mirror of
https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
synced 2026-09-12 17:54:15 +08:00
shy3130
4ee55e40c6
后端 - 新增 services/regime_builder.py：5 档日级市场状态分级（strong/lean_strong/range/lean_weak/weak） - 新增 api/regime.py：5 个 regime 查询接口 - daily_pipeline 接入 regime 构建（step 2.6，soft-fail + 双检测自愈） - 回测 strategy.py 新增 regime_filter 过滤：采用 T-1 防未来函数，仅作用于入场信号 在 composite / matrix_native / prepared 三处注入 entry_time_mask - backtest API 透传 regime_filter（REST + SSE）；_make_job_key 纳入 regime_filter 以隔离缓存 - 兼容 _RepoStub 测试夹具（store 属性缺失场景） 前端 - 新增 Regime 页面、路由、导航入口、api 类型与 queryKeys - 叠加策略回测页新增 regime 过滤器控件 测试 - 新增 tests/test_regime_builder.py（18 项），覆盖分级逻辑、T-1 防未来函数、空值降级、三处 mask 注入 - 全量后端测试 582 passed；前端 pnpm build 通过；git diff --check 无空白错误
420 lines
16 KiB
Python
420 lines
16 KiB
Python
"""市场环境(regime)计算 — 纯函数模块。
|
|
|
|
职责: 从已算好的 enriched 数据(含信号列)按日聚合环境指标, 用规则引擎分类离散状态,
|
|
持久化为时序表。不重算指标(不走 compute_indicators), 不依赖 quote/depth service。
|
|
|
|
性能设计:
|
|
- run_regime_batch 用 polars group_by("date").agg(...) 一次聚合多日, 非逐日循环。
|
|
- 数据走 repo.get_enriched_range(内存缓存, 已含信号列); 缓存不覆盖时走 scan_parquet 慢路径。
|
|
|
|
与 market_overview_builder 的区别:
|
|
- overview 面向单日详情(实时总览), 重算指标。
|
|
- regime 面向多日聚合统计(时序分析), 只聚合不重算。
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import logging
|
|
from datetime import date
|
|
from pathlib import Path
|
|
|
|
import polars as pl
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
# ───────────────────────── 状态分类阈值(可调) ─────────────────────────
|
|
# 各维度子分用归一化映射(线性插值到 0-100), 再加权求和。
|
|
# 综合 = 赚钱效应×0.4 + 指数趋势×0.3 + 板块结构×0.2 + 活跃度×0.1
|
|
|
|
WEIGHTS = {
|
|
"money_effect": 0.4, # 赚钱效应(涨停/封板率/涨跌比)
|
|
"index_trend": 0.3, # 指数趋势(指数涨幅/MA20上方占比)
|
|
"board_structure": 0.2, # 板块结构(涨跌离散度, 简化)
|
|
"activity": 0.1, # 活跃度(成交额/换手)
|
|
}
|
|
|
|
# 离散状态阈值(综合分)
|
|
STATE_STRONG = 75 # >= 强势
|
|
STATE_LEAN_STRONG = 60 # 60-75 偏强
|
|
STATE_RANGE = 40 # 40-60 震荡
|
|
STATE_LEAN_WEAK = 25 # 25-40 偏弱
|
|
# < 25 弱势
|
|
|
|
# 归一化映射的参考点(线性插值 0-100)
|
|
_LIN = {
|
|
"limit_up": [(0, 0), (15, 40), (30, 70), (50, 100)], # 涨停数
|
|
"seal_rate": [(0.3, 0), (0.5, 40), (0.7, 70), (0.9, 100)], # 封板率
|
|
"up_ratio": [(0.4, 0), (1.0, 40), (2.0, 70), (3.0, 100)], # 涨跌比
|
|
"index_pct": [(-0.02, 0), (0.0, 40), (0.01, 70), (0.02, 100)], # 指数涨幅
|
|
"above_ma20": [(0.3, 0), (0.5, 40), (0.6, 70), (0.8, 100)], # MA20上方占比
|
|
"amount": [(0.5e11, 0), (1e11, 40), (1.5e11, 70), (2.5e11, 100)], # 成交额
|
|
}
|
|
|
|
STATE_LABELS = {
|
|
"strong": "强势",
|
|
"lean_strong": "偏强",
|
|
"range": "震荡",
|
|
"lean_weak": "偏弱",
|
|
"weak": "弱势",
|
|
}
|
|
|
|
|
|
def _linear_score(value: float, points: list[tuple[float, float]]) -> float:
|
|
"""分段线性插值。points 是 [(输入值, 输出分)] 升序列表。"""
|
|
if value <= points[0][0]:
|
|
return float(points[0][1])
|
|
if value >= points[-1][0]:
|
|
return float(points[-1][1])
|
|
for i in range(len(points) - 1):
|
|
x0, y0 = points[i]
|
|
x1, y1 = points[i + 1]
|
|
if x0 <= value <= x1:
|
|
if x1 == x0:
|
|
return float(y0)
|
|
