def sample_412():
"""
4.1.2 索引行列序列
:return:
"""
print('xrange:', xrange(stock_day_change.shape[0]))
# 股票0 -> 股票stock_day_change.shape[0]
stock_symbols = ['股票 1 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
# 通过构造直接设置index参数，head(2)就显示两行，表4-2所示
print('pd.DataFrame(stock_day_change, index=stock_symbols).head(2):\n',
pd.DataFrame(stock_day_change, index=stock_symbols).head(2))
# 从2017-1-1向上时间递进，单位freq='1d'即1天
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1], freq='1d')
print('days:', days)
# 股票0 -> 股票stock_day_change.shape[0]
stock_symbols = ['股票 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
# 分别设置index和columns
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
# 表4-3所示
print('df.head(2):\n', df.head(2))
def sample_415():
"""
4.1.5 重采样数据
:return
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1], freq='1d')
stock_symbols = ['股票 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
df = df.T
df_stock0 = df['股票 0']
# 以5天为周期重采样（周k）
df_stock0_5 = pd_resample(df_stock0.cumsum(), '5D', how='ohlc')
# 以21天为周期重采样（月k），
# noinspection PyUnusedLocal
df_stock0_20 = pd_resample(df_stock0.cumsum(), '21D', how='ohlc')
# 打印5天重采样，如下输出2017-01-01, 2017-01-06, 2017-01-11, 表4-6所示
print('df_stock0_5.head():\n', df_stock0_5.head())
from abupy import ABuMarketDrawing
# 图4-2所示
ABuMarketDrawing.plot_candle_stick(df_stock0_5.index,
df_stock0_5['open'].values,
df_stock0_5['high'].values,
df_stock0_5['low'].values,
df_stock0_5['close'].values,
np.random.random(len(df_stock0_5)),
None, 'stock', day_sum=False,
html_bk=False, save=False)
print('type(df_stock0_5.open.values):', type(df_stock0_5['open'].values))
print('df_stock0_5.open.index:\n', df_stock0_5['open'].index)
print('df_stock0_5.columns:\n', df_stock0_5.columns)
def gen_buy_change_list():
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 1. 除数或者被除数其中一个是float类型
buy_change = buy_change / 100.0
buy_change_list.append(buy_change)
return buy_change_list
def gen_buy_change_list():
buy_change_list = []
# 下跌买入阀值从-0.05到-0.15，即从下跌5%到15%
for buy_change in xrange(-5, -16, -1):
buy_change = buy_change / 100
buy_change_list.append(buy_change)
return buy_change_list
def sample_212(show=True):
"""
2.1.2 字符串和容器
:return:
"""
show_func = print if show else lambda a: a
price_str = '30.14, 29.58, 26.36, 32.56, 32.82'
show_func('旧的price_str id= {}'.format(id(price_str)))
price_str = price_str.replace(' ', '')
show_func('新的price_str id= {}'.format(id(price_str)))
show_func(price_str)
# split以逗号分割字符串，返回数组price_array
price_array = price_str.split(',')
show_func(price_array)
# price_array尾部append一个重复的32.82
price_array.append('32.82')
show_func(price_array)
show_func(set(price_array))
price_array.remove('32.82')
show_func(price_array)
date_array = []
date_base = 20170118
# 这里用for只是为了计数，无用的变量python建议使用'_'声明
for _ in xrange(0, len(price_array)):
date_array.append(str(date_base))
# 本节只是简单示例，不考虑日期的进位
date_base += 1
show_func(date_array)
date_base = 20170118
date_array = [str(date_base + ind) for ind, _ in enumerate(price_array)]
show_func(date_array)
stock_tuple_list = [(date, price)
for date, price in zip(date_array, price_array)]
# tuple访问使用索引
show_func('20170119日价格：{}'.format(stock_tuple_list[1][1]))
show_func(stock_tuple_list)
stock_namedtuple = namedtuple('stock', ('date', 'price'))
stock_namedtuple_list = [
stock_namedtuple(date, price)
for date, price in zip(date_array, price_array)
]
# namedtuple访问使用price
show_func('20170119日价格：{}'.format(stock_namedtuple_list[1].price))
