def filter_stock(self, want_up=True, want_calc_sum = False):
filter_func = (lambda p_day:p_day.change >0) if want_up else (lambda p_day: p_day.change<0)
want_days = list(filter(filter_func, self.stock_dict.values()))
if not want_calc_sum:
return want_days
change_sum = 0.0000
for day in want_days:
change_sum += day.change
return change_sum
def filter_stock(stock_array_dict, want_up=True, want_calc_sum=False):
if not isinstance(stock_array_dict, OrderedDict):
raise TypeError('stock_array_dict must be OrderedDict!')
# python中的三目表达式的写法
filter_func = (lambda day: day.change > 0) if want_up else (lambda day: day.change < 0)
# 使用filter_func做筛选函数
want_days = list(filter(filter_func, stock_array_dict.values()))
if not want_calc_sum:
return want_days
# 需要计算涨跌幅和
change_sum = 0.0
for day in want_days:
change_sum += day.change
return change_sum
def filter_stock(stock_array_dict, want_up=True, want_calc_sum=False):
if not isinstance(stock_array_dict, OrderedDict):
raise TypeError('stock_array_dict must be OrderedDict!')
# python中的三目表达式的写法
filter_func = (lambda p_day: p_day.change > 0) if want_up else (lambda p_day: p_day.change < 0)
# 使用filter_func做筛选函数
want_days = list(filter(filter_func, stock_array_dict.values()))
if not want_calc_sum:
return want_days
# 需要计算涨跌幅和
change_sum = 0.0
for day in want_days:
change_sum += day.change
return change_sum
def filter_stock(self, want_up=True, want_calc_sum=False):
"""
筛选结果子集
:param want_up: 是否筛选上涨
:param want_calc_sum: 是否计算涨跌和
:return:
"""
# Python中的三目表达式的写法
filter_func = (lambda p_day: p_day.change > 0) if want_up else (
lambda p_day: p_day.change < 0)
# 使用filter_func做筛选函数
want_days = list(filter(filter_func, self.stock_dict.values()))
if not want_calc_sum:
return want_days
# 需要计算涨跌幅和
change_sum = 0.0
for day in want_days:
change_sum += day.change
return change_sum
def filter_stock(self, want_up=True, want_calc_sum=False):
"""
筛选结果子集
:param want_up: 是否筛选上涨
:param want_calc_sum: 是否计算涨跌和
:return:
"""
# Python中的三目表达式的写法
filter_func = (lambda p_day: p_day.change > 0) if want_up else (
lambda p_day: p_day.change < 0)
# 使用filter_func做筛选函数
want_days = list(filter(filter_func, self.stock_dict.values()))
if not want_calc_sum:
return want_days
# 需要计算涨跌幅和
change_sum = 0.0
for day in want_days:
change_sum += day.change
return change_sum
def sample_223(show=True):
"""
2.2.3 高阶函数
:return:
"""
stock_dict = sample_212(show=False)
show_func = print if show else lambda a: a
# 将字符串的的价格通过列表推导式显示转换为float类型
# 由于stock_dict是OrderedDict所以才可以直接
# 使用stock_dict.values()获取有序日期的收盘价格
price_float_array = [float(price_str) for price_str in stock_dict.values()]
# 通过将时间平移形成两个错开的收盘价序列，通过zip打包成为一个新的序列，
# 通过[:-1]:从第0个到倒数第二个，[1:]：从第一个到最后一个 错开形成相邻
# 组成的序列每个元素为相邻的两个收盘价格
pp_array = [(price1, price2) for price1, price2 in zip(
price_float_array[:-1], price_float_array[1:])]
show_func(pp_array)
# list for python3
change_array = list(
map(lambda pp: reduce(lambda a, b: round((b - a) / a, 3), pp),
pp_array))
# list insert插入数据，将第一天的涨跌幅设置为0
change_array.insert(0, 0)
show_func(change_array)
price_str = '30.14, 29.58, 26.36, 32.56, 32.82'
price_str = price_str.replace(' ', '')
price_array = price_str.split(',')
date_base = 20170118
date_array = [str(date_base + ind) for ind, _ in enumerate(price_array)]
# 使用namedtuple重新构建数据结构
stock_namedtuple = namedtuple('stock', ('date', 'price', 'change'))
# 通过zip分别从date_array，price_array，change_array拿数据组成
# stock_namedtuple然后以date做为key组成OrderedDict
stock_dict = OrderedDict(
(date, stock_namedtuple(date, price, change))
for date, price, change in zip(date_array, price_array, change_array))
show_func(stock_dict)
# list for python3
up_days = list(filter(lambda day: day.change > 0, stock_dict.values()))
show_func(up_days)
def filter_stock(stock_array_dict, want_up=True, want_calc_sum=False):
if not isinstance(stock_array_dict, OrderedDict):
raise TypeError('stock_array_dict must be OrderedDict!')
