# -*- encoding:utf-8 -*-

from __future__ import print_function

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

import warnings

# noinspection PyUnresolvedReferences

import abu_local_env

import abupy

from abupy import AbuMetricsBase

from abupy import AbuFactorBuyBreak

from abupy import AbuFactorAtrNStop

from abupy import AbuFactorPreAtrNStop

from abupy import AbuFactorCloseAtrNStop

# run_loop_back等一些常用且最外层的方法定义在abu中

from abupy import abu

warnings.filterwarnings('ignore')

sns.set_context(rc={'figure.figsize': (14, 7)})

# 使用沙盒数据，目的是和书中一样的数据环境

abupy.env.enable_example_env_ipython()

# 设置选股因子，None为不使用选股因子

stock_pickers = None

# 买入因子依然延用向上突破因子

buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},

{'xd': 42, 'class': AbuFactorBuyBreak}]

# 卖出因子继续使用上一章使用的因子

sell_factors = [

{'stop_loss_n': 1.0, 'stop_win_n': 3.0,

'class': AbuFactorAtrNStop},

{'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},

{'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}

]

"""

第九章 量化系统——度量与优化

abu量化系统github地址：https://github.com/bbfamily/abu (您的star是我的动力！)

abu量化文档教程ipython notebook：https://github.com/bbfamily/abu/tree/master/abupy_lecture

"""

def sample_91(show=True):

"""

9.1 度量的基本使用方法

:return:

"""

# 设置初始资金数

read_cash = 1000000

# 择时股票池

choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',

'usTSLA', 'usWUBA', 'usVIPS']

# 使用run_loop_back运行策略

abu_result_tuple, kl_pd_manager = abu.run_loop_back(read_cash,

buy_factors,

sell_factors,

stock_pickers,

choice_symbols=choice_symbols, n_folds=2)

metrics = AbuMetricsBase(*abu_result_tuple)

metrics.fit_metrics()

if show:

metrics.plot_returns_cmp()

return metrics

def sample_922():

"""

9.2.2 度量的可视化

:return:

"""

metrics = sample_91(show=False)

metrics.plot_sharp_volatility_cmp()

plt.show()

def sharpe(rets, ann=252):

return rets.mean() / rets.std() * np.sqrt(ann)

print('策略sharpe值计算为＝{}'.format(sharpe(metrics.algorithm_returns)))

metrics.plot_effect_mean_day()

plt.show()

metrics.plot_keep_days()

plt.show()

metrics.plot_sell_factors()

plt.show()

metrics.plot_max_draw_down()

plt.show()

"""

9.3 基于grid search寻找因子最优参数

"""

stop_win_range = np.arange(2.0, 4.5, 0.5)

stop_loss_range = np.arange(0.5, 2, 0.5)

sell_atr_nstop_factor_grid = {

'class': [AbuFactorAtrNStop],

'stop_loss_n': stop_loss_range,

'stop_win_n': stop_win_range

}

close_atr_range = np.arange(1.0, 4.0, 0.5)

pre_atr_range = np.arange(1.0, 3.5, 0.5)

sell_atr_pre_factor_grid = {

'class': [AbuFactorPreAtrNStop],

'pre_atr_n': pre_atr_range

}

sell_atr_close_factor_grid = {

'class': [AbuFactorCloseAtrNStop],

'close_atr_n': close_atr_range

}

def sample_931():

"""

9.3.1 参数取值范围

:return:

"""

print('止盈参数stop_win_n设置范围:{}'.format(stop_win_range))

print('止损参数stop_loss_n设置范围:{}'.format(stop_loss_range))

print('暴跌保护止损参数pre_atr_n设置范围:{}'.format(pre_atr_range))

print('盈利保护止盈参数close_atr_n设置范围:{}'.format(close_atr_range))

def sample_932(show=True):

"""

9.3.2 参数进行排列组合

:return:

