feat(platform): 因子平台与因子↔策略双向联动 v0.2.3

- 因子平台: /factors 一级页(检验/因子库/编辑器/组合/挖掘), DSL 公式因子(25 算子点选、双语字段、我的因子模板、脏公式守卫), 版本与生命周期, 自动挖掘 L1 统计筛选
- 因子↔策略四条桥: 触发器 Zap 快建因子条件信号、因子一键生成排名策略、自定义信号 AI 提示词接入因子分组、策略回测因子归因(胜/败单入场信号日因子均值, 独立 tab, 双语因子名)
- 回测: 统计卡新增盈亏比(≥1 红/<1 绿), 蒙卡回撤合并为中位/95% 双值卡(自适应字号), 高级设置基础过滤与策略编辑器参数对齐(5 组区间)
- 信号库独立页 /signals(原设置 tab 迁出), 持仓提醒入导航; 挖掘并入因子页第 5 tab, /mining 旧链接重定向
- 研究线配套: 因子目录 61→77(评分/矩阵双内核), stats_v2(Newey-West/BH-FDR/DSR), enriched 管道与异动/报价服务配套调整
- 文档: README 导航与特性表、features.md 因子平台章节、操作说明书 9.2、factor-platform-plan 执行状态与 §5、二开文档桥接说明; 交流与支持节改版
- 版本 0.2.2 → 0.2.3; 后端全量 1625 passed(1 例环境性跳过), 前端 build 通过

backend/app/backtest/factor.py

@@ -17,12 +17,14 @@ from typing import Any, Literal
 import numpy as np
 import polars as pl
 
+from app.backtest import stats_v2
 from app.backtest.engine import BacktestEngine
 from app.backtest.fundamentals import (
     FUNDAMENTAL_FACTOR_NAMES,
     attach_fundamental_factors,
     load_fundamental_snapshot,
 )
+from app.factors.registry import factor_columns_view as _factor_columns_view
 from app.strategy.scoring import (
     VIRTUAL_SCORING_DEPENDENCIES as DERIVED_FACTOR_DEPENDENCIES,
 )
@@ -33,80 +35,8 @@ from app.strategy.scoring import (
 logger = logging.getLogger(__name__)
 