# python中的三目表达式的写法
filter_func = (lambda p_day: p_day.change > 0) if want_up else (
lambda p_day: p_day.change < 0)
# 使用filter_func做筛选函数
want_days = list(filter(filter_func, stock_array_dict.values()))
if not want_calc_sum:
return want_days
# 需要计算涨跌幅和
change_sum = 0.0
for day in want_days:
change_sum += day.change
return change_sum
# 全部使用默认参数
show_func('所有上涨的交易日：{}'.format(filter_stock(stock_dict)))
# want_up=False
show_func('所有下跌的交易日：{}'.format(filter_stock(stock_dict, want_up=False)))
# 计算所有上涨的总会
show_func('所有上涨交易日的涨幅和：{}'.format(
filter_stock(stock_dict, want_calc_sum=True)))
# 计算所有下跌的总会
show_func('所有下跌交易日的跌幅和：{}'.format(
filter_stock(stock_dict, want_up=False, want_calc_sum=True)))
return stock_dict
# In[27]:
# 使用namedtuple重新构建数据结构
stock_namedtuple = namedtuple('stock', ('date', 'price', 'change'))
# 通过zip分别从date_array，price_array，change_array拿数据组成
# stock_namedtuple然后以date做为key组成OrderedDict
stock_dict = OrderedDict((date, stock_namedtuple(date, price, change)) for date, price, change in zip(date_array, price_array, change_array))
stock_dict
# In[28]:
# list(filter) for python3 配合 six.moves.filter for python3
up_days = list(filter(lambda day: day.change > 0, stock_dict.values()))
up_days
# In[29]:
def filter_stock(stock_array_dict, want_up=True, want_calc_sum=False):
if not isinstance(stock_array_dict, OrderedDict):
raise TypeError('stock_array_dict must be OrderedDict!')
# python中的三目表达式的写法
filter_func = (lambda day: day.change > 0) if want_up else (lambda day: day.change < 0)
# 使用filter_func做筛选函数
want_days = list(filter(filter_func, stock_array_dict.values()))
def sample_223(show=True):
"""
2.2.3 高阶函数
:return:
"""
stock_dict = sample_212(show=False)
show_func = print if show else lambda a: a
# 将字符串的的价格通过列表推导式显示转换为float类型
# 由于stock_dict是OrderedDict所以才可以直接
# 使用stock_dict.values()获取有序日期的收盘价格
price_float_array = [float(price_str) for price_str in stock_dict.values()]
# 通过将时间平移形成两个错开的收盘价序列，通过zip打包成为一个新的序列，
# 通过[:-1]:从第0个到倒数第二个，[1:]：从第一个到最后一个 错开形成相邻
# 组成的序列每个元素为相邻的两个收盘价格
pp_array = [(price1, price2) for price1, price2 in zip(price_float_array[:-1], price_float_array[1:])]
show_func(pp_array)
# list for python3
change_array = list(map(lambda pp: reduce(lambda a, b: round((b - a) / a, 3), pp), pp_array))
# list insert插入数据，将第一天的涨跌幅设置为0
change_array.insert(0, 0)
show_func(change_array)
price_str = '30.14, 29.58, 26.36, 32.56, 32.82'
price_str = price_str.replace(' ', '')
price_array = price_str.split(',')
date_base = 20170118
date_array = [str(date_base + ind) for ind, _ in enumerate(price_array)]
# 使用namedtuple重新构建数据结构
stock_namedtuple = namedtuple('stock', ('date', 'price', 'change'))
# 通过zip分别从date_array，price_array，change_array拿数据组成
# stock_namedtuple然后以date做为key组成OrderedDict
stock_dict = OrderedDict((date, stock_namedtuple(date, price, change)) for date, price, change in
zip(date_array, price_array, change_array))
show_func(stock_dict)
# list for python3
up_days = list(filter(lambda day: day.change > 0, stock_dict.values()))
show_func(up_days)
def filter_stock(stock_array_dict, want_up=True, want_calc_sum=False):
if not isinstance(stock_array_dict, OrderedDict):
raise TypeError('stock_array_dict must be OrderedDict!')
# python中的三目表达式的写法
filter_func = (lambda p_day: p_day.change > 0) if want_up else (lambda p_day: p_day.change < 0)
# 使用filter_func做筛选函数
want_days = list(filter(filter_func, stock_array_dict.values()))
if not want_calc_sum:
return want_days
# 需要计算涨跌幅和
change_sum = 0.0
for day in want_days:
change_sum += day.change
return change_sum
# 全部使用默认参数
show_func('所有上涨的交易日：{}'.format(filter_stock(stock_dict)))
# want_up=False
show_func('所有下跌的交易日：{}'.format(filter_stock(stock_dict, want_up=False)))
# 计算所有上涨的总会
show_func('所有上涨交易日的涨幅和：{}'.format(filter_stock(stock_dict, want_calc_sum=True)))
# 计算所有下跌的总会
show_func('所有下跌交易日的跌幅和：{}'.format(filter_stock(stock_dict, want_up=False, want_calc_sum=True)))
return stock_dict
