merge: 合并 wikillm 与 wikillm-git,全部内容入库(abuquant-src/OCR产物/images/IDEA.md)
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# -*- 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 numpy as np
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from sklearn import metrics
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import warnings
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import ast
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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 ml
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from abupy import AbuMetricsBase, EStoreAbu, abu
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from abupy import ABuMarketDrawing
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from abupy import AbuFactorBuyBreak
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from abupy import AbuFactorAtrNStop
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from abupy import AbuFactorPreAtrNStop
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from abupy import AbuFactorCloseAtrNStop
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from abupy import EMarketTargetType, EMarketDataFetchMode
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from abupy import AbuUmpMainDeg
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from abupy import AbuUmpMainJump
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from abupy import AbuUmpMainPrice
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from abupy import AbuUmpMainWave
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# 设置选股因子，None为不使用选股因子
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stock_pickers = None
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# 买入因子依然延用向上突破因子
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buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
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{'xd': 42, 'class': AbuFactorBuyBreak}]
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# 卖出因子继续使用上一章使用的因子
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sell_factors = [
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{'stop_loss_n': 1.0, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop},
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{'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},
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{'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
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]
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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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第11章 量化系统-机器学习•ABU
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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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* 因为需要全市场回测所以本章无法使用沙盒数据，《量化交易之路》中的原始示例使用的是美股市场，这里的示例改为使用A股市场。
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* 本节可以对照阅读abu量化文档第20-23节内容
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* 本节的基础是在abu量化文档中第20节内容完成运行后有A股训练集交易和A股测试集交易数据之后
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"""
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def load_abu_result_tuple():
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abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN
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abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
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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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abu_result_tuple_test = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
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custom_name='test_cn')
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metrics_train = AbuMetricsBase(*abu_result_tuple_train)
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metrics_train.fit_metrics()
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metrics_test = AbuMetricsBase(*abu_result_tuple_test)
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metrics_test.fit_metrics()
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return abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test
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def sample_110():
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abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test = load_abu_result_tuple()
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metrics_train.plot_returns_cmp(only_show_returns=True)
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metrics_test.plot_returns_cmp(only_show_returns=True)
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def sample_111():
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"""
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11.1 搜索引擎与量化交易
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请对照阅读ABU量化系统使用文档 ：第16节 UMP主裁交易决策 中相关内容
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:return:
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"""
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abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test = load_abu_result_tuple()
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orders_pd_train = abu_result_tuple_train.orders_pd
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# 选择失败的前20笔交易绘制交易快照
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# 这里只是示例，实战中根据需要挑选，rank或者其他方式
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plot_simple = orders_pd_train[orders_pd_train.profit_cg < 0][:20]
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# save=True保存在本地，文件保存在~/abu/data/save_png/中
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ABuMarketDrawing.plot_candle_from_order(plot_simple, save=True)
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"""
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11.2 主裁
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请对照阅读ABU量化系统使用文档 ：第15节 中相关内容
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"""
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def sample_112():
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"""
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11.2.1 角度主裁, 11.2.2 使用全局最优对分类簇集合进行筛选
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:return:
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"""
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abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test = load_abu_result_tuple()
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orders_pd_train = abu_result_tuple_train.orders_pd
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# 参数为orders_pd
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ump_deg = AbuUmpMainDeg(orders_pd_train)
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# df即由之前ump_main_make_xy生成的类df，表11-1所示
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print('ump_deg.fiter.df.head():\n', ump_deg.fiter.df.head())
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# 耗时操作，大概需要10几分钟，具体根据电脑性能，cpu情况
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_ = ump_deg.fit(brust_min=False)
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print('ump_deg.cprs:\n', ump_deg.cprs)
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max_failed_cluster = ump_deg.cprs.loc[ump_deg.cprs.lrs.argmax()]
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print('失败概率最大的分类簇{0}, 失败率为{1:.2f}%, 簇交易总数{2}, 簇平均交易获利{3:.2f}%'.format(
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ump_deg.cprs.lrs.argmax(), max_failed_cluster.lrs * 100, max_failed_cluster.lcs, max_failed_cluster.lms * 100))
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cpt = int(ump_deg.cprs.lrs.argmax().split('_')[0])
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print('cpt:\n', cpt)
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ump_deg.show_parse_rt(ump_deg.rts[cpt])
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max_failed_cluster_orders = ump_deg.nts[ump_deg.cprs.lrs.argmax()]
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print('max_failed_cluster_orders:\n', max_failed_cluster_orders)
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ml.show_orders_hist(max_failed_cluster_orders,
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['buy_deg_ang21', 'buy_deg_ang42', 'buy_deg_ang60', 'buy_deg_ang252'])
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print('分类簇中deg_ang60平均值为{0:.2f}'.format(
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max_failed_cluster_orders.buy_deg_ang60.mean()))
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print('分类簇中deg_ang21平均值为{0:.2f}'.format(
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max_failed_cluster_orders.buy_deg_ang21.mean()))
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print('分类簇中deg_ang42平均值为{0:.2f}'.format(
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max_failed_cluster_orders.buy_deg_ang42.mean()))
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print('分类簇中deg_ang252平均值为{0:.2f}'.format(
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max_failed_cluster_orders.buy_deg_ang252.mean()))
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ml.show_orders_hist(orders_pd_train, ['buy_deg_ang21', 'buy_deg_ang42', 'buy_deg_ang60', 'buy_deg_ang252'])
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print('训练数据集中deg_ang60平均值为{0:.2f}'.format(
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orders_pd_train.buy_deg_ang60.mean()))
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print('训练数据集中deg_ang21平均值为{0:.2f}'.format(
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orders_pd_train.buy_deg_ang21.mean()))
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print('训练数据集中deg_ang42平均值为{0:.2f}'.format(
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orders_pd_train.buy_deg_ang42.mean()))
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print('训练数据集中deg_ang252平均值为{0:.2f}'.format(
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orders_pd_train.buy_deg_ang252.mean()))
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"""
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11.2.2 使用全局最优对分类簇集合进行筛选
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"""
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brust_min = ump_deg.brust_min()
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print('brust_min:', brust_min)
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llps = ump_deg.cprs[(ump_deg.cprs['lps'] <= brust_min[0]) & (ump_deg.cprs['lms'] <= brust_min[1]) & (
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ump_deg.cprs['lrs'] >= brust_min[2])]
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print('llps:\n', llps)
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print(ump_deg.choose_cprs_component(llps))
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ump_deg.dump_clf(llps)
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"""
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11.2.3 跳空主裁
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"""
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def sample_1123():
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"""
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11.2.3 跳空主裁
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:return:
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"""
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abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test = load_abu_result_tuple()
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orders_pd_train = abu_result_tuple_train.orders_pd
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ump_jump = AbuUmpMainJump.ump_main_clf_dump(orders_pd_train, save_order=False)
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print(ump_jump.fiter.df.head())
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print('失败概率最大的分类簇{0}'.format(ump_jump.cprs.lrs.argmax()))
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# 拿出跳空失败概率最大的分类簇
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max_failed_cluster_orders = ump_jump.nts[ump_jump.cprs.lrs.argmax()]
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# 显示失败概率最大的分类簇，表11-6所示
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print('max_failed_cluster_orders:\n', max_failed_cluster_orders)
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ml.show_orders_hist(max_failed_cluster_orders, feature_columns=['buy_diff_up_days', 'buy_jump_up_power',
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'buy_diff_down_days', 'buy_jump_down_power'])
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print('分类簇中jump_up_power平均值为{0:.2f}， 向上跳空平均天数{1:.2f}'.format(
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max_failed_cluster_orders.buy_jump_up_power.mean(), max_failed_cluster_orders.buy_diff_up_days.mean()))
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print('分类簇中jump_down_power平均值为{0:.2f}, 向下跳空平均天数{1:.2f}'.format(
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max_failed_cluster_orders.buy_jump_down_power.mean(), max_failed_cluster_orders.buy_diff_down_days.mean()))
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print('训练数据集中jump_up_power平均值为{0:.2f}，向上跳空平均天数{1:.2f}'.format(
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orders_pd_train.buy_jump_up_power.mean(), orders_pd_train.buy_diff_up_days.mean()))
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print('训练数据集中jump_down_power平均值为{0:.2f}, 向下跳空平均天数{1:.2f}'.format(
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orders_pd_train.buy_jump_down_power.mean(), orders_pd_train.buy_diff_down_days.mean()))
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"""
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11.2.4 价格主裁
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"""
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def sample_1124():
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"""
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11.2.4 价格主裁
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:return:
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"""
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abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test = load_abu_result_tuple()
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orders_pd_train = abu_result_tuple_train.orders_pd
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ump_price = AbuUmpMainPrice.ump_main_clf_dump(orders_pd_train, save_order=False)
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print('ump_price.fiter.df.head():\n', ump_price.fiter.df.head())
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print('失败概率最大的分类簇{0}'.format(ump_price.cprs.lrs.argmax()))
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# 拿出价格失败概率最大的分类簇
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max_failed_cluster_orders = ump_price.nts[ump_price.cprs.lrs.argmax()]
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# 表11-8所示
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print('max_failed_cluster_orders:\n', max_failed_cluster_orders)
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"""
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11.2.5 波动主裁
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"""
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def sample_1125():
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"""
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11.2.5 波动主裁
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:return:
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"""
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abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test = load_abu_result_tuple()
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orders_pd_train = abu_result_tuple_train.orders_pd
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# 文件保存在~/abu/data/save_png/中
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ump_wave = AbuUmpMainWave.ump_main_clf_dump(orders_pd_train, save_order=True)
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print('ump_wave.fiter.df.head():\n', ump_wave.fiter.df.head())
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print('失败概率最大的分类簇{0}'.format(ump_wave.cprs.lrs.argmax()))
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# 拿出波动特征失败概率最大的分类簇
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max_failed_cluster_orders = ump_wave.nts[ump_wave.cprs.lrs.argmax()]
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# 表11-10所示
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print('max_failed_cluster_orders:\n', max_failed_cluster_orders)
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ml.show_orders_hist(max_failed_cluster_orders, feature_columns=['buy_wave_score1', 'buy_wave_score3'])
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print('分类簇中wave_score1平均值为{0:.2f}'.format(
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max_failed_cluster_orders.buy_wave_score1.mean()))
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print('分类簇中wave_score3平均值为{0:.2f}'.format(
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max_failed_cluster_orders.buy_wave_score3.mean()))
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ml.show_orders_hist(orders_pd_train, feature_columns=['buy_wave_score1', 'buy_wave_score1'])
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print('训练数据集中wave_score1平均值为{0:.2f}'.format(
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orders_pd_train.buy_wave_score1.mean()))
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print('训练数据集中wave_score3平均值为{0:.2f}'.format(
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orders_pd_train.buy_wave_score1.mean()))
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"""
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11.2.6 验证主裁是否称职
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请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容
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"""
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def sample_1126():
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"""
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11.2.6 验证主裁是否称职
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:return:
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"""
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"""
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需要有运行之前的代码即有本地化后的裁判，然后通过如下代码直接加载
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"""
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ump_deg = AbuUmpMainDeg(predict=True)
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ump_jump = AbuUmpMainJump(predict=True)
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ump_price = AbuUmpMainPrice(predict=True)
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ump_wave = AbuUmpMainWave(predict=True)
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def apply_ml_features_ump(order, predicter, need_hit_cnt):
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if not isinstance(order.ml_features, dict):
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# 低版本pandas dict对象取出来会成为str
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ml_features = ast.literal_eval(order.ml_features)
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else:
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ml_features = order.ml_features
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return predicter.predict_kwargs(need_hit_cnt=need_hit_cnt, **ml_features)
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abu_result_tuple_train, abu_result_tuple_test, metrics_train, metrics_test = load_abu_result_tuple()
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# 选取有交易结果的数据order_has_result
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order_has_result = abu_result_tuple_test.orders_pd[abu_result_tuple_test.orders_pd.result != 0]
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# 角度主裁开始裁决
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order_has_result['ump_deg'] = order_has_result.apply(apply_ml_features_ump, axis=1, args=(ump_deg, 2,))
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# 跳空主裁开始裁决
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order_has_result['ump_jump'] = order_has_result.apply(apply_ml_features_ump, axis=1, args=(ump_jump, 2,))
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# 波动主裁开始裁决
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order_has_result['ump_wave'] = order_has_result.apply(apply_ml_features_ump, axis=1, args=(ump_wave, 2,))
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# 价格主裁开始裁决
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order_has_result['ump_price'] = order_has_result.apply(apply_ml_features_ump, axis=1, args=(ump_price, 2,))
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block_pd = order_has_result.filter(regex='^ump_*')
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block_pd['sum_bk'] = block_pd.sum(axis=1)
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block_pd['result'] = order_has_result['result']
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block_pd = block_pd[block_pd.sum_bk > 0]
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print('四个裁判整体拦截正确率{:.2f}%'.format(
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block_pd[block_pd.result == -1].result.count() / block_pd.result.count() * 100))
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print('block_pd.tail():\n', block_pd.tail())
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def sub_ump_show(block_name):
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sub_block_pd = block_pd[(block_pd[block_name] == 1)]
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# 如果失败就正确 －1->1 1->0
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# noinspection PyTypeChecker
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sub_block_pd.result = np.where(sub_block_pd.result == -1, 1, 0)
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return metrics.accuracy_score(sub_block_pd[block_name], sub_block_pd.result)
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print('角度裁判拦截正确率{:.2f}%'.format(sub_ump_show('ump_deg') * 100))
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print('跳空裁判拦截正确率{:.2f}%'.format(sub_ump_show('ump_jump') * 100))
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print('波动裁判拦截正确率{:.2f}%'.format(sub_ump_show('ump_wave') * 100))
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print('价格裁判拦截正确率{:.2f}%'.format(sub_ump_show('ump_price') * 100))
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"""
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11.2.7 在abu系统中开启主裁拦截模式
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请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容
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"""
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"""
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11.3.1 角度边裁
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请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容
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11.3.2 价格边裁
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请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容
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11.3.3 波动边裁
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请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容
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11.3.4 综合边裁
|
||||
请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容
|
||||
|
||||
11.3.5 验证边裁是否称职
|
||||
|
||||
请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容
|
||||
|
||||
11.3.6 在abu系统中开启边裁拦截模式
|
||||
|
||||
请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容
|
||||
|
||||
"""
|
||||
|
||||
if __name__ == "__main__":
|
||||
sample_111()
|
||||
# sample_112()
|
||||
# sample_1123()
|
||||
# sample_1124()
|
||||
# sample_1125()
|
||||
# sample_1126()
|
||||
Reference in New Issue
Block a user