#-*- coding: utf-8 -*-#@Author: fangbei#@Date: 2017-08-26#@Original:

price_str= '30.14, 29.58, 26.36, 32.56, 32.82'price_str= price_str.replace(' ', '') #删除空格

price_array = price_str.split(',') #转成数组

date_array=[]

date_base= 20170118

'''# for 循环

for _ in range(0, len(price_array)):

date_array.append(str(date_base))

date_base += 1'''

推导式comprehensions(又称解析式)，是Python的一种独有特性。推导式是可以从一个数据序列构建另一个新的数据序列的结构体。

列表推导式

date_array = [str(date_base + ind) for ind, _ inenumerate(price_array)]print(date_array)#['20170118', '20170119', '20170120', '20170121', '20170122']

zip函数

stock_tuple_list = [(date, price) for date, price inzip(date_array, price_array)]print(stock_tuple_list)#[('20170118', '30.14'), ('20170119', '29.58'), ('20170120', '26.36'), ('20170121', '32.56'), ('20170122', '32.82')]

字典推导式

stock_dict = {date: price for date, price inzip(date_array, price_array)}print(stock_dict)#{'20170118': '30.14', '20170119': '29.58', '20170120': '26.36', '20170121': '32.56', '20170122': '32.82'}

可命名元组 namedtuple

from collections importnamedtuple

stock_nametuple= namedtuple('stock', ('date', 'price'))

stock_nametuple_list= [stock_nametuple(date, price) for date, price inzip(date_array, price_array)]print(stock_nametuple_list)#[stock(date='20170118', price='30.14'), stock(date='20170119', price='29.58'), stock(date='20170120', price='26.36'), stock(date='20170121', price='32.56'), stock(date='20170122', price='32.82')]

有序字典 OrderedDict

from collections importOrderedDict

stock_dict= OrderedDict((date, price) for date, price inzip(date_array, price_array))print(stock_dict.keys())#odict_keys(['20170118', '20170119', '20170120', '20170121', '20170122'])

最小收盘价

print(min(zip(stock_dict.values(), stock_dict.keys())))#('26.36', '20170120')

lambad函数

func = lambda x:x+1

以上lambda等同于以下函数

deffunc(x):return(x+1)

找出收盘价中第二大的价格

find_second_max_lambda = lambda dict_array : sorted(zip(dict_array.values(), dict_array.keys()))[-2]print(find_second_max_lambda(stock_dict))#('32.56', '20170121')

高阶函数

将相邻的收盘价格组成tuple后装入list

price_float_array = [float(price_str) for price_str instock_dict.values()]

pp_array= [(price1, price2) for price1, price2 in zip(price_float_array[:-1], price_float_array[1:])]print(pp_array)#[(30.14, 29.58), (29.58, 26.36), (26.36, 32.56), (32.56, 32.82)]

from functools importreduce#外层使用map函数针对pp_array()的每一个元素执行操作，内层使用reduce()函数即两个相邻的价格， 求出涨跌幅度，返回外层结果list

change_array = list(map(lambda pp:reduce(lambda a,b: round((b-a) / a, 3),pp), pp_array))#print(type(change_array))

change_array.insert(0,0)print(change_array)#[0, -0.019, -0.109, 0.235, 0.008]

#将涨跌幅数据加入OrderedDict，配合使用namedtuple重新构建数据结构stock_dict

stock_nametuple = namedtuple('stock', ('date', 'price', 'change'))

stock_dict=OrderedDict((date, stock_nametuple(date, price, change))for date, price, change inzip(date_array, price_array, change_array))print(stock_dict)#OrderedDict([('20170118', stock(date='20170118', price='30.14', change=0)), ('20170119', stock(date='20170119', price='29.58', change=-0.019)), ('20170120', stock(date='20170120', price='26.36', change=-0.109)), ('20170121', stock(date='20170121', price='32.56', change=0.235)), ('20170122', stock(date='20170122', price='32.82', change=0.008))])#用filter()进行筛选，选出上涨的交易日

up_days = list(filter(lambda day: day.change >0, stock_dict.values()))print(up_days)#[stock(date='20170121', price='32.56', change=0.235), stock(date='20170122', price='32.82', change=0.008)]

#定义函数计算涨跌日或涨跌值

def filter_stock(stock_array_dict, want_up=True, want_calc_sum=False):if notisinstance(stock_array_dict, OrderedDict):raise TypeError('stock_array_dict must be OrderedDict')

filter_func= (lambda day: day.change > 0) if want_up else (lambda day: day.change <0)

want_days=filter(filter_func, stock_array_dict.values())if notwant_calc_sum:returnwant_days

change_sum= 0.0

for day inwant_days:

change_sum+=day.changereturn change_sum

偏函数 partial

from functools importpartial

filter_stock_up_days= partial(filter_stock, want_up=True, want_calc_sum=False)#print(type(filter_stock_up_days))

filter_stock_down_days = partial(filter_stock, want_up=False, want_calc_sum=False)

filter_stock_up_sums= partial(filter_stock, want_up=True, want_calc_sum=True)

filter_stock_down_sums= partial(filter_stock, want_up=False, want_calc_sum=True)print('所有上涨的交易日：{}'.format(list(filter_stock_up_days(stock_dict))))print('所有下跌的交易日：{}'.format(list(filter_stock_down_days(stock_dict))))print('所有上涨交易日的涨幅和：{}'.format(filter_stock_up_sums(stock_dict)))print('所有下跌交易日的跌幅和：{}'.format(filter_stock_down_sums(stock_dict)))#所有上涨的交易日：[stock(date='20170121', price='32.56', change=0.235), stock(date='20170122', price='32.82', change=0.008)]#所有下跌的交易日：[stock(date='20170119', price='29.58', change=-0.019), stock(date='20170120', price='26.36', change=-0.109)]#所有上涨交易日的涨幅和：0.243#所有下跌交易日的跌幅和：-0.128