 # 可研究因子目录。保留历史 ID 兼容已有候选方案; 价格尺度相关指标优先提供归一化版本。
-FACTOR_COLUMNS: list[dict] = [
-    {"id": "momentum_5d", "label": "5日动量", "group": "动量", "desc": "5个交易日累计收益率"},
-    {"id": "momentum_10d", "label": "10日动量", "group": "动量", "desc": "10个交易日累计收益率"},
-    {"id": "momentum_20d", "label": "20日动量", "group": "动量", "desc": "20个交易日累计收益率"},
-    {"id": "momentum_30d", "label": "30日动量", "group": "动量", "desc": "30个交易日累计收益率"},
-    {"id": "momentum_60d", "label": "60日动量", "group": "动量", "desc": "60个交易日累计收益率"},
-    {"id": "change_pct", "label": "日涨跌幅", "group": "动量", "desc": "当日收盘相对前收盘的收益率"},
-
-    {"id": "ma5_bias", "label": "MA5乖离", "group": "均线偏离", "desc": "收盘价 / MA5 - 1"},
-    {"id": "ma10_bias", "label": "MA10乖离", "group": "均线偏离", "desc": "收盘价 / MA10 - 1"},
-    {"id": "ma20_bias", "label": "MA20乖离", "group": "均线偏离", "desc": "收盘价 / MA20 - 1"},
-    {"id": "ma30_bias", "label": "MA30乖离", "group": "均线偏离", "desc": "收盘价 / MA30 - 1"},
-    {"id": "ma60_bias", "label": "MA60乖离", "group": "均线偏离", "desc": "收盘价 / MA60 - 1"},
-    {"id": "ema5_bias", "label": "EMA5乖离", "group": "均线偏离", "desc": "收盘价 / EMA5 - 1"},
-    {"id": "ema10_bias", "label": "EMA10乖离", "group": "均线偏离", "desc": "收盘价 / EMA10 - 1"},
-    {"id": "ema20_bias", "label": "EMA20乖离", "group": "均线偏离", "desc": "收盘价 / EMA20 - 1"},
-    {"id": "ema30_bias", "label": "EMA30乖离", "group": "均线偏离", "desc": "收盘价 / EMA30 - 1"},
-    {"id": "ema60_bias", "label": "EMA60乖离", "group": "均线偏离", "desc": "收盘价 / EMA60 - 1"},
-
-    {"id": "rsi_6", "label": "RSI(6)", "group": "超买超卖", "desc": "6日相对强弱指标"},
-    {"id": "rsi_14", "label": "RSI(14)", "group": "超买超卖", "desc": "14日相对强弱指标"},
-    {"id": "rsi_24", "label": "RSI(24)", "group": "超买超卖", "desc": "24日相对强弱指标"},
-
-    {"id": "macd_hist", "label": "MACD柱(原值)", "group": "趋势", "desc": "兼容历史研究; 跨股票比较建议优先使用MACD柱强度"},
-    {"id": "macd_dif_pct", "label": "MACD DIF强度", "group": "趋势", "desc": "MACD DIF / 收盘价"},
-    {"id": "macd_dea_pct", "label": "MACD DEA强度", "group": "趋势", "desc": "MACD DEA / 收盘价"},
-    {"id": "macd_hist_pct", "label": "MACD柱强度", "group": "趋势", "desc": "MACD柱 / 收盘价, 消除股价尺度影响"},
-    {"id": "kdj_k", "label": "KDJ-K", "group": "趋势", "desc": "KDJ指标K值"},
-    {"id": "kdj_d", "label": "KDJ-D", "group": "趋势", "desc": "KDJ指标D值"},
-    {"id": "kdj_j", "label": "KDJ-J", "group": "趋势", "desc": "KDJ指标J值"},
-    {"id": "boll_position", "label": "布林位置", "group": "趋势", "desc": "收盘价在布林带下轨到上轨之间的位置"},
-
-    {"id": "annual_vol_20d", "label": "20日波动率", "group": "波动率", "desc": "20日收益率年化标准差"},
-    {"id": "atr_14", "label": "ATR(14)原值", "group": "波动率", "desc": "兼容历史研究; 跨股票比较建议优先使用ATR相对波动"},
-    {"id": "atr_pct", "label": "ATR相对波动", "group": "波动率", "desc": "ATR(14) / 收盘价"},
-    {"id": "amplitude", "label": "日振幅", "group": "波动率", "desc": "当日高低价差 / 前收盘价"},
-    {"id": "boll_width", "label": "布林带宽", "group": "波动率", "desc": "布林带上下轨宽度 / MA20"},
-
-    {"id": "vol_ratio_5d", "label": "5日量比", "group": "量价", "desc": "当日成交量 / 前5日平均成交量"},