show_func(stock_namedtuple_list)
# 字典推导式：{key: value for in}
stock_dict = {date: price for date, price in zip(date_array, price_array)}
show_func('20170119日价格：{}'.format(stock_dict['20170119']))
show_func(stock_dict)
show_func(stock_dict.keys())
stock_dict = OrderedDict(
(date, price) for date, price in zip(date_array, price_array))
show_func(stock_dict.keys())
return stock_dict
def sample_243():
"""
2.4.3 使用编译库提高性能
:return:
"""
# 买入后持股天数放大寻找范围 1 - 503 天, 间隔1天
keep_stock_list = list(range(1, 504, 1))
# 下跌买入阀值寻找范围 -0.01 - -0.99 共99个
buy_change_list = [buy_change / 100.0 for buy_change in xrange(-1, -100, -1)]
def do_single_task():
task_list = list(itertools.product(keep_stock_list, buy_change_list))
print('笛卡尔积参数集合总共结果为：{}个'.format(len(task_list)))
for keep_stock_threshold, buy_change_threshold in task_list:
calc(keep_stock_threshold, buy_change_threshold)
import time
start_time = time.time()
do_single_task()
end_time = time.time()
print('{} cost {}s'.format(do_single_task.__name__, round(end_time - start_time, 3)))
import numba as nb
do_single_task_nb = nb.jit(do_single_task)
start_time = time.time()
do_single_task_nb()
end_time = time.time()
print('{} cost {}s'.format(do_single_task_nb.__name__, round(end_time - start_time, 3)))
def sample_243():
"""
2.4.3 使用编译库提高性能
:return:
"""
# 买入后持股天数放大寻找范围 1 - 503 天, 间隔1天
keep_stock_list = list(range(1, 504, 1))
# 下跌买入阀值寻找范围 -0.01 - -0.99 共99个
buy_change_list = [buy_change / 100.0 for buy_change in xrange(-1, -100, -1)]
def do_single_task():
task_list = list(itertools.product(keep_stock_list, buy_change_list))
print('笛卡尔积参数集合总共结果为：{}个'.format(len(task_list)))
for keep_stock_threshold, buy_change_threshold in task_list:
calc(keep_stock_threshold, buy_change_threshold)
import time
start_time = time.time()
do_single_task()
end_time = time.time()
print('{} cost {}s'.format(do_single_task.__name__, round(end_time - start_time, 3)))
import numba as nb
do_single_task_nb = nb.jit(do_single_task)
start_time = time.time()
do_single_task_nb()
end_time = time.time()
print('{} cost {}s'.format(do_single_task_nb.__name__, round(end_time - start_time, 3)))
def gen_buy_change_list():
buy_change_list = []
# 下跌买入阀值从-0.05到-0.15，即从下跌5%到15%
for buy_change in xrange(-5, -16, -1):
buy_change = buy_change / 100
buy_change_list.append(buy_change)
return buy_change_list
def gen_buy_change_list():
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 1. 除数或者被除数其中一个是float类型
buy_change = buy_change / 100.0
buy_change_list.append(buy_change)
return buy_change_list
def sample_625():
"""
6.2.5 非凸函数计算怎样度过一生最幸福
:return:
"""
import scipy.optimize as sco
method = 'SLSQP'
# 提供一个函数来规范参数,np.sum(weights) = 1 -> np.sum(weights) - 1 = 0
constraints = ({'type': 'eq', 'fun': lambda p_x: np.sum(p_x) - 1})
# 参数的范围选定
bounds = tuple((0, 0.9) for _ in xrange(3))
print('bounds:', bounds)
def minimize_happiness_local(weights):
# print(weights)
return -my_life(weights)[1]
# 初始化猜测最优参数，这里使用brute计算出的全局最优参数作为guess
guess = [0.5, 0.2, 0.3]
opt_local = sco.minimize(minimize_happiness_local,
guess,
method=method,
bounds=bounds,
constraints=constraints)
print('opt_local:', opt_local)
def gen_buy_change_list():
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 1. 原始buy_change
print(buy_change)
buy_change = buy_change / 100
# 2. buy_change/100
print(buy_change)
buy_change_list.append(buy_change)
return buy_change_list
def gen_buy_change_list():
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 1. 原始buy_change