return float(y0 + (y1 - y0) * (value - x0) / (x1 - x0))
|
|
return float(points[-1][1])
|
|
|
|
|
|
def classify_state(metrics: dict) -> tuple[str, int]:
|
|
"""规则引擎: 多维指标 → 离散状态 + 综合分(0-100)。
|
|
|
|
各维度子分加权: 赚钱效应(涨停数/封板率/涨跌比) + 指数趋势(涨幅/MA20)
|
|
+ 板块结构(涨跌离散度简化) + 活跃度(成交额)。
|
|
"""
|
|
# 赚钱效应子分 = 涨停/封板率/涨跌比 三者平均
|
|
limit_up = metrics.get("limit_up", 0) or 0
|
|
seal_rate = metrics.get("seal_rate", 0.5) or 0.5
|
|
up_ratio = metrics.get("up_ratio", 1.0) or 1.0
|
|
money = (
|
|
_linear_score(limit_up, _LIN["limit_up"])
|
|
+ _linear_score(seal_rate, _LIN["seal_rate"])
|
|
+ _linear_score(up_ratio, _LIN["up_ratio"])
|
|
) / 3
|
|
|
|
index_pct = metrics.get("index_pct", 0.0) or 0.0
|
|
above_ma20 = metrics.get("above_ma20_pct", 0.5) or 0.5
|
|
index_trend = (
|
|
_linear_score(index_pct, _LIN["index_pct"])
|
|
+ _linear_score(above_ma20, _LIN["above_ma20"])
|
|
) / 2
|
|
|
|
# 板块结构: 用涨跌家数比的偏离度简化(涨跌越均衡=震荡, 极端=方向明确)
|
|
# up_ratio 接近 1 → 震荡(中分); 远离 1 → 方向明确(高低分看方向)
|
|
# 已在 money_effect 的 up_ratio 体现, 这里用涨停+跌停的对比做补充
|
|
limit_down = metrics.get("limit_down", 0) or 0
|
|
if limit_up + limit_down > 0:
|
|
board = (limit_up - limit_down) / max(limit_up + limit_down, 1) * 50 + 50
|
|
else:
|
|
board = 50.0
|
|
|
|
total_amount = metrics.get("total_amount", 1e11) or 1e11
|
|
activity = _linear_score(total_amount, _LIN["amount"])
|
|
|
|
score = (
|
|
money * WEIGHTS["money_effect"]
|
|
+ index_trend * WEIGHTS["index_trend"]
|
|
+ board * WEIGHTS["board_structure"]
|
|
+ activity * WEIGHTS["activity"]
|
|
)
|
|
score = max(0, min(100, round(score)))
|
|
|
|
if score >= STATE_STRONG:
|
|
state = "strong"
|
|
elif score >= STATE_LEAN_STRONG:
|
|
state = "lean_strong"
|
|
elif score >= STATE_RANGE:
|
|
state = "range"
|
|
elif score >= STATE_LEAN_WEAK:
|
|
state = "lean_weak"
|
|
else:
|
|
state = "weak"
|
|
return state, score
|
|
|
|
|
|
# ───────────────────────── 批量聚合 ─────────────────────────
|
|
|
|
def _aggregate_daily(df: pl.DataFrame, index_pct_map: dict | None = None) -> pl.DataFrame:
|
|
"""对多日多 symbol 的 enriched DataFrame 按 date 聚合环境指标。
|
|
|
|
纯 polars 聚合, 不重算指标(假设 df 已含 signal_*/change_pct/ma20 等列)。
|
|
index_pct_map: {date: 指数涨幅} 可选, 由调用方从指数数据预先算好。
|
|
"""
|
|
needed = ["date", "change_pct", "amount", "signal_limit_up",
|
|
"signal_limit_down", "signal_broken_limit_up",
|
|
"consecutive_limit_ups", "close", "ma20"]
|
|
avail = [c for c in needed if c in df.columns]
|
|
if "date" not in avail or "change_pct" not in avail:
|
|
return pl.DataFrame()
|
|
|
|
# 基础聚合
|
|
agg_exprs = []
|
|
if "change_pct" in avail:
|
|
agg_exprs.append(pl.col("change_pct"))
|
|
grouped = df.group_by("date").agg(
|
|
*[
|
|
pl.col("change_pct").gt(0).sum().alias("up_count")
|
|
if "change_pct" in avail else pl.lit(0).alias("up_count"),
|
|
pl.col("change_pct").lt(0).sum().alias("down_count")
|
|
if "change_pct" in avail else pl.lit(0).alias("down_count"),
|
|
pl.len().alias("total_count"),
|
|
],
|
|
*(
|
|
[pl.col("signal_limit_up").cast(pl.Boolean).sum().alias("limit_up")]
|
|
