stock/QMT/get_macd.py

# coding:utf-8
from datetime import datetime as dt
import os
import pandas as pd
import time
from sqlalchemy import create_engine
from jqdatasdk import *
import pymysql
import multiprocessing as mp
import math
import talib as ta

# pd.set_option('display.max_columns', None)  # 设置显示最大行


def err_call_back(err):
    print(f'出错啦~ error：{str(err)}')


def myself_kdj(df):
    low_list = df['low'].rolling(9, min_periods=9).min()
    low_list.fillna(value=df['low'].expanding().min(), inplace=True)
    high_list = df['high'].rolling(9, min_periods=9).max()
    high_list.fillna(value=df['high'].expanding().max(), inplace=True)
    rsv = (df['close'] - low_list) / (high_list - low_list) * 100
    df['k'] = pd.DataFrame(rsv).ewm(com=2).mean()
    df['d'] = df['k'].ewm(com=2).mean()
    df['j'] = 3 * df['k'] - 2 * df['d']
    return df


# macd指标
def get_macd_data(data, short=0, long1=0, mid=0):
    if short == 0:
        short = 12
    if long1 == 0:
        long1 = 26
    if mid == 0:
        mid = 9
    data['sema'] = pd.Series(data['close']).ewm(span=short).mean()
    data['lema'] = pd.Series(data['close']).ewm(span=long1).mean()
    data.fillna(0, inplace=True)
    data['dif'] = data['sema'] - data['lema']
    data['dea'] = pd.Series(data['dif']).ewm(span=mid).mean()
    data['macd'] = 2 * (data['dif'] - data['dea'])
    data.fillna(0, inplace=True)
    return data[['dif', 'dea', 'macd']]


# rsi指标
# 建议用talib库的RSI方法，亲测有用
def get_ris(data):
    data["rsi_6"] = ta.RSI(data['close'], timeperiod=6)
    data["rsi_12"] = ta.RSI(data['close'], timeperiod=12)
    data["rsi_24"] = ta.RSI(data['close'], timeperiod=24)


def get_bias(df):
    # 计算方法：
    # bias指标
    # N期BIAS=(当日收盘价-N期平均收盘价)/N期平均收盘价*100%
    df['bias_6'] = (df['close'] - df['close'].rolling(6, min_periods=1).mean()) / \
                    df['close'].rolling(6, min_periods=1).mean() * 100
    df['bias_12'] = (df['close'] - df['close'].rolling(12, min_periods=1).mean()) / \
                     df['close'].rolling(12, min_periods=1).mean() * 100
    df['bias_24'] = (df['close'] - df['close'].rolling(24, min_periods=1).mean()) / \
                     df['close'].rolling(24, min_periods=1).mean() * 100
    df['bias_6'] = round(df['bias_6'], 2)
    df['bias_12'] = round(df['bias_12'], 2)
    df['bias_24'] = round(df['bias_24'], 2)


def get_wilr(df):
    # 威廉指标
    # 建议用talib库的WILLR方法，亲测有用
    df['willr'] = ta.WILLR(df['high'], df['low'], df['close'], timeperiod=14)


def tech_anal(datas):
    engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks?charset=utf8')
    engine_tech = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qmt_stocks_tech?charset=utf8')
    for stock in datas:
        stock = stock.replace('XSHG', 'SH').replace('XSHE', 'SZ')
        df = pd.read_sql_table('%s_1d' % stock, con=engine)
        get_macd_data(df)
        get_ris(df)
        get_bias(df)
        get_wilr(df)
        df = df.reset_index(drop=True)
        df.to_sql('%s_1d' % stock, con=engine_tech, index=False, if_exists='replace')
        # with engine.connect() as con:
        #     con.execute("ALTER TABLE `%s_1d` ADD PRIMARY KEY (`time`);" % stock)
        print(stock, df)


if __name__ == '__main__':
    sttime = dt.now()

    engine_hlfx_pool = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')
    stock_pool = pd.read_sql_query('select securities from stocks_list', engine_hlfx_pool)
    stock_pool = stock_pool.iloc[-1, 0].split(",")
    stock_pool.sort()
    print(len(stock_pool))

    pool = mp.Pool(processes=mp.cpu_count())
    # step = math.ceil(len(stock_pool) / mp.cpu_count())
    step = 100

    for i in range(0, len(stock_pool), step):
        pool.apply_async(func=tech_anal, args=(stock_pool[i:i + step],), error_callback=err_call_back)

    pool.close()
    pool.join()
    edtime = dt.now()

    print(edtime - sttime)