# -*- encoding:utf-8 -*-
from __future__ import print_function
import seaborn as sns
import warnings

# noinspection PyUnresolvedReferences
import abu_local_env
import abupy
from abupy import EStoreAbu, abu
from abupy import ABuSymbolPd
from abupy import tl
from abupy import nd
from abupy import ABuMarketDrawing

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

"""
 附录C-量化统计分析及指标应用

 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

 本节内容建议对照阅读abu量化文档:第13节 量化技术分析应用
"""


def sample_c1():
 """
 C.1 量化统计分析应用
 :return:
 """
 tsla_df = ABuSymbolPd.make_kl_df('usTSLA', n_folds=2)

 jumps = tl.jump.calc_jump(tsla_df)
 print('jumps:\n', jumps)

 # sw[0]代表非时间因素的jump_power,sw[1]代表时间加权因素的jump_power,当sw[0]=1时与非加权方式相同,具体实现请参考源代码
 filter_jumps = tl.jump.calc_jump_line_weight(tsla_df, sw=(0.5, 0.5))
 print('filter_jumps:\n', filter_jumps)

 # tl.wave.calc_wave_abs()函数可视化价格波动情况
 tl.wave.calc_wave_abs(tsla_df, xd=21, show=True)


"""
 C.2 量化技术指标应用: 对量化策略失败结果的人工分析
"""


def sample_c2():
 """
 C.2 量化技术指标应用: 对量化策略失败结果的人工分析
 :return:
 """
 abupy.env.disable_example_env_ipython()

 # 从之前章节的缓存中读取交易数据
 abu_result_tuple_train = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
 custom_name='train_cn')
 # 只筛选orders中有交易结果的单子
 has_result = abu_result_tuple_train.orders_pd[
 abu_result_tuple_train.orders_pd.result == -1]

 # 随便拿一个交易数据作为示例
 sample_order = has_result.ix[100]
 _ = ABuMarketDrawing.plot_candle_from_order(sample_order)

 nd.macd.plot_macd_from_order(sample_order, date_ext=252)
 nd.boll.plot_boll_from_order(has_result.ix[100], date_ext=252)
 nd.ma.plot_ma_from_order(has_result.ix[100], date_ext=252, time_period=[10, 20, 30, 60, 90, 120])


if __name__ == "__main__":
 sample_c1()
 # sample_c2()