"""

from abupy import ABuGridHelper

sell_factors_product = ABuGridHelper.gen_factor_grid(

ABuGridHelper.K_GEN_FACTOR_PARAMS_SELL,

[sell_atr_nstop_factor_grid, sell_atr_pre_factor_grid, sell_atr_close_factor_grid])

if show:

print('卖出因子参数共有{}种组合方式'.format(len(sell_factors_product)))

print('卖出因子组合0形式为{}'.format(sell_factors_product[0]))

buy_bk_factor_grid1 = {

'class': [AbuFactorBuyBreak],

'xd': [42]

}

buy_bk_factor_grid2 = {

'class': [AbuFactorBuyBreak],

'xd': [60]

}

buy_factors_product = ABuGridHelper.gen_factor_grid(

ABuGridHelper.K_GEN_FACTOR_PARAMS_BUY, [buy_bk_factor_grid1, buy_bk_factor_grid2])

if show:

print('买入因子参数共有{}种组合方式'.format(len(buy_factors_product)))

print('买入因子组合形式为{}'.format(buy_factors_product))

return sell_factors_product, buy_factors_product

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_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_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_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)

"""

9.4 资金限制对度量的影响

如下内容不能使用沙盒环境, 建议对照阅读：

abu量化文档－第十九节 数据源

第20节 美股交易UMP决策

"""

def sample_94_1():

"""

9.4_1 下载市场中所有股票的6年数据,

如果没有运行过abu量化文档－第十九节 数据源：中使用腾讯数据源进行数据更新，需要运行

如果运行过就不要重复运行了：

"""

from abupy import EMarketTargetType, EMarketSourceType, EDataCacheType

# 关闭沙盒数据环境

abupy.env.disable_example_env_ipython()

abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx

abupy.env.g_data_cache_type = EDataCacheType.E_DATA_CACHE_CSV

# 首选这里预下载市场中所有股票的6年数据(做5年回测，需要预先下载6年数据)

abu.run_kl_update(start='2011-08-08', end='2017-08-08', market=EMarketTargetType.E_MARKET_TARGET_US)

def sample_94_2(from_cache=False):

"""

9.4_2 使用切割训练集测试集模式，且生成交易特征，回测训练集交易数据, mac pro顶配大概下面跑了4个小时

:return:

"""

# 关闭沙盒数据环境

abupy.env.disable_example_env_ipython()

from abupy import EMarketDataFetchMode

# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL

abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL

# 回测生成买入时刻特征

abupy.env.g_enable_ml_feature = True

# 回测将symbols切割分为训练集数据和测试集数据

abupy.env.g_enable_train_test_split = True

# 下面设置回测时切割训练集，测试集使用的切割比例参数，默认为10，即切割为10份，9份做为训练，1份做为测试，

# 由于美股股票数量多，所以切割分为4份，3份做为训练集，1份做为测试集

abupy.env.g_split_tt_n_folds = 4

from abupy import EStoreAbu

if from_cache:

abu_result_tuple = \

abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,

custom_name='train_us')

else:

# 初始化资金200万，资金管理依然使用默认atr

read_cash = 5000000

# 每笔交易的买入基数资金设置为万分之15

abupy.beta.atr.g_atr_pos_base = 0.0015

# 使用run_loop_back运行策略，因子使用和之前一样，

# choice_symbols=None为全市场回测，5年历史数据回测

# 不同电脑运行速度差异大，mac pro顶配大概下面跑了4小时

# choice_symbols=None为全市场回测，5年历史数据回测

abu_result_tuple, _ = abu.run_loop_back(read_cash,

buy_factors, sell_factors,

stock_pickers,

choice_symbols=None,

start='2012-08-08', end='2017-08-08')

# 把运行的结果保存在本地，以便之后分析回测使用，保存回测结果数据代码如下所示

abu.store_abu_result_tuple(abu_result_tuple, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,

custom_name='train_us')

print('abu_result_tuple.action_pd.deal.value_counts():\n', abu_result_tuple.action_pd.deal.value_counts())

metrics = AbuMetricsBase(*abu_result_tuple)

metrics.fit_metrics()

metrics.plot_returns_cmp(only_show_returns=True)

def sample_94_3(from_cache=False, show=True):

"""

