327 lines
15 KiB
Python
327 lines
15 KiB
Python
# -*- encoding:utf-8 -*-
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"""策略验证模块"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import logging
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from itertools import chain
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import numpy as np
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import pandas as pd
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from ..SimilarBu.ABuSimilar import find_similar_with_folds
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from ..CoreBu import ABuEnv
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from ..CoreBu.ABuEnv import EMarketTargetType, EMarketDataFetchMode
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from ..MarketBu.ABuSymbol import IndexSymbol
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from ..MarketBu.ABuDataCheck import all_market_env_check
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from ..CoreBu.ABuEnvProcess import add_process_env_sig, AbuEnvProcess
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from ..UtilBu.ABuProgress import AbuMulPidProgress, AbuProgress
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from ..CoreBu.ABuParallel import delayed, Parallel
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from ..UtilBu import ABuProgress
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from ..TradeBu.ABuBenchmark import AbuBenchmark
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from ..TradeBu.ABuCapital import AbuCapital
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from ..AlphaBu.ABuPickTimeMaster import AbuPickTimeMaster
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from ..MetricsBu.ABuMetricsBase import AbuMetricsBase
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from ..SimilarBu.ABuSimilar import ECoreCorrType
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from ..MarketBu import ABuMarketDrawing
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__author__ = '阿布'
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__weixin__ = 'abu_quant'
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@add_process_env_sig
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def cross_val_mul_process(vc, cv, corr_series, benchmark, buy_factors, sell_factors, cash=10000000):
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"""
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:param vc: 多进程调度层分配的相关度范围 eg：(0.564, 1.0]
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:param cv: 交叉验证的数量级，eg：10，内部根据cv进行多次从相关度范围内随机抽取symbol进行回测
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:param corr_series: 多进程调度层传递的相关pd.Series对象，index为symbol，value为corr值
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:param benchmark: 交易基准对象，AbuBenchmark实例对象
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:param buy_factors: 买入因子组合
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:param sell_factors: 卖出因子组合
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:param cash: 初始化资金数(int)，默认10000000
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"""
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# 由于cross_val_mul_process以处于多任务运行环境，所以不内部不再启动多任务，使用1个进程择时
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n_process_pick_time = 1
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# 由于cross_val_mul_process以处于多任务运行环境，所以不内部不再启动多任务，使用1个进程数据收集
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n_process_kl = 1
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# 进程承接层使用chain.from_iterable摊开展平
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metrics_array = []
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"""
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根据多进程调度层分配的相关度范围对symbol进行筛选：
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eg：
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vc = (0.564, 1.0]
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vc.left = 0.564
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vc.right = 1.0
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"""
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symbol_vc = corr_series[(corr_series > vc.left) & (corr_series <= vc.right)].index
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with AbuMulPidProgress(cv, 'cross val progress') as progress:
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progress.display_step = 1
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for epoch in np.arange(0, cv):
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progress.show(epoch + 1)
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# 通过初始化资金数，交易基准对象构造资金管理对象capital
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capital = AbuCapital(cash, benchmark)
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# 在满足相关度范围的symbol_vc里面随机抽取cv个symbol
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choice_symbols = np.random.choice(symbol_vc, cv, replace=False)
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# 通过买入因子，卖出因子等进行择时操作
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orders_pd, action_pd, _ = AbuPickTimeMaster.do_symbols_with_same_factors_process(
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choice_symbols, benchmark,
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buy_factors, sell_factors, capital, n_process_kl=n_process_kl,
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n_process_pick_time=n_process_pick_time, show_progress=False)
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# 使用AbuMetricsBase对回测结果进行度量
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metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
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metrics.fit_metrics()
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"""
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度量结果添加到返回数组中添加的对象为tuple：
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tuple = (回测结果metrics对象，多进程调度层分配的相关度范围，本轮随机抽取的symbol)
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"""
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metrics_array.append((metrics, vc, choice_symbols))
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return metrics_array
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# noinspection PyProtectedMember
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class AbuCrossVal(object):
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"""对策略根据相关性进行交叉多次验证实现类"""
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def __init__(self, market=None, corr_type=ECoreCorrType.E_CORE_TYPE_PEARS):
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"""
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:param market: 进行验证的市场，默认None将使用env中设置的市场
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:param corr_type: 相关系数计算方法参数
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"""
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# None则服从ABuEnv.g_market_target市场设置
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self.market = ABuEnv.g_market_target if market is None else market
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# ipython notebook下使用logging.info
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self.log_func = logging.info if ABuEnv.g_is_ipython else print
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self.corr_type = corr_type
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def _find_or_cache_similar(self, n_folds, benchmark, enable_cache):
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"""
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根据是否有相关度数据缓存获取相关数据，通过hasattr查询类中是否有对应市场缓存，如果没有
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使用find_similar_with_folds获取相关数据通过setattr设置为类变量
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"""
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cache_similar_key = '{}_{}_similar_cache'.format(self.market.value, n_folds)
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if enable_cache and hasattr(self, cache_similar_key):
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# 查询是否存在类缓存
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self.log_func('load similar from cache！')
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return getattr(self, cache_similar_key)
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self.log_func('begin similar work...')
