feat: 推荐原因丰富化——因子值→带数字/方向的中文描述

deploy/dbg_bucket.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""服务器调试：两级状态桶在 000993 上的真实分布"""
+import sys
+import warnings
+
+sys.path.insert(0, '/opt/a_stock_timeline')
+warnings.filterwarnings('ignore')
+
+from src.fetcher.kline_fetcher import KlineFetcher
+from src.quant.model import per_stock_expected, _bucket, rsi_series
+import numpy as np
+import pandas as pd
+
+DB = '/opt/a_stock_timeline/data/a_stock.db'
+kf = KlineFetcher(DB)
+k = kf.load('000993', 'day', 150)
+print('bars:', len(k))
+
+close = k['close'].reset_index(drop=True)
+ma20 = close.rolling(20).mean()
+rsi = rsi_series(close)
+
+for fine in (True, False):
+    cur = _bucket(close.iloc[-1], ma20.iloc[-1], rsi.iloc[-1], fine)
+    rets = []
+    horizon = 5
+    for t in range(30, len(close) - horizon):
+        b = _bucket(close.iloc[t], ma20.iloc[t], rsi.iloc[t], fine)
+        if b == cur:
+            rets.append(close.iloc[t + horizon] / close.iloc[t] - 1)
+    med = float(np.median(rets)) if rets else None
+    print('fine={} 桶={} 样本={} 中位={}'.format(fine, cur, len(rets), med))
+
+med, n, bucket = per_stock_expected(k)
+print('per_stock_expected:', med, n, bucket)