9.4_3 使用切割好的测试数据集快，mac pro顶配大概下面跑了半个小时

:return:

"""

# 关闭沙盒数据环境

abupy.env.disable_example_env_ipython()

from abupy import EMarketDataFetchMode

# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL

abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL

abupy.env.g_enable_train_test_split = False

# 使用切割好的测试数据

abupy.env.g_enable_last_split_test = True

# 回测生成买入时刻特征

abupy.env.g_enable_ml_feature = True

from abupy import EStoreAbu

if from_cache:

abu_result_tuple_test = \

abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,

custom_name='test_us')

else:

read_cash = 5000000

abupy.beta.atr.g_atr_pos_base = 0.007

choice_symbols = None

abu_result_tuple_test, kl_pd_manager_test = abu.run_loop_back(read_cash,

buy_factors, sell_factors, stock_pickers,

choice_symbols=choice_symbols, start='2012-08-08',

end='2017-08-08')

abu.store_abu_result_tuple(abu_result_tuple_test, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,

custom_name='test_us')

print('abu_result_tuple_test.action_pd.deal.value_counts():\n', abu_result_tuple_test.action_pd.deal.value_counts())

metrics = AbuMetricsBase(*abu_result_tuple_test)

metrics.fit_metrics()

if show:

metrics.plot_returns_cmp(only_show_returns=True)

return metrics

def sample_94_4(from_cache=False):

"""

满仓乘数

9.4_4 《量化交易之路》中通过把初始资金扩大到非常大，但是每笔交易的买入基数却不增高，来使交易全部都成交，

再使用满仓乘数的示例，由于需要再次进行全市场回测，比较耗时。

下面直接示例通过AbuMetricsBase中的transform_to_full_rate_factor接口将之前的回测结果转换为使用大初始资金回测的结果

:return:

"""

metrics_test = sample_94_3(from_cache=True, show=False)

from abupy import EStoreAbu

if from_cache:

test_us_fr = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,

custom_name='test_us_full_rate')

# 本地读取后使用AbuMetricsBase构造度量对象，参数enable_stocks_full_rate_factor=True, 即使用满仓乘数

test_frm = AbuMetricsBase(test_us_fr.orders_pd, test_us_fr.action_pd, test_us_fr.capital, test_us_fr.benchmark,

enable_stocks_full_rate_factor=True)

test_frm.fit_metrics()

else:

test_frm = metrics_test.transform_to_full_rate_factor(n_process_kl=4, show=False)

# 转换后保存起来，下次直接读取，不用再转换了

from abupy import AbuResultTuple

test_us_fr = AbuResultTuple(test_frm.orders_pd, test_frm.action_pd, test_frm.capital, test_frm.benchmark)

abu.store_abu_result_tuple(test_us_fr, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,

custom_name='test_us_full_rate')

"""

使用test_frm进行度量结果可以看到所有交易都顺利成交了，策略买入成交比例:100.0000%，但资金利用率显然过低，

它导致基准收益曲线和策略收益曲线不在一个量级上，无法有效的进行对比

"""

AbuMetricsBase.show_general(test_frm.orders_pd,

test_frm.action_pd, test_frm.capital, test_frm.benchmark, only_show_returns=True)

"""转换出来的test_frm即是一个使用满仓乘数的度量对象，下面使用test_frm直接进行满仓度量即可"""

print(type(test_frm))

test_frm.plot_returns_cmp(only_show_returns=True)

# 如果不需要与基准进行对比，最简单的方式是使用plot_order_returns_cmp

metrics_test.plot_order_returns_cmp()

"""

其它市场的回测, A股市场回测全局设置

请阅读abu量化文档相关章节

"""

if __name__ == "__main__":

sample_91()

# sample_922()

# sample_931()

# sample_932()

# 耗时操作

# sample_933()

# sample_934()

# sample_935_1()

# sample_935_2()

# sample_94_1()

# sample_94_2()

# sample_94_2(from_cache=True)

# sample_94_3()

# sample_94_3(from_cache=True)

# sample_94_4()

# sample_94_4(from_cache=True)