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extC.py
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# -*- 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()