77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
# -*- encoding:utf-8 -*-
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from __future__ import print_function
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import seaborn as sns
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import warnings
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# noinspection PyUnresolvedReferences
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import abu_local_env
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import abupy
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from abupy import EStoreAbu, abu
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from abupy import ABuSymbolPd
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from abupy import tl
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from abupy import nd
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from abupy import ABuMarketDrawing
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warnings.filterwarnings('ignore')
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sns.set_context(rc={'figure.figsize': (14, 7)})
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"""
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附录C-量化统计分析及指标应用
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abu量化系统github地址：https://github.com/bbfamily/abu (您的star是我的动力！)
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abu量化文档教程ipython notebook：https://github.com/bbfamily/abu/tree/master/abupy_lecture
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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
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"""
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def sample_c1():
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"""
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C.1 量化统计分析应用
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:return:
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"""
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tsla_df = ABuSymbolPd.make_kl_df('usTSLA', n_folds=2)
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jumps = tl.jump.calc_jump(tsla_df)
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print('jumps:\n', jumps)
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# 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
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filter_jumps = tl.jump.calc_jump_line_weight(tsla_df, sw=(0.5, 0.5))
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print('filter_jumps:\n', filter_jumps)
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# tl.wave.calc_wave_abs()函数可视化价格波动情况
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tl.wave.calc_wave_abs(tsla_df, xd=21, show=True)
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"""
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C.2 量化技术指标应用: 对量化策略失败结果的人工分析
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"""
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def sample_c2():
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"""
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C.2 量化技术指标应用: 对量化策略失败结果的人工分析
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:return:
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"""
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abupy.env.disable_example_env_ipython()
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# 从之前章节的缓存中读取交易数据
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abu_result_tuple_train = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
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custom_name='train_cn')
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# 只筛选orders中有交易结果的单子
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has_result = abu_result_tuple_train.orders_pd[
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abu_result_tuple_train.orders_pd.result == -1]
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# 随便拿一个交易数据作为示例
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sample_order = has_result.ix[100]
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_ = ABuMarketDrawing.plot_candle_from_order(sample_order)
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nd.macd.plot_macd_from_order(sample_order, date_ext=252)
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nd.boll.plot_boll_from_order(has_result.ix[100], date_ext=252)
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nd.ma.plot_ma_from_order(has_result.ix[100], date_ext=252, time_period=[10, 20, 30, 60, 90, 120])
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if __name__ == "__main__":
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sample_c1()
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# sample_c2()
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