} | {f_icon} {d.get('final', '—')} | "
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f"{d.get('ema200_slope', '—')} | {d.get('price_vs_ema200', '—')} | "
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f"{d.get('ath_drawdown', '—')} | {d.get('price_range', '—')} | "
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f"{range_pct:.1f}% | {d.get('bull_votes', '—')} | "
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f"{d.get('bear_votes', '—')} | {d.get('sideways_votes', '—')} | "
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f"{ema200_val:.2f} | {ath_val:.2f} |"
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)
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return "\n".join(lines)
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def render_current_status(regimes_by_tf: dict[str, list[dict]]) -> str:
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"""渲染当前最新状态"""
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lines = [
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"## 当前各周期牛熊状态",
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"",
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"| 周期 | 判定 | EMA200斜率 | 价格vsEMA200 | ATH回撤 | 窄幅盘整 | 振幅% | 牛票 | 熊票 | 震票 | EMA200 | Close |",
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"|------|------|-----------|-------------|---------|---------|------|------|------|------|--------|-------|",
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]
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for tf in TIMEFRAMES:
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regs = regimes_by_tf[tf]
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if not regs or regs[-1]["regime"] == "unknown":
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lines.append(f"| {tf} | ⏳ 数据不足 | — | — | — | — | — | — | — | — | — | — |")
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continue
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r = regs[-1]
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d = r["detail"]
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icon = regime_icon(r["regime"])
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range_pct = d.get("range_pct", 0) * 100
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lines.append(
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f"| {tf} | {icon} **{r['regime']}** | "
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f"{d.get('ema200_slope', '—')} | {d.get('price_vs_ema200', '—')} | "
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f"{d.get('ath_drawdown', '—')} | {d.get('price_range', '—')} | "
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f"{range_pct:.1f}% | {d.get('bull_votes', '—')} | "
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f"{d.get('bear_votes', '—')} | {d.get('sideways_votes', '—')} | "
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f"{r.get('ema200', 0):.2f} | {r['close']:.2f} |"
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)
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return "\n".join(lines)
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def render_stats(stats: dict) -> str:
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"""渲染统计信息"""
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lines = [
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"## 周期判定分布统计",
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"",
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"| 周期 | 🐂 牛市 | 占比 | 🐻 熊市 | 占比 | ⚪ 震荡 | 占比 |",
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"|------|--------|------|--------|------|--------|------|",
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]
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for tf in TIMEFRAMES:
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s = stats.get(tf, {})
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lines.append(
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f"| {tf} | {s.get('bull', 0)} | {s.get('bull_pct', 0):.1f}% | "
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f"{s.get('bear', 0)} | {s.get('bear_pct', 0):.1f}% | "
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f"{s.get('sideways', 0)} | {s.get('sideways_pct', 0):.1f}% |"
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)
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lines.append("")
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lines.append("## 周期一致性统计")
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lines.append("")
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lines.append(f"- **全周期一致**（所有周期判定相同）：{stats.get('all_agree', 0)} 天 ({stats.get('all_agree_pct', 0):.1f}%)")
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lines.append(f"- **1h ↔ 1w 背离**（1h 与 1w 方向相反）：{stats.get('diverge_1h_1w', 0)} 天 ({stats.get('diverge_1h_1w_pct', 0):.1f}%)")
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return "\n".join(lines)
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# ════════════════════════════════════════════════════════
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# 主流程
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# ════════════════════════════════════════════════════════
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async def main():
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kw = parse_args()
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symbol = kw["symbol"]
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matrix_rows = kw["matrix_rows"]
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global OUTPUT_FILE
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OUTPUT_FILE = (
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Path(__file__).resolve().parent.parent
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/ "backtest"
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/ f"REGIME_MATRIX_{symbol}.md"
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)
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out: list[str] = []
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def w(line: str = ""):
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out.append(line)
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print(line)
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w(f"# 多周期牛熊判定矩阵 — {symbol}")
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w()
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w(f"> 生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} UTC")
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w(f"> 判定方法：EMA200斜率 + 价格vsEMA200 + ATH回撤 + 窄幅盘整，四选二投票")
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w(f"> 时间对齐基准：日线(1d) bar 边界")
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w()
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# ── 加载数据并运行判定 ──
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ds = DataService(config.db)
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await ds.connect()
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try:
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times_by_tf: dict[str, list[float]] = {}
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regimes_by_tf: dict[str, list[dict]] = {}
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date_ranges: dict[str, tuple[datetime, datetime]] = {}
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for tf in TIMEFRAMES:
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print(f" 加载 {tf} 数据...", end=" ", flush=True)
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times, regimes, data_start, data_end = await load_and_detect(ds, symbol, tf)
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times_by_tf[tf] = times
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regimes_by_tf[tf] = regimes
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date_ranges[tf] = (data_start, data_end)
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valid_count = sum(1 for r in regimes if r["regime"] != "unknown")
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print(f"{len(regimes)} 根 bar，{valid_count} 根有效（>220热身）")
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# ── 数据范围 ──
|
||||
w()
|
||||
w("## 数据覆盖范围")
|
||||
w()
|
||||
w("| 周期 | 数据条数 | 有效条数 | 起始时间 | 结束时间 |")
|
||||
w("|------|---------|---------|---------|---------|")
|
||||
for tf in TIMEFRAMES:
|
||||
regs = regimes_by_tf[tf]
|
||||
valid = sum(1 for r in regs if r["regime"] != "unknown")
|
||||
s, e = date_ranges[tf]
|
||||
w(f"| {tf} | {len(regs)} | {valid} | {s.strftime('%Y-%m-%d')} | {e.strftime('%Y-%m-%d')} |")
|
||||
w()
|
||||
|
||||
# ── 当前状态 ──
|
||||
w(render_current_status(regimes_by_tf))
|
||||
w()
|
||||
|
||||
# ── 历史矩阵 ──
|
||||
matrix = build_matrix(times_by_tf, regimes_by_tf, "1d", num_rows=matrix_rows)
|
||||
|
||||
w(f"## 历史牛熊矩阵（最近 {len(matrix)} 个日线时间点）")
|
||||
w()
|
||||
w("> 以日线 bar 的 open_time 为基准，查找各周期在该时刻的即时判定。")
|
||||
w()
|
||||
w(render_matrix_table(matrix))
|
||||
w()
|
||||
|
||||
# ── 各周期详细拆解 ──
|
||||
w("## 各周期详细拆解")
|
||||
w()
|
||||
for tf in TIMEFRAMES:
|
||||
w(render_detail_table(matrix, tf, n=min(5, len(matrix))))
|
||||
w()
|
||||
|
||||
# ── 统计 ──
|
||||
stats = calc_alignment_stats(matrix)
|
||||
w(render_stats(stats))
|
||||
w()
|
||||
|
||||
finally:
|
||||
await ds.close()
|
||||
|
||||
# ── 写出文件 ──
|
||||
OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(OUTPUT_FILE, "w", encoding="utf-8") as f:
|
||||
f.write("\n".join(out) + "\n")
|
||||
|
||||
print(f"\n✓ 结果已保存到: {OUTPUT_FILE}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user