print(buy_change)
buy_change = buy_change / 100
# 2. buy_change/100
print(buy_change)
buy_change_list.append(buy_change)
return buy_change_list
def sample_212(show=True):
"""
2.1.2 字符串和容器
:return:
"""
show_func = print if show else lambda a: a
price_str = '30.14, 29.58, 26.36, 32.56, 32.82'
show_func('旧的price_str id= {}'.format(id(price_str)))
price_str = price_str.replace(' ', '')
show_func('新的price_str id= {}'.format(id(price_str)))
show_func(price_str)
# split以逗号分割字符串，返回数组price_array
price_array = price_str.split(',')
show_func(price_array)
# price_array尾部append一个重复的32.82
price_array.append('32.82')
show_func(price_array)
show_func(set(price_array))
price_array.remove('32.82')
show_func(price_array)
date_array = []
date_base = 20170118
# 这里用for只是为了计数，无用的变量python建议使用'_'声明
for _ in xrange(0, len(price_array)):
date_array.append(str(date_base))
# 本节只是简单示例，不考虑日期的进位
date_base += 1
show_func(date_array)
date_base = 20170118
date_array = [str(date_base + ind) for ind, _ in enumerate(price_array)]
show_func(date_array)
stock_tuple_list = [(date, price) for date, price in zip(date_array, price_array)]
# tuple访问使用索引
show_func('20170119日价格：{}'.format(stock_tuple_list[1][1]))
show_func(stock_tuple_list)
stock_namedtuple = namedtuple('stock', ('date', 'price'))
stock_namedtuple_list = [stock_namedtuple(date, price) for date, price in zip(date_array, price_array)]
# namedtuple访问使用price
show_func('20170119日价格：{}'.format(stock_namedtuple_list[1].price))
show_func(stock_namedtuple_list)
# 字典推导式：{key: value for in}
stock_dict = {date: price for date, price in zip(date_array, price_array)}
show_func('20170119日价格：{}'.format(stock_dict['20170119']))
show_func(stock_dict)
show_func(stock_dict.keys())
stock_dict = OrderedDict((date, price) for date, price in zip(date_array, price_array))
show_func(stock_dict.keys())
return stock_dict
def gen_buy_change_list():
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 只针对循环执行到buy_change == -10，中断开始调试
if buy_change == -10:
# 打断点，通过set_trace
pdb.set_trace()
buy_change = buy_change / 100
buy_change_list.append(buy_change)
# 故意向外抛出异常
raise RuntimeError('debug for pdb')
def sample_412():
"""
4.1.2 索引行列序列
:return:
"""
# 股票0 -> 股票stock_day_change.shape[0]
stock_symbols = ['股票 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
# 通过构造直接设置index参数，head(2)就显示两行，表4-2所示
print('pd.DataFrame(stock_day_change, index=stock_symbols).head(2):\n',
pd.DataFrame(stock_day_change, index=stock_symbols).head(2))
# 从2017-1-1向上时间递进，单位freq='1d'即1天
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1], freq='1d')
# 股票0 -> 股票stock_day_change.shape[0]
stock_symbols = ['股票 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
# 分别设置index和columns
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
# 表4-3所示
print('df.head(2):\n', df.head(2))
def gen_buy_change_list():
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 只针对循环执行到buy_change == -10，中断开始调试
if buy_change == -10:
# 打断点，通过set_trace
pdb.set_trace()
buy_change = buy_change / 100
buy_change_list.append(buy_change)
# 故意向外抛出异常
raise RuntimeError('debug for pdb')
def sample_242():
"""
2.4.2 多进程 vs 多线程
:return:
"""
# range集合：买入后持股天数从2天－30天，间隔两天
keep_stock_list = list(range(2, 30, 2))
print('持股天数参数组：{}'.format(keep_stock_list))
# 下跌买入阀值从-0.05到-0.15，即从下跌5%到15%
buy_change_list = [
buy_change / 100.0 for buy_change in xrange(-1, -100, -1)
]
print('下跌阀值参数组：{}'.format(buy_change_list))
result = []
