feat(web): 因子研究升级 + 因子组合页

- 因子元数据增加 brief 使用简介（后端 FactorDef + /api/factors 返回，9 个内置因子补齐文案）
- 因子研究页：
  · 时间默认「今日 ~ 近 6 个月」
  · 因子多选，逐个跑 IC/RankIC Job 并分卡展示报告
  · 目录显示使用简介，行点击展开公式/方向/用法详解
  · 新增计分规则说明与「去因子组合」入口
- 因子组合页 /factors/compose：多因子勾选+权重、组合得分规则说明（z-score×权重求和→TopN），
  可配 topN/调仓/区间/ST，一键回测 Job → 净值/回撤/月度/未建模标注
- 回测页时间默认同样近 6 月
- 验证：tsc + next build 通过；组合回测（momentum_60×1.5 + volatility_60×1.0）Job 成功归档
  EXP-330CAAAE（-13.51%，交易 20）；后端 factors/research/api 相关测试通过 / ruff clean

backend/app/api/factors.py

@@ -15,6 +15,7 @@ def list_factor_catalog() -> list[dict]:
         {
             "name": d.name,
             "description": d.description,
+            "brief": d.brief,
             "formula": d.formula,
             "frequency": d.frequency,
             "lookback": d.lookback,

backend/app/core/config.py

@@ -130,7 +130,9 @@ def get_settings() -> Settings:
     except (TypeError, ValueError):
         job_memory_limit_gb = 6
     try:
-        job_max_concurrent = int(os.environ.get("QLIB_JOB_MAX_CONCURRENT") or job_cfg.get("max_concurrent_jobs") or 2)
+        job_max_concurrent = int(
+            os.environ.get("QLIB_JOB_MAX_CONCURRENT") or job_cfg.get("max_concurrent_jobs") or 2
+        )
     except (TypeError, ValueError):
         job_max_concurrent = 2
 

backend/app/quant/factors.py

@@ -21,6 +21,7 @@ class FactorDef:
     name: str
     description: str
     formula: str
+    brief: str = ""  # 一句话使用简介（面向用户：怎么用、什么时候有效）
     frequency: str = "daily"
     lookback: int = 20
     direction: str = "higher_is_better"  # | lower_is_better
@@ -82,14 +83,26 @@ def _rolling_vol(prices: pd.DataFrame, lookback: int) -> pd.DataFrame:
 
 
 @register(
-    FactorDef("momentum_20", "过去 20 个交易日收益率", "close / close.shift(20) - 1", lookback=20)
+    FactorDef(
+        "momentum_20",
+        "过去 20 个交易日收益率",
+        "close / close.shift(20) - 1",
+        brief="短期动量：近一个月强势股延续性较强，适合趋势延续环境（牛市中段）；震荡市易追高。",
+        lookback=20,
+    )
 )
 def _momentum_20(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
     return _rolling_return(fields["close"], 20)
 
 
 @register(
-    FactorDef("momentum_60", "过去 60 个交易日收益率", "close / close.shift(60) - 1", lookback=60)
+    FactorDef(
+        "momentum_60",
+        "过去 60 个交易日收益率",
+        "close / close.shift(60) - 1",
+        brief="中期动量：A 股常见有效时段（约 1~3 个月），趋势行情首选；需结合市场阶段判断方向。",
+        lookback=60,
+    )
 )
 def _momentum_60(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
     return _rolling_return(fields["close"], 60)
@@ -97,7 +110,11 @@ def _momentum_60(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
 
 @register(
     FactorDef(
-        "momentum_120", "过去 120 个交易日收益率", "close / close.shift(120) - 1", lookback=120
+        "momentum_120",
+        "过去 120 个交易日收益率",
+        "close / close.shift(120) - 1",
+        brief="长期动量：反映近半年强势，适合大级别趋势；换手慢、回撤修复慢，弱市慎用。",
+        lookback=120,
     )
 )
 def _momentum_120(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
@@ -109,6 +126,7 @@ def _momentum_120(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
         "volatility_20",
         "过去 20 个交易日收益率波动率",
         "std(pct_change, 20)",
+        brief="低波防御（方向 lower_is_better）：近月波动小的股票抗跌，弱市/熊市阶段相对占优。",
         lookback=20,
         direction="lower_is_better",
     )
@@ -122,6 +140,7 @@ def _volatility_20(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
         "volatility_60",
         "过去 60 个交易日收益率波动率",
         "std(pct_change, 60)",
+        brief="低波动（方向 lower_is_better）：近一季低波组合长期回测常有超额，是防御型核心因子。",
         lookback=60,
         direction="lower_is_better",
     )
@@ -135,6 +154,7 @@ def _volatility_60(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
         "close_to_high_60",
         "收盘价相对 60 日最高价的接近程度",
         "close / rolling_max(high, 60)",
+        brief="贴近 60 日高点（接近新高）：趋势确认型强势股，常与动量互补；需配合市场热度判断。",
         lookback=60,
         requires=("close", "high"),
     )
@@ -149,6 +169,7 @@ def _close_to_high_60(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
         "volume_ratio_5_60",
         "量比：5 日均量 / 60 日均量",
         "mean(volume, 5) / mean(volume, 60)",
+        brief="量比放大提示资金关注（短线活跃型）；高换手也伴随更高波动，注意与波动因子搭配。",
         lookback=60,
         requires=("volume",),
     )
@@ -163,6 +184,7 @@ def _volume_ratio_5_60(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
         "ma_bias_20",
         "20 日均线乖离率",
         "(close - ma(close, 20)) / ma(close, 20)",
+        brief="20 日均线乖离：上行趋势中正乖离偏强；乖离过大易回落，需警惕过热。",
         lookback=20,
     )
 )
@@ -177,6 +199,7 @@ def _ma_bias_20(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
         "reversal_5",
         "短期反转：过去 5 日收益率取负（越低越接近超跌）",
         "-1 * (close / close.shift(5) - 1)",
+        brief="短期反转（方向 higher_is_better）：前期跌幅大的超跌反弹机会，适合震荡/修复行情。",
         lookback=5,
     )
 )