test+fix: 自动化测试18条全过 + 量比基准排除当日 + 截面打分最低有效因子数修正

tests/helpers.py

@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+"""合成K线数据生成器：供测试使用（无网络依赖）"""
+import numpy as np
+import pandas as pd
+
+
+def gen_kline(n=120, base=10.0, trend=0.001, vol_pct=0.02, seed=42):
+    """
+    生成合成日K线。
+    trend: 每日平均漂移（如 0.001 = +0.1%/天）
+    vol_pct: 每日随机波动幅度
+    返回 DataFrame: [bar_time, open, high, low, close, volume, amount]
+    """
+    rng = np.random.RandomState(seed)
+    dates = pd.date_range('2025-01-01', periods=n, freq='B')
+    close = np.zeros(n)
+    close[0] = base
+    for i in range(1, n):
+        close[i] = close[i-1] * (1 + trend + rng.randn() * vol_pct)
+    open_ = close * (1 + rng.randn(n) * vol_pct * 0.3)
+    high = np.maximum(open_, close) * (1 + abs(rng.randn(n)) * vol_pct * 0.3)
+    low = np.minimum(open_, close) * (1 - abs(rng.randn(n)) * vol_pct * 0.3)
+    volume = np.abs(rng.randn(n)) * 1e6 + 5e5
+    amount = volume * close
+    return pd.DataFrame({
+        'bar_time': dates.strftime('%Y-%m-%d'),
+        'open': open_, 'high': high, 'low': low,
+        'close': close, 'volume': volume,
+        'amount': amount,
+    })
+
+
+def gen_trending_up(n=80, base=10.0):
+    """持续上涨趋势K线"""
+    return gen_kline(n=n, base=base, trend=0.008, vol_pct=0.01, seed=7)
+
+
+def gen_trending_down(n=80, base=50.0):
+    """持续下跌趋势K线"""
+    return gen_kline(n=n, base=base, trend=-0.008, vol_pct=0.01, seed=7)