macd 本地计算测试

bk_test.py

@@ -36,3 +36,58 @@ for key,value in num_industry.items():
 
 
 print(set(a))
+
+
+
+
+from jqdatasdk import *
+from datetime import datetime as dt
+import pandas as pd
+import pymysql
+from sqlalchemy import create_engine
+import numpy as np
+from jqdatasdk.technical_analysis import *
+
+auth('18616891214', 'Ea?*7f68nD.dafcW34d!')
+
+
+
+def calculateEMA(period, closeArray, emaArray=[]):
+    """计算指数移动平均"""
+    length = len(closeArray)
+    nanCounter = np.count_nonzero(np.isnan(closeArray))
+    if not emaArray:
+        emaArray.extend(np.tile([np.nan], (nanCounter + period - 1)))
+        firstema = np.mean(closeArray[nanCounter:nanCounter + period - 1])
+        emaArray.append(firstema)
+        for i in range(nanCounter + period, length):
+            ema = (2 * closeArray[i] + (period - 1) * emaArray[-1]) / (period + 1)
+            emaArray.append(ema)
+    return np.array(emaArray)
+
+
+def calculateMACD(closeArray, shortPeriod=12, longPeriod=26, signalPeriod=9):
+    ema12 = calculateEMA(shortPeriod, closeArray, [])
+    print(ema12)
+    ema26 = calculateEMA(longPeriod, closeArray, [])
+    print(ema26)
+    diff = ema12 - ema26
+
+    dea = calculateEMA(signalPeriod, diff, [])
+    macd = 2 * (diff - dea)
+    return macd, diff, dea
+
+stock = '000010.XSHE'
+fre = '1d'
+engine_stock = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks?charset=utf8')
+df = pd.read_sql_query('select date,open,close,high,low,volume,money from `stk%s_%s`' % (stock, fre), engine_stock)
+df_close = df['close']
+df2 = calculateMACD(df_close)
+print(df2)
+print(len(df), len(df2[0]))
+df3 = pd.concat([df, pd.Series(df2[0]).rename('macd'), pd.Series(df2[1]).rename('diff'), pd.Series(df2[2]).rename('dea')], axis=1)
+
+print(df3.loc[df.date== '2010-02-25',:])
+
+x_macd_dif, x_macd_dea, x_macd_macd = MACD(stock, check_date='2010-02-26 00:00:00', SHORT=12, LONG=26, MID=9, unit=fre)
+print(x_macd_macd, x_macd_dif, x_macd_dea)