HandleData

from fxdayu_data import DataAPI
from datetime import datetime

Map()方法

codes = map(
    lambda code: code+'.XSHE' if code.startswith('0') else code+ '.XSHG',
    ['000001', '601318', '600029', '000089', '000402', '000895', '600016', '000858', '600036', '600050']
)
codes

设置config.py的位置

DataAPI.set_file("config.py")

从DataAPI获取日线

candle = DataAPI.candle(tuple(codes), 'D', start=datetime(2016, 1, 1), end=datetime(2016, 12, 31))
candle['000001.XSHE'].tail()

计算一只股票的atr

import talib as ta
ta.abstract.ATR(candle['000001.XSHE'], timeperiod=10).tail()

使用iteritem()遍历所有股票的K线传入一个方法计算并返回atr

def atr_10(item):
    name, data = item
    return name, ta.abstract.ATR(data, timeperiod=10)
for item in candle.iteritems():
    print atr_10(item)

用map()简化代码并生成字典{code： atr}

atrs = dict(map(atr_10, candle.iteritems()))
atrs

将字典生成DataFrame

import pandas as pd
atrs = pd.DataFrame(atrs)
atrs.tail()

计算日线数据的MACD值

计算一只股票的MACD

ta.abstract.MACD(candle['000001.XSHE']).tail()

生成{code： MACD}格式的字典(与上面方法一样)

MACDs = {name: ta.abstract.MACD(value) for name, value in candle.iteritems()}

生成panel

indicators = pd.Panel.from_dict(MACDs)

将atr插入indicators，获得一个Panel(包含MACD的三个指标和atr)

indicators.loc[:, :, 'atr'] = atrs
indicators

使用stack()方法将DataFrame转换成MultiIndexSeries

indicators.minor_xs("atr").stack()

将panel转换成MultiIndexDataFrame展示

pd.DataFrame(
    {name: indicators.minor_xs(name).stack() for name in indicators.minor_axis}
).tail(20)