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# 使用find_similar_with_folds获取相关数据
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sorted_corr = find_similar_with_folds(benchmark, n_folds=n_folds, corr_type=self.corr_type)
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# 通过setattr设置为类变量
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setattr(self, cache_similar_key, sorted_corr)
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self.log_func('end similar work...')
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return sorted_corr
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def _do_cross_corr(self, buy_factors, sell_factors, benchmark, corr_series, cv, n_folds):
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"""
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多进程调度层：通过pd.qcut将相关数据corr_series切分成cv个
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相关范围段，启动cv个进程分别在每个进程中对相关范围段symbol
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进行回测，汇总多进程的执行结果使用chain.from_iterable将结果
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摊平
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:param buy_factors: 买入因子组合
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:param sell_factors: 卖出因子组合
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:param benchmark: 交易基准对象，AbuBenchmark实例对
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:param corr_series: 多进程调度层传递的相关pd.Series对象，index为symbol，value为corr值
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:param cv: 交叉验证的数量级，eg：10，内部根据cv进行多次从相关度范围内随机抽取symbol进行回测
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:param n_folds: 交叉验证的回测历史年数，需要确保本地有缓存对应的年数数据存在
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"""
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cats = pd.qcut(corr_series, cv)
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corr_vc = cats.value_counts()
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"""
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eg: corr_vc
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(0.564, 1.0] 551
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(0.486, 0.564] 551
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(0.427, 0.486] 551
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(0.377, 0.427] 551
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(0.321, 0.377] 551
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(0.268, 0.321] 551
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(0.201, 0.268] 551
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(0.128, 0.201] 551
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(0.0588, 0.128] 551
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(-0.984, 0.0588] 551
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"""
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# 回测历史时间周期设置只依赖标尺AbuBenchmark的构造时间长度
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benchmark = AbuBenchmark(benchmark, n_folds=n_folds)
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parallel = Parallel(
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n_jobs=cv, verbose=0, pre_dispatch='2*n_jobs')
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# 多任务环境下的内存环境拷贝对象AbuEnvProcess
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p_nev = AbuEnvProcess()
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out_cross_val = parallel(
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delayed(cross_val_mul_process)(vc, cv, corr_series, benchmark, buy_factors,
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sell_factors, env=p_nev)
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for vc in corr_vc.index)
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# 摊开多个子结果序列eg: ([], [], [], [])->[]
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self.metrics_array = list(chain.from_iterable(out_cross_val))
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self.out_cross_val = out_cross_val
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# 都完事时检测一下还有没有ui进度条
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ABuProgress.do_check_process_is_dead()
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# 输出验证整体结果
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self.show_cross_val_all()
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def fit(self, buy_factors, sell_factors, cv=10, market=None, enable_cache=True, n_folds=5):
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"""
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主执行函数：
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1. 首先计算全市场所有symbol与大盘指标的相关度
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2. 通过pd.qcut将相关数据corr_series切分成cv个相关范围段
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3. 启动cv个进程分别在每个进程中对相关范围段symbol进行回测
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4. 汇总多进程的执行结果使用chain.from_iterable将结果摊平
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:param buy_factors: 买入因子组合
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:param sell_factors: 卖出因子组合
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:param cv: cv默认为为10
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:param market: 进行验证的市场，默认None将使用env中设置的市场
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:param enable_cache: 是否重复使用相关度数据缓存
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:param n_folds: 交叉验证的回测历史年数，需要确保本地有缓存对应的年数数据存在
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"""
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if ABuEnv._g_enable_example_env_ipython:
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# 只支持非沙盒本地数据模式
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self.log_func('cross val only support local data, sandbox data now!')
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self.log_func('please use abupy.env.disable_example_env_ipython() close sandbox mode!')
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return
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# 相关性全市场数据监测
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if not all_market_env_check():
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return
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if market is not None:
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self.market = market
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if self.market == EMarketTargetType.E_MARKET_TARGET_US:
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# 美股
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benchmark = IndexSymbol.IXIC
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elif ABuEnv.g_market_target == EMarketTargetType.E_MARKET_TARGET_HK:
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# 港股
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benchmark = IndexSymbol.HSI
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elif ABuEnv.g_market_target == EMarketTargetType.E_MARKET_TARGET_CN:
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# a股
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benchmark = IndexSymbol.SH
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else:
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raise RuntimeError('cross val only support us, cn, hk market!')
