數字貨幣量化教程——構造金融數據的數據結構

 

一、數據準備

price_str = '30.14, 29.58, 26.36, 32.56, 32.82'
price_str = price_str.replace(' ', '')  #刪除空格
price_array = price_str.split(',')      #轉成數組

date_array = []
date_base = 20170118

 

二、構造日期

#推導式comprehensions（又稱解析式），是Python的一種獨有特性。推導式是能夠從一個數據序列構建另外一個新的數據序列的結構體。
#列表推導式

date_array = [str(date_base + ind) for ind, _ in enumerate(price_array)]
print(date_array)

返回

['20170118', '20170119', '20170120', '20170121', '20170122']

 

三、生成日期價格對

3.1 列表格式

# zip函數
stock_tuple_list = [(date, price) for date, price in zip(date_array, price_array)]
print(stock_tuple_list)

返回

[('20170118', '30.14'), ('20170119', '29.58'), ('20170120', '26.36'), ('20170121', '32.56'), ('20170122', '32.82')]

3.2 字典格式

#字典推導式
stock_dict = {date: price for date, price in zip(date_array, price_array)}
print(stock_dict)

返回

{'20170118': '30.14', '20170119': '29.58', '20170120': '26.36', '20170121': '32.56', '20170122': '32.82'}

3.3 元組格式

# 可命名元組 namedtuple
from collections import namedtuple
stock_nametuple = namedtuple('stock', ('date', 'price'))
stock_nametuple_list = [stock_nametuple(date, price) for date, price in zip(date_array, price_array)]
print(stock_nametuple_list)

返回

[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 import OrderedDict
stock_dict = OrderedDict((date, price) for date, price in zip(date_array, price_array))
print(stock_dict.keys())

返回

 odict_keys(['20170118', '20170119', '20170120', '20170121', '20170122'])

 

#最小收盤價
print(min(zip(stock_dict.values(), stock_dict.keys())))

返回

('26.36', '20170120')

 

#lambad函數
func = lambda x:x+1
#以上lambda等同於如下函數
def func(x):
    return(x+1)

 

#找出收盤價中第二大的價格
find_second_max_lambda = lambda dict_array : sorted(zip(dict_array.values(), dict_array.keys()))[-2]
print(find_second_max_lambda(stock_dict))

返回

 ('32.56', '20170121')

 

#高階函數
#將相鄰的收盤價格組成tuple後裝入list
price_float_array = [float(price_str) for price_str in stock_dict.values()]
pp_array = [(price1, price2) for price1, price2 in zip(price_float_array[:-1], price_float_array[1:])]
print(pp_array)

返回

 [(30.14, 29.58), (29.58, 26.36), (26.36, 32.56), (32.56, 32.82)]

 

from functools import reduce
#外層使用map函數針對pp_array()的每個元素執行操做，內層使用reduce()函數即兩個相鄰的價格， 求出漲跌幅度，返回外層結果list
change_array = list(map(lambda pp:reduce(lambda a,b: round((b-a) / a, 3),pp), pp_array))

change_array.insert(0,0)
print(change_array)

返回

[0, -0.019, -0.109, 0.235, 0.008]

 

 

#將漲跌幅數據加入OrderedDict，配合使用namedtuple從新構建數據結構stock_dict
stock_nametuple = namedtuple('stock', ('date', 'price', 'change'))
stock_dict = OrderedDict((date, stock_nametuple(date, price, change))
                         for date, price, change in
                         zip(date_array, price_array, change_array))
print(stock_dict)

返回

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()進行篩選，選出上漲的交易日
up_days = list(filter(lambda day: day.change > 0, stock_dict.values()))
print(up_days)

返回

[stock(date='20170121', price='32.56', change=0.235), stock(date='20170122', price='32.82', change=0.008)]

 

 

#定義函數計算漲跌日或漲跌值
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')

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

    want_days = list(filter(filter_func, stock_array_dict.values()))

    if not want_calc_sum:
        return want_days

    change_sum = 0.0
    for day in want_days:
        change_sum += day.change

    return change_sum

    

#偏函數 partial
from functools import partial
filter_stock_up_days    = partial(filter_stock, want_up=True,  want_calc_sum=False)
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('全部上漲的交易日：{}'.format(list(filter_stock_up_days(stock_dict))))
print('全部下跌的交易日：{}'.format(list(filter_stock_down_days(stock_dict))))
print('全部上漲交易日的漲幅和：{}'.format(filter_stock_up_sums(stock_dict)))
print('全部下跌交易日的跌幅和：{}'.format(filter_stock_down_sums(stock_dict)))

 

# 全部上漲的交易日：[stock(date='20170121', price='32.56', change=0.235), stock(date='20170122', price='32.82', change=0.008)]
# 全部下跌的交易日：[stock(date='20170119', price='29.58', change=-0.019), stock(date='20170120', price='26.36', change=-0.109)]
# 全部上漲交易日的漲幅和：0.243
# 全部下跌交易日的跌幅和：-0.128