使用cci、kdj和站上5日线为买入标志

util.py

@@ -1,5 +1,6 @@
 import requests
 import pandas as pd
+import numpy as np
 
 CN_CODE_TYPE = {
     "600":'sh',
@@ -18,7 +19,7 @@ CN_CODE_TYPE = {
     "18":"sz"
     }
 
-
+## arbr指标
 def calculate_brar(df: pd.DataFrame, N: int = 26) -> pd.DataFrame:
     """
     计算AR和BR情绪指标
@@ -58,7 +59,37 @@ def calculate_brar(df: pd.DataFrame, N: int = 26) -> pd.DataFrame:
 
     return data
 
+## kdj计算
+def calc_kdj_tdx(df, n=9, m1=3, m2=3):
+    # 1. 计算 RSV
+    low_min = df['low'].rolling(n).min()
+    high_max = df['high'].rolling(n).max()
+    rsv = (df['close'] - low_min) / (high_max - low_min) * 100
+    rsv = rsv.fillna(0)  # 或者处理 NaN
 
+    # 2. 自定义 SMA 函数（通达信风格）
+    def sma_tdx(series, period, weight):
+        # period=N, weight=M。公式：Y = (X*M + PREV_Y*(N-M))/N
+        result = np.zeros(len(series))
+        # 初始化第一个有效值。通常，如果数据足够，初始值为 series[0]
+        result[0] = series[0] if not np.isnan(series[0]) else 0
+        for i in range(1, len(series)):
+            # 如果 series[i] 是 NaN（例如，前 n 天），则保持 0 或向前填充，但通常 RSV 已填充。
+            val = series[i] if not np.isnan(series[i]) else 0
+            result[i] = (val * weight + result[i - 1] * (period - weight)) / period
+        return result
+
+    # 3. 计算 K 和 D
+    k_series = sma_tdx(rsv.values, m1, 1)  # K = SMA(RSV, 3, 1)
+    d_series = sma_tdx(k_series, m2, 1)  # D = SMA(K, 3, 1)
+
+    df['K'] = k_series
+    df['D'] = d_series
+    df['J'] = 3 * df['K'] - 2 * df['D']
+
+    return df
+
+## 获取历史k线
 def get_history_k(code, start_date='19700101', end_date=None):
 
     if '.' in code:
@@ -93,6 +124,7 @@ def get_history_k(code, start_date='19700101', end_date=None):
     return df
 
 
+## 获取当日5分钟的k线
 def get_5min_k(code, start_date='19700101', end_date=None):
     stock_code = ''