# 回调函数，通过add_done_callback任务完成后调用
def when_done(r):
# when_done在主进程中运行
result.append(r.result())
"""
with class_a() as a: 上下文管理器：稍后会具体讲解
"""
with ProcessPoolExecutor() as pool:
for keep_stock_threshold, buy_change_threshold in \
itertools.product(keep_stock_list, buy_change_list):
"""
submit提交任务：使用calc函数和的参数通过submit提交到独立进程
提交的任务必须是简单函数，进程并行不支持类方法、闭包等
函数参数和返回值必须兼容pickle序列化，进程间的通信需要
"""
future_result = pool.submit(calc, keep_stock_threshold,
buy_change_threshold)
# 当进程完成任务即calc运行结束后的回调函数
future_result.add_done_callback(when_done)
print('Process sorted(result)[::-1][:10]:\n', sorted(result)[::-1][:10])
result = []
def when_done(r):
result.append(r.result())
with ThreadPoolExecutor(max_workers=8) as pool:
for keep_stock_threshold, buy_change_threshold in \
itertools.product(keep_stock_list, buy_change_list):
future_result = pool.submit(calc, keep_stock_threshold,
buy_change_threshold)
future_result.add_done_callback(when_done)
print('Thread sorted(result)[::-1][:10]:\n', sorted(result)[::-1][:10])
def sample_242():
"""
2.4.2 多进程 vs 多线程
:return:
"""
# range集合：买入后持股天数从2天－30天，间隔两天
keep_stock_list = list(range(2, 30, 2))
print('持股天数参数组：{}'.format(keep_stock_list))
# 下跌买入阀值从-0.05到-0.15，即从下跌5%到15%
buy_change_list = [buy_change / 100.0 for buy_change in xrange(-1, -100, -1)]
print('下跌阀值参数组：{}'.format(buy_change_list))
result = []
# 回调函数，通过add_done_callback任务完成后调用
def when_done(r):
# when_done在主进程中运行
result.append(r.result())
"""
with class_a() as a: 上下文管理器：稍后会具体讲解
"""
with ProcessPoolExecutor() as pool:
for keep_stock_threshold, buy_change_threshold in \
itertools.product(keep_stock_list, buy_change_list):
"""
submit提交任务：使用calc函数和的参数通过submit提交到独立进程
提交的任务必须是简单函数，进程并行不支持类方法、闭包等
函数参数和返回值必须兼容pickle序列化，进程间的通信需要
"""
future_result = pool.submit(calc, keep_stock_threshold,
buy_change_threshold)
# 当进程完成任务即calc运行结束后的回调函数
future_result.add_done_callback(when_done)
print('Process sorted(result)[::-1][:10]:\n', sorted(result)[::-1][:10])
result = []
def when_done(r):
result.append(r.result())
with ThreadPoolExecutor(max_workers=8) as pool:
for keep_stock_threshold, buy_change_threshold in \
itertools.product(keep_stock_list, buy_change_list):
future_result = pool.submit(calc, keep_stock_threshold,
buy_change_threshold)
future_result.add_done_callback(when_done)
print('Thread sorted(result)[::-1][:10]:\n', sorted(result)[::-1][:10])
def gen_buy_change_list():
# 会打印出来，因为info >= level=logging.INFO
logging.info("gen_buy_change_list begin")
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 不会打印出来，debug < level=logging.INFO
logging.debug(buy_change)
buy_change = buy_change / 100
# 不会打印出来，debug < level=logging.INFO
logging.debug(buy_change)
buy_change_list.append(buy_change)
# 会打印出来，因为info >= level=logging.INFO
logging.info("gen_buy_change_list end")
return buy_change_list
def gen_buy_change_list():
# 会打印出来，因为info >= level=logging.INFO
logging.info("gen_buy_change_list begin")
buy_change_list = []
for buy_change in xrange(-5, -16, -1):
# 不会打印出来，debug < level=logging.INFO
logging.debug(buy_change)
buy_change = buy_change / 100
# 不会打印出来，debug < level=logging.INFO
logging.debug(buy_change)
buy_change_list.append(buy_change)
# 会打印出来，因为info >= level=logging.INFO
logging.info("gen_buy_change_list end")
return buy_change_list
def sample_413():
"""
4.1.3 金融时间序列
:return:
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1], freq='1d')
stock_symbols = ['股票 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
# df做个转置
df = df.T
