本文结合tgw与talib，高性能计算指标，举出三种例子：
输入为一个时间序列，输出为多个时间序列；
输入为一个时间序列，输出为一个时间序列；
输入为多个时间序列，输出为多个时间序列；
计算复权数据
用前复权因子乘以开高低收成交量，做前复权处理。
import talib
import pandas as pd
import numpy as np
import tgw
from AmazingQuant.data_center.tgw_source.tgw_login import tgw_login
from AmazingQuant.data_center.tgw_source.tgw_api import TgwApiData
from AmazingQuant.data_center.api_data.get_data import get_local_data
from AmazingQuant.config.local_data_path import LocalDataPath
from AmazingQuant.constant import LocalDataFolderName, AdjustmentFactor
from AmazingQuant.utils.performance_test import Timer
class CalIndicator(object):
def __init__(self, open_df, high_df, low_df, close_df, volume_trade_df, value_trade_df, forward_factor):
self.open_df = open_df
self.high_df = high_df
self.low_df = low_df
self.close_df = close_df
self.volume_trade_df = volume_trade_df
self.value_trade_df = value_trade_df
self.forward_factor = forward_factor
def adj_data(self):
"""
open_df, high_df, low_df, close_df, volume_trade_df，前复权
value_trade_df 无需复权
"""
self.open_df = self.open_df * self.forward_factor
self.high_df = self.high_df * self.forward_factor
self.low_df = self.low_df * self.forward_factor
self.close_df = self.close_df * self.forward_factor
self.volume_trade_df = self.volume_trade_df * self.forward_factor输入输出一对多
注意使用apply对dataframe进行处理，输出多个结果，并解析，速度比循环计算快很多；
def cal_macd(self, fastperiod=12, slowperiod=26, signalperiod=9):
"""
输入为一个时间序列
输出为多个时间序列
"""
def macd(x):
macd, macdsignal, macdhist = talib.MACD(x, fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod)
return [macd, macdsignal, macdhist]
result = self.close_df.apply(lambda x: macd(x), result_type="expand")
return pd.DataFrame(result.loc[0].T.to_dict()), pd.DataFrame(result.loc[1].T.to_dict()),
pd.DataFrame(result.loc[2].T.to_dict()).multiply(2)输入输出一对一
def cal_ema(self, timeperiod=30):
"""
输入为一个时间序列
输出为一个时间序列
"""
return self.close_df.apply(lambda x: talib.EMA(x, timeperiod=timeperiod))输入输出多对多
同样使用apply对dataframe进行处理，输出多个结果，并解析，速度比循环计算快很多；
但注意，只能对输入的某一个时间序列做apply，取其他几个输入时间序列；
def cal_kdj(self, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0):
"""
输入为多个时间序列
输出为多个时间序列
"""
def kdj(x):
slowk, slowd = talib.STOCH(self.high_df[x.name], self.low_df[x.name], x,
fastk_period=fastk_period, slowk_period=slowk_period, slowk_matype=slowk_matype,
slowd_period=slowd_period, slowd_matype=slowd_matype)
slowj = 3 * slowk - 2 * slowd
return [slowk, slowd, slowj]
result = self.close_df.apply(lambda x: kdj(x), result_type="expand")
return pd.DataFrame(result.loc[0].T.to_dict()), pd.DataFrame(result.loc[1].T.to_dict()),
pd.DataFrame(result.loc[2].T.to_dict())
计算性能测试
10年历史日线数据，10日股票，
可使用分布式任务计算，按照不同指标分别计算，满足盘中实时计算的要求。
if __name__ == "__main__":
tgw_login()
tgw_api_object = TgwApiData(20991231)
code_sh_list, code_sz_list = tgw_api_object.get_code_list()
calendar_index = tgw_api_object.get_calendar()
path = LocalDataPath.path + LocalDataFolderName.MARKET_DATA.value + "//" + LocalDataFolderName.KLINE_DAILY.value +
"//" + LocalDataFolderName.A_SHARE.value + "//"
open_df = get_local_data(path, "open_price.h5")
high_df = get_local_data(path, "high_price.h5")
low_df = get_local_data(path, "low_price.h5")
close_df = get_local_data(path, "close_price.h5")
volume_trade_df = get_local_data(path, "volume_trade.h5")
value_trade_df = get_local_data(path, "value_trade.h5")
adj_factor_path = LocalDataPath.path + LocalDataFolderName.ADJ_FACTOR.value + "/"
forward_factor = get_local_data(adj_factor_path, AdjustmentFactor.FROWARD_ADJ_FACTOR.value + ".h5")
cal_indicator_object = CalIndicator(open_df, high_df, low_df, close_df, volume_trade_df, value_trade_df,
forward_factor)
cal_indicator_object.adj_data()
with Timer(True):
dif, dea, macd = cal_indicator_object.cal_macd()
ema = cal_indicator_object.cal_ema()
k, d, j = cal_indicator_object.cal_kdj()
%timeit cal_indicator_object.cal_macd()
6.61 ms ± 88 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit cal_indicator_object.cal_ema()
1.77 ms ± 46.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit cal_indicator_object.cal_kdj()
12.8 ms ± 6.28 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
文章为作者独立观点，不代表股票自动交易程序化数据接口观点
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