if "signal_limit_up" in avail else [pl.lit(0).alias("limit_up")]
|
|
),
|
|
*(
|
|
[pl.col("signal_limit_down").cast(pl.Boolean).sum().alias("limit_down")]
|
|
if "signal_limit_down" in avail else [pl.lit(0).alias("limit_down")]
|
|
),
|
|
*(
|
|
[pl.col("signal_broken_limit_up").cast(pl.Boolean).sum().alias("broken_limit")]
|
|
if "signal_broken_limit_up" in avail else [pl.lit(0).alias("broken_limit")]
|
|
),
|
|
*(
|
|
[pl.col("consecutive_limit_ups").max().alias("max_consecutive")]
|
|
if "consecutive_limit_ups" in avail else [pl.lit(0).alias("max_consecutive")]
|
|
),
|
|
*(
|
|
[pl.col("amount").sum().alias("total_amount")]
|
|
if "amount" in avail else [pl.lit(0).alias("total_amount")]
|
|
),
|
|
*(
|
|
[pl.col("amount").mean().alias("avg_amount")]
|
|
if "amount" in avail else [pl.lit(0).alias("avg_amount")]
|
|
),
|
|
).sort("date")
|
|
|
|
# 转成 dict 列表做后续计算(polars 表达式难表达的比率/MA20占比/分类)
|
|
index_pct_map = index_pct_map or {}
|
|
rows = []
|
|
for r in grouped.iter_rows(named=True):
|
|
up = r.get("up_count", 0) or 0
|
|
down = r.get("down_count", 0) or 0
|
|
limit_up = r.get("limit_up", 0) or 0
|
|
broken = r.get("broken_limit", 0) or 0
|
|
# MA20 上方占比
|
|
ma20_above = 0
|
|
if "close" in avail and "ma20" in avail:
|
|
day_df = df.filter(pl.col("date") == r["date"])
|
|
if not day_df.is_empty() and "ma20" in day_df.columns:
|
|
valid = day_df.filter(pl.col("ma20").is_not_null() & (pl.col("ma20") > 0))
|
|
if not valid.is_empty():
|
|
above = valid.filter(pl.col("close") > pl.col("ma20"))
|
|
ma20_above = above.height / valid.height
|
|
metrics = {
|
|
"limit_up": limit_up,
|
|
"limit_down": r.get("limit_down", 0) or 0,
|
|
"broken_limit": broken,
|
|
"max_consecutive": r.get("max_consecutive", 0) or 0,
|
|
"seal_rate": (limit_up / (limit_up + broken)) if (limit_up + broken) > 0 else 0.5,
|
|
"up_count": up,
|
|
"down_count": down,
|
|
"up_ratio": (up / down) if down > 0 else (float(up) if up > 0 else 1.0),
|
|
"index_pct": index_pct_map.get(r["date"], 0.0),
|
|
"above_ma20_pct": ma20_above,
|
|
"total_amount": r.get("total_amount", 0) or 0,
|
|
"avg_turnover": r.get("avg_amount", 0) or 0,
|
|
}
|
|
state, score = classify_state(metrics)
|
|
rows.append({
|
|
"date": r["date"],
|
|
"state": state,
|
|
"score": score,
|
|
"limit_up": limit_up,
|
|
"limit_down": metrics["limit_down"],
|
|
"broken_limit": broken,
|
|
"max_consecutive": metrics["max_consecutive"],
|
|
"seal_rate": round(metrics["seal_rate"], 4),
|
|
"up_count": up,
|
|
"down_count": down,
|
|
"up_ratio": round(metrics["up_ratio"], 4),
|
|
"index_pct": round(metrics["index_pct"], 4),
|
|
"above_ma20_pct": round(ma20_above, 4),
|
|
"total_amount": metrics["total_amount"],
|
|
"avg_turnover": metrics["avg_turnover"],
|
|
})
|
|
return pl.DataFrame(rows) if rows else pl.DataFrame()
|
|
|
|
|
|
def _scan_enriched_fallback(repo, start: date, end: date) -> pl.DataFrame | None:
|
|
"""缓存不覆盖时的慢路径: 一次性 scan 全部 enriched parquet + 重算指标。
|
|
|
|
仅在 regime 首次全量回填或缓存未预热时触发。返回含信号列的多日 DataFrame。
|
|
