stock/z_jq_his/bk_test.py

from jqdatasdk import *
from datetime import datetime as dt
import pandas as pd
import pymysql
from sqlalchemy import create_engine
import time

auth('18616891214', 'Ea?*7f68nD.dafcW34d!')

# fre = ['30m', '1d']

engine_hlfx_pool = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')
stock_pool = pd.read_sql_query(
                    'select value from MA5_1d', engine_hlfx_pool)
stock_pool = stock_pool.iloc[-2, 0].split(",")
print(type(stock_pool), len(stock_pool),stock_pool)
num_industry = get_industry(stock_pool)
print(num_industry)
results = []
a = []
for key in num_industry.values():
    for key2 in key.values():
        results.append(key2['industry_name'])
results = pd.value_counts(results)
print(results)

results = results[0:3]
results = list(results.index)
print(results)

for key,value in num_industry.items():
    for key2 in value.values():
        if key2['industry_name'] in results:
            a.append(key)



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 emaArray:
        # emaArray.extend(np.tile([np.nan], (nanCounter + period - 1)))
        # firstema = np.mean(closeArray[nanCounter:nanCounter + period - 1])
        firstema = emaArray[-1]
        emaArray.append(firstema)
        for i in range(nanCounter, length):
            ema = (2 * closeArray[i] + (period - 1) * emaArray[-1]) / (period + 1)
            emaArray.append(ema)
    return np.array(emaArray)


def calculateMACD(emaArray, closeArray, shortPeriod=12, longPeriod=26, signalPeriod=9):
    ema12 = calculateEMA(shortPeriod, closeArray, emaArray)
    print(ema12)
    ema26 = calculateEMA(longPeriod, closeArray, emaArray)
    print(ema26)
    diff = ema12 - ema26

    dea = calculateEMA(signalPeriod, diff, [])
    macd = 2 * (diff - dea)
    return macd, diff, dea

stock = '000010.XSHE'
fre = '1d'
emaArray = []
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']
emaArray.append = EMA(stock, check_date='2010-01-04', timeperiod=fre)[stock]
df2 = calculateMACD(emaArray, 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)