# 表4-4所示
print('df.head():\n', df.head())
df_20 = pd_resample(df, '21D', how='mean')
# 表4-5所示
print('df_20.head():\n', df_20.head())
def sample_413():
"""
4.1.3 金融时间序列
:return:
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1],
freq='1d')
stock_symbols = ['股票 ' + str(x) for x in xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
# df做个转置
df = df.T
# 表4-4所示
print('df.head():\n', df.head())
df_20 = pd_resample(df, '21D', how='mean')
# 表4-5所示
print('df_20.head():\n', df_20.head())
def sample_414():
"""
4.1.4 Series构建及方法
:return
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1],
freq='1d')
stock_symbols = ['股票 ' + str(x) for x in xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
df = df.T
print('df.head():\n', df.head())
df_stock0 = df['股票 0']
# 打印df_stock0类型
print('type(df_stock0):', type(df_stock0))
# 打印出Series的前5行数据, 与DataFrame一致
print('df_stock0.head():\n', df_stock0.head())
df_stock0.cumsum().plot()
plt.show()
def sample_414():
"""
4.1.4 Series构建及方法
:return
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1], freq='1d')
stock_symbols = ['股票 ' + str(x) for x in
xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
df = df.T
print('df.head():\n', df.head())
df_stock0 = df['股票 0']
# 打印df_stock0类型
print('type(df_stock0):', type(df_stock0))
# 打印出Series的前5行数据, 与DataFrame一致
print('df_stock0.head():\n', df_stock0.head())
df_stock0.cumsum().plot()
plt.show()
def sample_241_2():
"""
2.4.1_2 笛卡尔积最优参数
:return:
"""
# range集合：买入后持股天数从2天－30天，间隔两天
keep_stock_list = list(range(2, 30, 2))
print('持股天数参数组：{}'.format(keep_stock_list))
# 下跌买入阀值从-0.05到-0.15，即从下跌5%到15%
buy_change_list = [buy_change / 100.0 for buy_change in xrange(-5, -16, -1)]
print('下跌阀值参数组：{}'.format(buy_change_list))
result = []
for keep_stock_threshold, buy_change_threshold in itertools.product(
keep_stock_list, buy_change_list):
# 使用calc计算参数对应的最终盈利，结果加入result序列
result.append(calc(keep_stock_threshold, buy_change_threshold))
print('笛卡尔积参数集合总共结果为：{}个'.format(len(result)))
# [::-1]将整个排序结果反转，反转后盈亏收益从最高向低排序
# [:10]取出收益最高的前10个组合查看
print(sorted(result)[::-1][:10])
def sample_241_2():
"""
2.4.1_2 笛卡尔积最优参数
:return:
"""
# range集合：买入后持股天数从2天－30天，间隔两天
keep_stock_list = list(range(2, 30, 2))
print('持股天数参数组：{}'.format(keep_stock_list))
# 下跌买入阀值从-0.05到-0.15，即从下跌5%到15%
buy_change_list = [buy_change / 100.0 for buy_change in xrange(-5, -16, -1)]
print('下跌阀值参数组：{}'.format(buy_change_list))
result = []
for keep_stock_threshold, buy_change_threshold in itertools.product(
keep_stock_list, buy_change_list):
# 使用calc计算参数对应的最终盈利，结果加入result序列
result.append(calc(keep_stock_threshold, buy_change_threshold))
print('笛卡尔积参数集合总共结果为：{}个'.format(len(result)))
# [::-1]将整个排序结果反转，反转后盈亏收益从最高向低排序
# [:10]取出收益最高的前10个组合查看
print(sorted(result)[::-1][:10])
def sample_415():
"""
4.1.5 重采样数据
:return
"""
days = pd.date_range('2017-1-1',
periods=stock_day_change.shape[1],
freq='1d')
stock_symbols = ['股票 ' + str(x) for x in xrange(stock_day_change.shape[0])]
df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)
df = df.T
df_stock0 = df['股票 0']
# 以5天为周期重采样（周k）
df_stock0_5 = pd_resample(df_stock0.cumsum(), '5D', how='ohlc')
# 以21天为周期重采样（月k），
# noinspection PyUnusedLocal
df_stock0_20 = pd_resample(df_stock0.cumsum(), '21D', how='ohlc')
# 打印5天重采样，如下输出2017-01-01, 2017-01-06, 2017-01-11, 表4-6所示
print('df_stock0_5.head():\n', df_stock0_5.head())
from abupy import ABuMarketDrawing
# 图4-2所示
ABuMarketDrawing.plot_candle_stick(df_stock0_5.index,
df_stock0_5['open'].values,