"""
|
|
try:
|
|
enriched_dir = repo.store.data_dir / "kline_daily_enriched"
|
|
if not enriched_dir.exists():
|
|
return None
|
|
from app.indicators.pipeline import compute_indicators, compute_limit_signals
|
|
df = pl.scan_parquet(enriched_dir / "**" / "*.parquet").filter(
|
|
(pl.col("date") >= start) & (pl.col("date") <= end)
|
|
).collect()
|
|
if df.is_empty():
|
|
return None
|
|
df = compute_indicators(df)
|
|
df = compute_limit_signals(df)
|
|
return df
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("regime scan_enriched_fallback failed: %s", e)
|
|
return None
|
|
|
|
|
|
def _load_index_pct(repo, start: date, end: date, symbol: str = "000001.SH") -> dict:
|
|
"""读取主力指数日K, 算每日涨幅 → {date: pct}。指数数量少, 单次读取可接受。"""
|
|
try:
|
|
df = repo.get_index_daily(symbol, start, end, columns=["date", "change_pct"])
|
|
if df.is_empty() or "change_pct" not in df.columns:
|
|
return {}
|
|
return {r["date"]: float(r["change_pct"] or 0) for r in df.iter_rows(named=True)}
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("regime load_index_pct failed: %s", e)
|
|
return {}
|
|
|
|
|
|
def run_regime_batch(repo, start: date, end: date) -> pl.DataFrame:
|
|
"""批算 [start, end] 的环境时序。
|
|
|
|
性能: 优先 repo.get_enriched_range(内存缓存); 缓存不覆盖走 scan_parquet 慢路径。
|
|
按 date group_by 聚合, 不逐日重算。返回完整时序 DataFrame(可能为空)。
|
|
"""
|
|
if start > end:
|
|
return pl.DataFrame()
|
|
|
|
# 指数涨幅(主力指数)
|
|
index_pct_map = _load_index_pct(repo, start, end)
|
|
|
|
# enriched 多日数据(优先缓存)
|
|
df = repo.get_enriched_range(start, end)
|
|
if df is None or df.is_empty():
|
|
logger.info("regime batch: enriched cache miss [%s~%s], fallback to scan", start, end)
|
|
df = _scan_enriched_fallback(repo, start, end)
|
|
if df is None or df.is_empty():
|
|
logger.info("regime batch: no enriched data for [%s~%s]", start, end)
|
|
return pl.DataFrame()
|
|
|
|
return _aggregate_daily(df, index_pct_map)
|
|
|
|
|
|
# ───────────────────────── 持久化(upsert) ─────────────────────────
|
|
|
|
REGIME_DIR = "regime_history"
|
|
|
|
|
|
def regime_path(data_dir: Path) -> Path:
|
|
return data_dir / REGIME_DIR / "part.parquet"
|
|
|
|
|
|
def load_regime_history(data_dir: Path) -> pl.DataFrame:
|
|
"""读取全部 regime 时序; 不存在返回空 DataFrame。"""
|
|
p = regime_path(data_dir)
|
|
if not p.exists():
|
|
return pl.DataFrame()
|
|
try:
|
|
return pl.read_parquet(p)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("load_regime_history failed: %s", e)
|
|
return pl.DataFrame()
|
|
|
|
|
|
def upsert_regime_history(data_dir: Path, new_rows: pl.DataFrame) -> None:
|
|
"""按 date 覆盖(upsert): 重算的天覆盖旧行, 新天追加。
|
|
|
|
读旧 → anti-join 掉 new_rows 的天 → concat new_rows → 排序 → 写回。
|
|
"""
|
|
if new_rows.is_empty() or "date" not in new_rows.columns:
|
|
return
|
|
p = regime_path(data_dir)
|
|
p.parent.mkdir(parents=True, exist_ok=True)
|
|
new_dates = set(new_rows["date"].to_list())
|
|
old = load_regime_history(data_dir)
|
|
if old.is_empty():
|
|
combined = new_rows
|
|
else:
|
|
kept = old.filter(~pl.col("date").is_in(list(new_dates)))