-    {"id": "vol_ratio_10d", "label": "10日量比", "group": "量价", "desc": "当日成交量 / 前10日平均成交量"},
-    {"id": "vol_trend_5_10", "label": "成交量趋势", "group": "量价", "desc": "5日平均成交量 / 10日平均成交量 - 1"},
-    {"id": "turnover_rate", "label": "换手率", "group": "量价", "desc": "使用历史时点流通股本计算的当日换手率"},
-    {"id": "turnover_ratio_5d", "label": "换手率放大", "group": "量价", "desc": "当日换手率 / 前5日平均换手率 - 1"},
-    {"id": "log_amount", "label": "成交额对数", "group": "量价", "desc": "ln(成交额 + 1), 降低极端规模影响"},
-    {"id": "amount_ratio_5d", "label": "成交额放大", "group": "量价", "desc": "当日成交额 / 前5日平均成交额 - 1"},
-
-    {"id": "gap_return", "label": "开盘跳空", "group": "价格位置", "desc": "开盘价 / 前收盘价 - 1"},
-    {"id": "intraday_return", "label": "日内收益", "group": "价格位置", "desc": "收盘价 / 开盘价 - 1"},
-    {"id": "close_position", "label": "收盘位置", "group": "价格位置", "desc": "收盘价在当日最低价到最高价之间的位置"},
-    {"id": "distance_to_high_60d", "label": "距60日高点", "group": "价格位置", "desc": "收盘价 / 60日最高收盘价 - 1"},
-    {"id": "distance_from_low_60d", "label": "距60日低点", "group": "价格位置", "desc": "收盘价 / 60日最低收盘价 - 1"},
-    {"id": "vwap_bias", "label": "VWAP乖离", "group": "价格位置", "desc": "收盘价 / 当日成交均价 - 1, 成交均价 = 成交额 / (成交量x100)"},
-
-    {"id": "max_ret_20d", "label": "20日最大单日涨幅", "group": "收益形态", "desc": "近20个交易日单日涨幅最大值(彩票效应, 高值代表博彩型特征强)"},
-    {"id": "ret_skew_20d", "label": "20日收益偏度", "group": "收益形态", "desc": "近20个交易日日收益偏度, 高值代表右偏(偶发大涨)"},
-    {"id": "up_days_20d", "label": "20日上涨天数", "group": "收益形态", "desc": "近20个交易日中上涨天数(0~20)"},
-
-    {"id": "amihud_20d", "label": "20日Amihud非流动性", "group": "流动性", "desc": "近20日平均 |日涨跌幅| / 成交额(亿元), 高值代表流动性差"},
-    {"id": "turnover_z_60d", "label": "换手率60日z分", "group": "流动性", "desc": "(当日换手率 - 前60日均值) / 前60日标准差, 衡量换手异动"},
-
-    {"id": "vol_price_corr_20d", "label": "20日量价相关", "group": "量价", "desc": "近20个交易日日涨跌幅与成交量的相关系数, 高值代表量价同向"},
-    {"id": "vol_trend_5_60", "label": "量能趋势(5/60)", "group": "量价", "desc": "5日平均成交量 / 60日平均成交量 - 1"},
-
-    {"id": "limit_up_count_20d", "label": "涨停基因(20日)", "group": "涨停基因", "desc": "近20个交易日涨停次数"},
-    {"id": "limit_up_count_60d", "label": "涨停基因(60日)", "group": "涨停基因", "desc": "近60个交易日涨停次数"},
-
-    {"id": "pb_latest", "label": "市净率(最新公告)", "group": "财务", "desc": "收盘价 / 最新已公告每股净资产; 无财务数据或公告前为空"},
-    {"id": "roe_latest", "label": "ROE(最新公告)", "group": "财务", "desc": "最新已公告净资产收益率(%); 无财务数据或公告前为空"},
-    {"id": "gross_margin_latest", "label": "毛利率(最新公告)", "group": "财务", "desc": "最新已公告销售毛利率(%)"},
-    {"id": "net_margin_latest", "label": "净利率(最新公告)", "group": "财务", "desc": "最新已公告销售净利率(%)"},
-    {"id": "revenue_yoy_latest", "label": "营收增速(最新公告)", "group": "财务", "desc": "最新已公告营业收入同比(%)"},
-    {"id": "net_income_yoy_latest", "label": "净利增速(最新公告)", "group": "财务", "desc": "最新已公告归母净利润同比(%)"},
-    {"id": "debt_ratio_latest", "label": "资产负债率(最新公告)", "group": "财务", "desc": "最新已公告资产负债率(%)"},
-]
+# P1 起目录元数据单一权威来源为 app/factors/registry.py, 本常量为兼容别名 (顺序与键不变)。
+FACTOR_COLUMNS: list[dict] = _factor_columns_view()
 
