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 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 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])
trade_loop_back = TradeLoopBack(trade_days, trade_strategy2)
trade_loop_back.execute_trade()
# 计算回测结果的最终盈亏值profit
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)
# In[66]:
# 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))
# In[58]:
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)))
# %%
set(price_array)
# %%
price_array.remove('32.82')
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 range(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
# %%
date_array = []
date_base = 20170118
price_cnt = len(price_array) - 1
while price_cnt > 0:
date_array.append(str(date_base))
date_base += 1
price_cnt -= 1
date_array