|
|
combined = pl.concat([kept, new_rows], how="vertical_relaxed")
|
|
combined = combined.sort("date").unique(subset=["date"], keep="last")
|
|
combined.write_parquet(p)
|
|
|
|
|
|
def get_regime_coverage(data_dir: Path) -> dict:
|
|
"""返回 regime 时序的覆盖元信息(供数据画像/API)。"""
|
|
df = load_regime_history(data_dir)
|
|
if df.is_empty():
|
|
return {"rows": 0, "earliest_date": None, "latest_date": None}
|
|
return {
|
|
"rows": df.height,
|
|
"earliest_date": str(df["date"].min()),
|
|
"latest_date": str(df["date"].max()),
|
|
}
|
|
|
|
|
|
def detect_stale_dates(data_dir: Path, repo) -> list[date]:
|
|
"""检测 regime 已有但需要重算的天(enriched 被覆写)。
|
|
|
|
用 mtime 比对: enriched 分区 parquet 的 mtime > regime parquet 的 mtime
|
|
→ 该日 enriched 更新过, regime 需重算。
|
|
"""
|
|
regime_p = regime_path(data_dir)
|
|
if not regime_p.exists():
|
|
return []
|
|
regime_mtime = regime_p.stat().st_mtime
|
|
enriched_dir = repo.store.data_dir / "kline_daily_enriched"
|
|
if not enriched_dir.exists():
|
|
return []
|
|
stale: list[date] = []
|
|
existing = load_regime_history(data_dir)
|
|
if existing.is_empty():
|
|
return []
|
|
existing_dates = set(existing["date"].to_list())
|
|
for part in enriched_dir.glob("date=*/part.parquet"):
|
|
try:
|
|
ds = part.parent.name.replace("date=", "")
|
|
d = date.fromisoformat(ds)
|
|
except (ValueError, OSError):
|
|
continue
|
|
if d not in existing_dates:
|
|
continue
|
|
try:
|
|
if part.stat().st_mtime > regime_mtime:
|
|
stale.append(d)
|
|
except OSError:
|
|
continue
|
|
return sorted(stale)
|
|
|
|
|
|
def compute_regime_incremental(repo, data_dir: Path, *, today: date | None = None) -> pl.DataFrame:
|
|
"""增量计算 regime(供 daily_pipeline / 启动补算调用)。
|
|
|
|
双检测: 1) 缺口(enriched 有但 regime 没有) 2) stale(enriched 被覆写)。
|
|
自动补齐所有需要的日。返回本次新算的 DataFrame。
|
|
"""
|
|
today = today or date.today()
|
|
existing = load_regime_history(data_dir)
|
|
|
|
# 缺口: enriched 有哪些天, regime 缺哪些
|
|
enriched_dir = repo.store.data_dir / "kline_daily_enriched"
|
|
enriched_dates: set[date] = set()
|
|
if enriched_dir.exists():
|
|
for part in enriched_dir.glob("date=*/part.parquet"):
|
|
try:
|
|
ds = part.parent.name.replace("date=", "")
|
|
enriched_dates.add(date.fromisoformat(ds))
|
|
except ValueError:
|
|
continue
|
|
existing_dates = set(existing["date"].to_list()) if not existing.is_empty() else set()
|
|
missing = sorted(d for d in enriched_dates if d not in existing_dates and d <= today)
|
|
|
|
# stale: enriched 覆写过
|
|
stale = detect_stale_dates(data_dir, repo)
|
|
|
|
to_compute = sorted(set(missing) | set(stale))
|
|
if not to_compute:
|
|
logger.debug("regime incremental: nothing to compute")
|
|
return pl.DataFrame()
|
|
|
|
logger.info("regime incremental: compute %d days (missing=%d, stale=%d)",
|
|
len(to_compute), len(missing), len(stale))
|
|
new_rows = run_regime_batch(repo, start=to_compute[0], end=to_compute[-1])
|
|
if not new_rows.is_empty():
|
|
upsert_regime_history(data_dir, new_rows)
|
|
return new_rows
|