 FACTOR_WARMUP_DAYS = 120
 FACTOR_METHODOLOGY_VERSION = "factor_v2"
@@ -208,6 +138,12 @@ class FactorBatchItem:
     yearly_ic: list[dict] = field(default_factory=list)
     ic_decay: list[dict] = field(default_factory=list)
     regime_stats: list[dict] = field(default_factory=list)
+    # metrics_v2 (P3): NW HAC t 值 (滞后=1, 日频 1 日前瞻) 与 BH-FDR q 值; 样本不足为 None
+    t_naive: float | None = None
+    t_newey_west: float | None = None
+    nw_lag: int | None = None
+    p_value: float | None = None
+    q_value: float | None = None
 
 
 @dataclass
@@ -357,6 +293,10 @@ class FactorBacktestService:
                     **evaluate_kwargs,
                 )
                 long_short = result.long_short_stats
+                # metrics_v2: 由 IC 序列推导 NW HAC t 值与 p 值 (日频 1 日前瞻 → 滞后 1)
+                ic_values = [row.get("ic") for row in result.ic_series]
+                t_naive = stats_v2.naive_t(ic_values)
+                nw = stats_v2.newey_west_t(ic_values, lag=1)
                 items.append(FactorBatchItem(
                     factor_name=factor_name,
                     label=str(meta.get("label", factor_name)),
@@ -377,6 +317,14 @@ class FactorBacktestService:
                     n_dates=result.n_dates,
                     elapsed_ms=result.elapsed_ms,
                     error=result.error,
+                    t_naive=t_naive,
+                    t_newey_west=nw[0] if nw else None,
+                    nw_lag=1 if nw else None,
+                    p_value=(
+                        stats_v2.normal_two_sided_p(nw[0]) if nw
+                        else stats_v2.normal_two_sided_p(t_naive) if t_naive is not None
+                        else None
+                    ),
                 ))
             except Exception as exc:  # 单因子失败不能中止整个筛选批次
                 logger.exception("factor batch item failed: %s", factor_name)
@@ -390,6 +338,10 @@ class FactorBacktestService:
 
         n_symbols = max((item.n_symbols for item in items), default=0)
         n_dates = max((item.n_dates for item in items), default=0)
+        # metrics_v2: 批内 BH-FDR q 值 (m = 可检验因子数, 计算失败项不计)
+        q_values = stats_v2.bh_fdr_qvalues([item.p_value for item in items])
+        for item, q_value in zip(items, q_values, strict=True):
+            item.q_value = q_value
         return FactorBatchResult(
             run_id=run_id,
             config=result_config,
@@ -701,8 +653,31 @@ class FactorBacktestService:
             logger.warning("factors %s cannot be computed, missing columns: %s", factor_cols, missing)
             return panel
 
+        from app.factors.registry import get_factor
         from app.indicators.pipeline import compute_indicators
 
+        # custom/composite 因子走注册表→DSL/组合 的同一条物化路径 (P3, 与策略评分共用);
+        # 其底层依赖 (如 change_pct/ma20) 先经内置补算路径物化, 再做 DSL/组合物化。
+        registry_names = {
+            name for name in factor_cols
+            if (spec := get_factor(name)) is not None and spec.kind in ("custom", "composite")
+        }
+        if registry_names:
+            base_deps: set[str] = set()
+            for name in registry_names:
+                spec = get_factor(name)
+                if spec is not None:
+                    base_deps.update(spec.dependencies)
+            base_deps -= set(panel.columns) | registry_names
+            if base_deps:
+                panel = FactorBacktestService._compute_missing_factors(
+                    panel, base_deps, assume_sorted=assume_sorted,
+                )
+            panel = materialize_scoring_columns(panel, registry_names)
+            factor_cols = factor_cols - registry_names
+            if not factor_cols:
+                return panel
+
         derived = factor_cols & set(DERIVED_FACTOR_DEPENDENCIES)
         indicator_columns = factor_cols - derived
         for factor_name in derived: