488 lines
18 KiB
Python
488 lines
18 KiB
Python
# -*- encoding:utf-8 -*-
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from __future__ import print_function
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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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 AbuMetricsBase
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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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# run_loop_back等一些常用且最外层的方法定义在abu中
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from abupy import abu
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warnings.filterwarnings('ignore')
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sns.set_context(rc={'figure.figsize': (14, 7)})
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# 使用沙盒数据，目的是和书中一样的数据环境
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abupy.env.enable_example_env_ipython()
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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,
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'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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"""
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第九章 量化系统——度量与优化
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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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"""
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def sample_91(show=True):
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"""
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9.1 度量的基本使用方法
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:return:
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"""
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# 设置初始资金数
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read_cash = 1000000
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# 择时股票池
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choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
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'usTSLA', 'usWUBA', 'usVIPS']
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# 使用run_loop_back运行策略
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abu_result_tuple, kl_pd_manager = abu.run_loop_back(read_cash,
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buy_factors,
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sell_factors,
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stock_pickers,
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choice_symbols=choice_symbols, n_folds=2)
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metrics = AbuMetricsBase(*abu_result_tuple)
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metrics.fit_metrics()
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if show:
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metrics.plot_returns_cmp()
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return metrics
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def sample_922():
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"""
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9.2.2 度量的可视化
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:return:
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"""
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metrics = sample_91(show=False)
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metrics.plot_sharp_volatility_cmp()
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plt.show()
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def sharpe(rets, ann=252):
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return rets.mean() / rets.std() * np.sqrt(ann)
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print('策略sharpe值计算为＝{}'.format(sharpe(metrics.algorithm_returns)))
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metrics.plot_effect_mean_day()
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plt.show()
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metrics.plot_keep_days()
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plt.show()
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metrics.plot_sell_factors()
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plt.show()
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metrics.plot_max_draw_down()
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plt.show()
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"""
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9.3 基于grid search寻找因子最优参数
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"""
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stop_win_range = np.arange(2.0, 4.5, 0.5)
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stop_loss_range = np.arange(0.5, 2, 0.5)
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sell_atr_nstop_factor_grid = {
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'class': [AbuFactorAtrNStop],
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'stop_loss_n': stop_loss_range,
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'stop_win_n': stop_win_range
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}
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close_atr_range = np.arange(1.0, 4.0, 0.5)
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pre_atr_range = np.arange(1.0, 3.5, 0.5)
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sell_atr_pre_factor_grid = {
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'class': [AbuFactorPreAtrNStop],
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'pre_atr_n': pre_atr_range
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}
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sell_atr_close_factor_grid = {
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'class': [AbuFactorCloseAtrNStop],
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'close_atr_n': close_atr_range
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}
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def sample_931():
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"""
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9.3.1 参数取值范围
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:return:
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"""
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print('止盈参数stop_win_n设置范围:{}'.format(stop_win_range))
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print('止损参数stop_loss_n设置范围:{}'.format(stop_loss_range))
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print('暴跌保护止损参数pre_atr_n设置范围:{}'.format(pre_atr_range))
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print('盈利保护止盈参数close_atr_n设置范围:{}'.format(close_atr_range))
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def sample_932(show=True):
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"""
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9.3.2 参数进行排列组合
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:return:
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"""
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from abupy import ABuGridHelper
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sell_factors_product = ABuGridHelper.gen_factor_grid(
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ABuGridHelper.K_GEN_FACTOR_PARAMS_SELL,
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[sell_atr_nstop_factor_grid, sell_atr_pre_factor_grid, sell_atr_close_factor_grid])
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if show:
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print('卖出因子参数共有{}种组合方式'.format(len(sell_factors_product)))
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print('卖出因子组合0形式为{}'.format(sell_factors_product[0]))
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buy_bk_factor_grid1 = {
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'class': [AbuFactorBuyBreak],
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'xd': [42]
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}
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buy_bk_factor_grid2 = {
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'class': [AbuFactorBuyBreak],
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'xd': [60]
