def sample_935_2():
"""
9.3.5_2 不同权重的评分: 只考虑胜率
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print(
'../gen/score_tuple_array not exist! please execute sample_933 first!'
)
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 只有第一项为1，其他都是0代表只考虑胜率来评分
scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])
# 返回按照评分排序后的队列
scorer_returns_max = scorer.fit_score()
# index[-1]为最优参数序号
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=False)
# 最后打印出只考虑胜率下最优结果使用的买入策略和卖出策略
print(
'best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n',
best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors)
def sample_935_2():
"""
9.3.5_2 不同权重的评分: 只考虑胜率
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 只有第一项为1，其他都是0代表只考虑胜率来评分
scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])
# 返回按照评分排序后的队列
scorer_returns_max = scorer.fit_score()
# index[-1]为最优参数序号
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=False)
# 最后打印出只考虑胜率下最优结果使用的买入策略和卖出策略
print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
best_score_tuple_grid.sell_factors)
def sample_934():
"""
9.3.4 度量结果的评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print(
'../gen/score_tuple_array not exist! please execute sample_933 first!'
)
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化一个评分类WrsmScorer，它的参数为之前GridSearch返回的score_tuple_array对象
scorer = WrsmScorer(score_tuple_array)
print('scorer.score_pd.tail():\n', scorer.score_pd.tail())
# score_tuple_array[658]与grid_search.best_score_tuple_grid是一致的
sfs = scorer.fit_score()
# 打印前15个高分组合
print('sfs[::-1][:15]:\n', sfs[::-1][:15])
def sample_933():
"""
9.3.3 GridSearch寻找最优参数
:return:
"""
from abupy import GridSearch
read_cash = 1000000
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
sell_factors_product, buy_factors_product = sample_932(show=False)
grid_search = GridSearch(read_cash, choice_symbols,
buy_factors_product=buy_factors_product,
sell_factors_product=sell_factors_product)
from abupy import ABuFileUtil
"""
注意下面的运行耗时大约1小时多，如果所有cpu都用上的话，也可以设置n_jobs为 < cpu进程数，一边做其它的一边跑
"""
# 运行GridSearch n_jobs=-1启动cpu个数的进程数
scores, score_tuple_array = grid_search.fit(n_jobs=-1)
"""
针对运行完成输出的score_tuple_array可以使用dump_pickle保存在本地，以方便修改其它验证效果。
"""
ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')
print('组合因子参数数量{}'.format(len(buy_factors_product) * len(sell_factors_product)))
print('最终评分结果数量{}'.format(len(scores)))
best_score_tuple_grid = grid_search.best_score_tuple_grid
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd, best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital, best_score_tuple_grid.benchmark)
def sample_935_1():
"""
9.3.5_1 不同权重的评分: 只考虑投资回报来评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化WrsmScorer，参数weights，只有第二项为1，其他都是0，
# 代表只考虑投资回报来评分
scorer = WrsmScorer(score_tuple_array, weights=[0, 1, 0, 0])
# 返回排序后的队列
scorer_returns_max = scorer.fit_score()
# 因为是倒序排序，所以index最后一个为最优参数
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
# 由于篇幅，最优结果只打印文字信息
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=True)
# 最后打印出只考虑投资回报下最优结果使用的买入策略和卖出策略
print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
best_score_tuple_grid.sell_factors)
def sample_935_1():
"""
9.3.5_1 不同权重的评分: 只考虑投资回报来评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化WrsmScorer，参数weights，只有第二项为1，其他都是0，
# 代表只考虑投资回报来评分
scorer = WrsmScorer(score_tuple_array, weights=[0, 1, 0, 0])
# 返回排序后的队列
scorer_returns_max = scorer.fit_score()
# 因为是倒序排序，所以index最后一个为最优参数
best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
# 由于篇幅，最优结果只打印文字信息
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital,
best_score_tuple_grid.benchmark,
only_info=True)
# 最后打印出只考虑投资回报下最优结果使用的买入策略和卖出策略
print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
best_score_tuple_grid.sell_factors)
def sample_933():
"""
9.3.3 GridSearch寻找最优参数
:return:
"""
from abupy import GridSearch
read_cash = 1000000
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
sell_factors_product, buy_factors_product = sample_932(show=False)
grid_search = GridSearch(read_cash, choice_symbols,
buy_factors_product=buy_factors_product,
sell_factors_product=sell_factors_product)
from abupy import ABuFileUtil
"""
注意下面的运行耗时大约1小时多，如果所有cpu都用上的话，也可以设置n_jobs为 < cpu进程数，一边做其它的一边跑
"""
# 运行GridSearch n_jobs=-1启动cpu个数的进程数
scores, score_tuple_array = grid_search.fit(n_jobs=-1)
"""
针对运行完成输出的score_tuple_array可以使用dump_pickle保存在本地，以方便修改其它验证效果。
"""
ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')
print('组合因子参数数量{}'.format(len(buy_factors_product) * len(sell_factors_product)))
print('最终评分结果数量{}'.format(len(scores)))
best_score_tuple_grid = grid_search.best_score_tuple_grid
AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd, best_score_tuple_grid.action_pd,
best_score_tuple_grid.capital, best_score_tuple_grid.benchmark)
def load_score_cache():
"""有本地数据score_tuple_array后，即可以从本地缓存读取score_tuple_array"""
global scores, score_tuple_array
with AbuBlockProgress('load score cache'):
score_tuple_array = ABuFileUtil.load_pickle('../gen/score_tuple_array')
if not hasattr(grid_search, 'best_score_tuple_grid'):
# load_pickle的grid_search没有赋予best_score_tuple_grid，这里补上
from abupy import make_scorer, WrsmScorer
scores = make_scorer(score_tuple_array, WrsmScorer)
grid_search.best_score_tuple_grid = score_tuple_array[
scores.index[-1]]
print('load complete!')
def sample_934():
"""
9.3.4 度量结果的评分
:return:
"""
from abupy import ABuFileUtil
score_fn = '../gen/score_tuple_array'
if not ABuFileUtil.file_exist(score_fn):
print('../gen/score_tuple_array not exist! please execute sample_933 first!')
return
"""
直接读取本地序列化文件
"""
score_tuple_array = ABuFileUtil.load_pickle(score_fn)
from abupy import WrsmScorer
# 实例化一个评分类WrsmScorer，它的参数为之前GridSearch返回的score_tuple_array对象
scorer = WrsmScorer(score_tuple_array)
print('scorer.score_pd.tail():\n', scorer.score_pd.tail())
# score_tuple_array[658]与grid_search.best_score_tuple_grid是一致的
sfs = scorer.fit_score()
# 打印前15个高分组合
print('sfs[::-1][:15]:\n', sfs[::-1][:15])
def run_grid_search():
global scores, score_tuple_array
# 运行GridSearch n_jobs=-1启动cpu个数的进程数
scores, score_tuple_array = grid_search.fit(n_jobs=-1)
# 运行完成输出的score_tuple_array可以使用dump_pickle保存在本地，以方便之后使用
ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')