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restore_market = ABuEnv.g_market_target
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# 临时切换市场，都完事后需要再切换回来
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ABuEnv.g_market_target = self.market
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# 需要强制走本地数据，体高效率以及对比度公正
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restore_date_mode = ABuEnv.g_data_fetch_mode
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# 临时强制走本地数据，都完事后需要再切换回来
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ABuEnv.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
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sorted_corr = self._find_or_cache_similar(n_folds, benchmark, enable_cache)
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"""
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eg:
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sorted_corr:
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[(us_NYSE:.IXIC, 0.99999999999999989),
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('usTQQQ', 0.98232461061643761),
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('usQQQ', 0.98189324977534143),
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('usVONG', 0.97282023230260084),
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('usDGRW', 0.92769329708593817),
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('usTDIV', 0.90129474970808454),
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('usVTHR', 0.89865579645001936),
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('usCFO', 0.89842456369113732),
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('usPNQI', 0.88093679204010267),
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('usTY', 0.86932098539827651)]
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corr_series:
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us.IXIC 1.0000
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usTQQQ 0.9823
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usQQQ 0.9819
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usONEQ 0.9795
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usVONG 0.9728
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usQQEW 0.9681
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usQQXT 0.9428
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usVONE 0.9389
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usDGRW 0.9277
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usSKYY 0.9211
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"""
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corr_series = pd.Series([corr[1] for corr in sorted_corr], index=[corr[0] for corr in sorted_corr])
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# 把第一个大盘symbol跳过
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corr_series = corr_series[1:]
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self._do_cross_corr(buy_factors, sell_factors, benchmark, corr_series, cv, n_folds)
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# 恢复之前的市场设置以及数据获取模式
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ABuEnv.g_market_target = restore_market
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ABuEnv.g_data_fetch_mode = restore_date_mode
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def plot_all_cross_val_orders(self):
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"""通过fit函数进行相关交叉验证后，将验证的所有交易单保存在本地"""
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with AbuProgress(len(self.metrics_array), 0, 'save and plot orders to png file') as progess:
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for index, metrics in enumerate(self.metrics_array):
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progess.show(index + 1)
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if metrics is not None and metrics[0].valid and metrics[0].orders_pd.shape[0] > 0:
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ABuMarketDrawing.plot_candle_from_order(metrics[0].orders_pd, save=True)
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self.log_func('all orders plot and save to png complete! path={}'.format(ABuMarketDrawing.save_dir_name()))
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def show_cross_val_se(self, start=0, end=1):
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"""
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显示通过fit函数进行验证后得到的metrics_array中的：
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1. 相关度范围段
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2. 相关度范围段随机抽取的symbol
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3. symbol对应的度量结果
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默认只显示一个，设置参数start， start的值调整显示数量和范围
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"""
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for metrics in self.metrics_array[start:end]:
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if metrics is not None and metrics[0].valid:
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self.log_func(u'回测symbol:{}'.format(metrics[2]))
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self.log_func(u'回测symbol与大盘相关度范围:{}'.format(metrics[1]))
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metrics[0].plot_order_returns_cmp()
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self.log_func('\n')
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def show_cross_val_all(self):
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"""
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显示通过fit函数进行验证后得到的metrics_array中所有交易的度量结果：
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统计所有交易数量，加权计算所有交易的
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1. 买入后卖出的交易总数量
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2. 胜率
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3. 平均获利期望
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4. 平均亏损期望
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5. 盈亏比
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6. 所有交易收益比例和
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7. 所有交易总盈亏和
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"""
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def _show_metrics(metrics_array, p_title):
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all_deal_cnt = 0
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all_win_rate = 0
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all_gains_mean = 0
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all_losses_mean = 0
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all_win_loss_profit_rate = 0
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all_profit_cg = 0
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all_profit = 0
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for metrics in metrics_array:
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metrics = metrics[0]
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if metrics is not None and metrics.valid:
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deal_cnt = metrics.order_has_ret.shape[0]
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all_deal_cnt += deal_cnt
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all_win_rate += metrics.win_rate * deal_cnt
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all_gains_mean += metrics.gains_mean * deal_cnt
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all_losses_mean += metrics.losses_mean * deal_cnt
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all_win_loss_profit_rate += metrics.win_loss_profit_rate * deal_cnt
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all_profit_cg += metrics.order_has_ret.profit_cg.sum()
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all_profit += metrics.all_profit
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self.log_func(p_title)
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self.log_func(u'买入后卖出的交易总数量:{}'.format(all_deal_cnt))
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self.log_func(u'胜率:{:.4f}%'.format(all_win_rate / all_deal_cnt * 100))
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self.log_func(u'平均获利期望:{:.4f}%'.format(all_gains_mean / all_deal_cnt * 100))
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self.log_func(u'平均亏损期望:{:.4f}%'.format(all_losses_mean / all_deal_cnt * 100))
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self.log_func(u'盈亏比:{:.4f}'.format(all_win_loss_profit_rate / all_deal_cnt))
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self.log_func(u'所有交易收益比例和:{:.4f} '.format(all_profit_cg))
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self.log_func(u'所有交易总盈亏和:{:.4f} '.format(all_profit))
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self.log_func('\n')
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_show_metrics(self.metrics_array, u'所有交叉验证交易度量结果如下：')
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for metrics_cv in self.out_cross_val:
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if len(metrics_cv) > 0:
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title = u'与大盘相关度范围:{}验证结果如下：'.format(metrics_cv[0][1])
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_show_metrics(metrics_cv, title)
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