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}
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buy_factors_product = ABuGridHelper.gen_factor_grid(
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ABuGridHelper.K_GEN_FACTOR_PARAMS_BUY, [buy_bk_factor_grid1, buy_bk_factor_grid2])
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if show:
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print('买入因子参数共有{}种组合方式'.format(len(buy_factors_product)))
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print('买入因子组合形式为{}'.format(buy_factors_product))
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return sell_factors_product, buy_factors_product
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def sample_933():
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"""
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9.3.3 GridSearch寻找最优参数
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:return:
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"""
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from abupy import GridSearch
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read_cash = 1000000
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choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
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'usTSLA', 'usWUBA', 'usVIPS']
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sell_factors_product, buy_factors_product = sample_932(show=False)
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grid_search = GridSearch(read_cash, choice_symbols,
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buy_factors_product=buy_factors_product,
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sell_factors_product=sell_factors_product)
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from abupy import ABuFileUtil
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"""
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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
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"""
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# 运行GridSearch n_jobs=-1启动cpu个数的进程数
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scores, score_tuple_array = grid_search.fit(n_jobs=-1)
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"""
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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。
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"""
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ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')
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print('组合因子参数数量{}'.format(len(buy_factors_product) * len(sell_factors_product)))
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print('最终评分结果数量{}'.format(len(scores)))
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best_score_tuple_grid = grid_search.best_score_tuple_grid
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AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd, best_score_tuple_grid.action_pd,
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best_score_tuple_grid.capital, best_score_tuple_grid.benchmark)
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def sample_934():
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"""
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9.3.4 度量结果的评分
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:return:
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"""
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from abupy import ABuFileUtil
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score_fn = '../gen/score_tuple_array'
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if not ABuFileUtil.file_exist(score_fn):
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print('../gen/score_tuple_array not exist! please execute sample_933 first!')
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return
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"""
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直接读取本地序列化文件
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"""
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score_tuple_array = ABuFileUtil.load_pickle(score_fn)
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from abupy import WrsmScorer
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# 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
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scorer = WrsmScorer(score_tuple_array)
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print('scorer.score_pd.tail():\n', scorer.score_pd.tail())
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# score_tuple_array[658]与grid_search.best_score_tuple_grid是一致的
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sfs = scorer.fit_score()
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# 打印前15个高分组合
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print('sfs[::-1][:15]:\n', sfs[::-1][:15])
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def sample_935_1():
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"""
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9.3.5_1 不同权重的评分: 只考虑投资回报来评分
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:return:
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"""
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from abupy import ABuFileUtil
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score_fn = '../gen/score_tuple_array'
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if not ABuFileUtil.file_exist(score_fn):
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print('../gen/score_tuple_array not exist! please execute sample_933 first!')
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return
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"""
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直接读取本地序列化文件
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"""
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score_tuple_array = ABuFileUtil.load_pickle(score_fn)
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from abupy import WrsmScorer
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# 实例化WrsmScorer，参数weights，只有第二项为1，其他都是0，
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# 代表只考虑投资回报来评分
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scorer = WrsmScorer(score_tuple_array, weights=[0, 1, 0, 0])
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# 返回排序后的队列
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scorer_returns_max = scorer.fit_score()
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# 因为是倒序排序，所以index最后一个为最优参数
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best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
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# 由于篇幅，最优结果只打印文字信息
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AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
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best_score_tuple_grid.action_pd,
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best_score_tuple_grid.capital,
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best_score_tuple_grid.benchmark,
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only_info=True)
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# 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
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print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
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best_score_tuple_grid.sell_factors)
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def sample_935_2():
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"""
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9.3.5_2 不同权重的评分: 只考虑胜率
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:return:
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"""
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from abupy import ABuFileUtil
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score_fn = '../gen/score_tuple_array'
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if not ABuFileUtil.file_exist(score_fn):
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print('../gen/score_tuple_array not exist! please execute sample_933 first!')