df_stock0_5['high'].values,
df_stock0_5['low'].values,
df_stock0_5['close'].values,
np.random.random(len(df_stock0_5)),
None,
'stock',
day_sum=False,
html_bk=False,
save=False)
print('type(df_stock0_5.open.values):', type(df_stock0_5['open'].values))
print('df_stock0_5.open.index:\n', df_stock0_5['open'].index)
print('df_stock0_5.columns:\n', df_stock0_5.columns)
def sample_625():
"""
6.2.5 非凸函数计算怎样度过一生最幸福
:return:
"""
import scipy.optimize as sco
method = 'SLSQP'
# 提供一个函数来规范参数,np.sum(weights) = 1 -> np.sum(weights) - 1 = 0
constraints = ({'type': 'eq', 'fun': lambda p_x: np.sum(p_x) - 1})
# 参数的范围选定
bounds = tuple((0, 0.9) for _ in xrange(3))
print('bounds:', bounds)
def minimize_happiness_local(weights):
# print(weights)
return -my_life(weights)[1]
# 初始化猜测最优参数，这里使用brute计算出的全局最优参数作为guess
guess = [0.5, 0.2, 0.3]
opt_local = sco.minimize(minimize_happiness_local, guess,
method=method, bounds=bounds,
constraints=constraints)
print('opt_local:', opt_local)
# In[11]:
price_array.remove('32.82')
price_array
# In[12]:
date_array = []
date_base = 20170118
# 这里用for只是为了计数，无用的变量python建议使用'_'声明
for _ in xrange(0, len(price_array)):
# 本节只是简单示例，不考虑日期的进位
date_array.append(str(date_base))
date_base += 1
date_array
# In[13]:
date_array = []
date_base = 20170118
price_cnt = len(price_array) - 1
while price_cnt > 0:
date_array.append(str(date_base))
date_base += 1
def sample_622():
"""
6.2.2 使用蒙特卡洛方法计算怎样度过一生最幸福
:return:
"""
living_day, happiness, wealth, fame = my_life([0.4, 0.3, 0.3])
print('活了{}年，幸福指数{}, 积累财富{}, 名望权力{}'.format(
living_day, happiness, wealth, fame))
from abupy import AbuProgress
progress = AbuProgress(2000, 0, label='my_life...')
result = []
for pos, _ in enumerate(xrange(2000)):
# 2000次随机权重分配
weights = np.random.random(3)
weights /= np.sum(weights)
# result中：tuple[0]权重weights,，tuple[1]my_life返回的结果
result.append((weights, my_life(weights)))
progress.show(a_progress=pos + 1)
# result中tuple[1]=my_life返回的结果, my_life[1]=幸福指数，so->x[1][1]
sorted_scores = sorted(result, key=lambda p_x: p_x[1][1], reverse=True)
# 将最优权重sorted_scores[0][0]代入my_life得到结果
living_day, happiness, wealth, fame = my_life(sorted_scores[0][0])
print('活了{}年，幸福指数{}, 积累财富{}, 名望权力{}'.format
(living_day, happiness, wealth, fame))
print('人生最优权重：追求健康{:.3f},追求财富{:.3f},追求名望{:.3f}'.format(
sorted_scores[0][0][0], sorted_scores[0][0][1],
sorted_scores[0][0][2]))
# noinspection PyUnresolvedReferences
from mpl_toolkits.mplot3d import Axes3D
"""
result中: tuple[0]权重weights, tuple[1]my_life返回的结果
r[0][0]: 追求健康长寿快乐的权重
r[0][1]: 追求财富金钱的权重
r[0][2]: 追求名望权力的权重
r[1][1]: my_life[1]=幸福指数
"""
result_show = np.array(
[[r[0][0], r[0][1], r[0][2], r[1][1]] for r in result])
fig = plt.figure(figsize=(9, 6))
ax = fig.gca(projection='3d')
ax.view_init(30, 60)
"""
x:追求健康长寿快乐的权重, y:追求财富金钱的权重
z:追求名望权力的权重, c:color 幸福指数, 颜色越深越幸福
"""
ax.scatter3D(result_show[:, 0], result_show[:, 1], result_show[:, 2],
c=result_show[:, 3], cmap='spring')
ax.set_xlabel('health')
ax.set_ylabel('stock')
ax.set_zlabel('fame')
plt.show()
# 幸福指数
happiness_result = result_show[:, 3]
# 使用qcut分10份
print('pd.qcut(happiness_result, 10).value_counts():\n', pd.qcut(happiness_result, 10).value_counts())
def sample_622():
"""
6.2.2 使用蒙特卡洛方法计算怎样度过一生最幸福
:return:
"""
living_day, happiness, wealth, fame = my_life([0.4, 0.3, 0.3])
print('活了{}年，幸福指数{}, 积累财富{}, 名望权力{}'.format(
living_day, happiness, wealth, fame))
from abupy import AbuProgress
progress = AbuProgress(2000, 0, label='my_life...')