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return
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"""
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直接读取本地序列化文件
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"""
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score_tuple_array = ABuFileUtil.load_pickle(score_fn)
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from abupy import WrsmScorer
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# 只有第一项为1，其他都是0代表只考虑胜率来评分
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scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])
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# 返回按照评分排序后的队列
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scorer_returns_max = scorer.fit_score()
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# index[-1]为最优参数序号
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best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]
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AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,
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best_score_tuple_grid.action_pd,
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best_score_tuple_grid.capital,
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best_score_tuple_grid.benchmark,
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only_info=False)
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# 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
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print('best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors:\n', best_score_tuple_grid.buy_factors,
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best_score_tuple_grid.sell_factors)
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"""
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9.4 资金限制对度量的影响
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如下内容不能使用沙盒环境, 建议对照阅读：
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abu量化文档－第十九节 数据源
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第20节 美股交易UMP决策
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"""
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def sample_94_1():
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"""
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9.4_1 下载市场中所有股票的6年数据,
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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
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如果运行过就不要重复运行了：
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"""
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from abupy import EMarketTargetType, EMarketSourceType, EDataCacheType
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# 关闭沙盒数据环境
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abupy.env.disable_example_env_ipython()
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abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
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abupy.env.g_data_cache_type = EDataCacheType.E_DATA_CACHE_CSV
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# 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)
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abu.run_kl_update(start='2011-08-08', end='2017-08-08', market=EMarketTargetType.E_MARKET_TARGET_US)
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def sample_94_2(from_cache=False):
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"""
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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
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:return:
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"""
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# 关闭沙盒数据环境
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abupy.env.disable_example_env_ipython()
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from abupy import EMarketDataFetchMode
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# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL
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abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
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# 回测生成买入时刻特征
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abupy.env.g_enable_ml_feature = True
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# 回测将symbols切割分为训练集数据和测试集数据
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abupy.env.g_enable_train_test_split = True
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# 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，
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# 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
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abupy.env.g_split_tt_n_folds = 4
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from abupy import EStoreAbu
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if from_cache:
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abu_result_tuple = \
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abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
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custom_name='train_us')
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else:
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# 初始化资金200万，资金管理依然使用默认atr
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read_cash = 5000000
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# 每笔交易的买入基数资金设置为万分之15
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abupy.beta.atr.g_atr_pos_base = 0.0015
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# 使用run_loop_back运行策略，因子使用和之前一样，
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# choice_symbols=None为全市场回测，5年历史数据回测
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# 不同电脑运行速度差异大，mac pro顶配大概下面跑了4小时
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# choice_symbols=None为全市场回测，5年历史数据回测
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abu_result_tuple, _ = abu.run_loop_back(read_cash,
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buy_factors, sell_factors,
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stock_pickers,
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choice_symbols=None,
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start='2012-08-08', end='2017-08-08')
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# 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
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abu.store_abu_result_tuple(abu_result_tuple, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
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custom_name='train_us')
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print('abu_result_tuple.action_pd.deal.value_counts():\n', abu_result_tuple.action_pd.deal.value_counts())
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metrics = AbuMetricsBase(*abu_result_tuple)
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metrics.fit_metrics()
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metrics.plot_returns_cmp(only_show_returns=True)
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def sample_94_3(from_cache=False, show=True):
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"""
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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
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:return:
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"""
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# 关闭沙盒数据环境
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abupy.env.disable_example_env_ipython()
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from abupy import EMarketDataFetchMode
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# 因为sample_94_1下载了预先数据，使用缓存，设置E_DATA_FETCH_FORCE_LOCAL
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abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
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abupy.env.g_enable_train_test_split = False
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# 使用切割好的测试数据
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abupy.env.g_enable_last_split_test = True
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# 回测生成买入时刻特征
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abupy.env.g_enable_ml_feature = True
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from abupy import EStoreAbu
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if from_cache:
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abu_result_tuple_test = \
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abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
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custom_name='test_us')
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else:
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read_cash = 5000000
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abupy.beta.atr.g_atr_pos_base = 0.007
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choice_symbols = None
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abu_result_tuple_test, kl_pd_manager_test = abu.run_loop_back(read_cash,
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buy_factors, sell_factors, stock_pickers,
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choice_symbols=choice_symbols, start='2012-08-08',
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end='2017-08-08')
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abu.store_abu_result_tuple(abu_result_tuple_test, n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME,
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custom_name='test_us')
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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)
|