result = []
for pos, _ in enumerate(xrange(2000)):
# 2000次随机权重分配
weights = np.random.random(3)
weights /= np.sum(weights)
# result中：tuple[0]权重weights,，tuple[1]my_life返回的结果
result.append((weights, my_life(weights)))
progress.show(a_progress=pos + 1)
# result中tuple[1]=my_life返回的结果, my_life[1]=幸福指数，so->x[1][1]
sorted_scores = sorted(result, key=lambda p_x: p_x[1][1], reverse=True)
# 将最优权重sorted_scores[0][0]代入my_life得到结果
living_day, happiness, wealth, fame = my_life(sorted_scores[0][0])
print('活了{}年，幸福指数{}, 积累财富{}, 名望权力{}'.format
(living_day, happiness, wealth, fame))
print('人生最优权重：追求健康{:.3f},追求财富{:.3f},追求名望{:.3f}'.format(
sorted_scores[0][0][0], sorted_scores[0][0][1],
sorted_scores[0][0][2]))
# noinspection PyUnresolvedReferences
from mpl_toolkits.mplot3d import Axes3D
"""
result中: tuple[0]权重weights, tuple[1]my_life返回的结果
r[0][0]: 追求健康长寿快乐的权重
r[0][1]: 追求财富金钱的权重
r[0][2]: 追求名望权力的权重
r[1][1]: my_life[1]=幸福指数
"""
result_show = np.array([[r[0][0], r[0][1], r[0][2], r[1][1]] for r in result])
fig = plt.figure(figsize=(9, 6))
ax = fig.gca(projection='3d')
ax.view_init(30, 60)
"""
x:追求健康长寿快乐的权重, y:追求财富金钱的权重
z:追求名望权力的权重, c:color 幸福指数, 颜色越深越幸福
"""
ax.scatter3D(result_show[:, 0], result_show[:, 1], result_show[:, 2],
c=result_show[:, 3], cmap='spring')
ax.set_xlabel('health')
ax.set_ylabel('stock')
ax.set_zlabel('fame')
plt.show()
# 幸福指数
happiness_result = result_show[:, 3]
# 使用qcut分10份
print('pd.qcut(happiness_result, 10).value_counts():\n', pd.qcut(happiness_result, 10).value_counts())
profit = 0.0 if len(trade_loop_back.profit_array) == 0 else \
reduce(lambda a, b: a + b, trade_loop_back.profit_array)
# 返回值profit和函数的两个输入参数
return profit, keep_stock_threshold, buy_change_threshold
# 测试，使用上一节使用的参数
calc(20, -0.08)
# %%
# range集合：买入后持股天数从2天－30天，间隔两天
keep_stock_list = list(range(2, 30, 2))
print('持股天数参数组：{}'.format(keep_stock_list))
# 下跌买入阀值从-0.05到-0.15，即从下跌5%到15%
buy_change_list = [buy_change / 100.0 for buy_change in xrange(-5, -16, -1)]
print('下跌阀值参数组：{}'.format(buy_change_list))
# %%
result = []
for keep_stock_threshold, buy_change_threshold in itertools.product(
keep_stock_list, buy_change_list):
# 使用calc计算参数对应的最终盈利，结果加入result序列
result.append(calc(keep_stock_threshold, buy_change_threshold))
print('笛卡尔积参数集合总共结果为：{}个'.format(len(result)))
# %%
# [::-1]将整个排序结果反转，反转后盈亏收益从最高向低排序
# [:10]取出收益最高的前10个组合查看
