406 lines
16 KiB
Python
406 lines
16 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 pandas as pd
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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 AbuFactorBuyBreak
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from abupy import AbuFactorSellBreak
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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 AbuBenchmark
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from abupy import AbuPickTimeWorker
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from abupy import AbuCapital
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from abupy import AbuKLManager
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from abupy import ABuTradeProxy
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from abupy import ABuTradeExecute
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from abupy import ABuPickTimeExecute
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from abupy import AbuMetricsBase
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from abupy import ABuMarket
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from abupy import AbuPickTimeMaster
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from abupy import ABuRegUtil
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from abupy import AbuPickRegressAngMinMax
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from abupy import AbuPickStockWorker
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from abupy import ABuPickStockExecute
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from abupy import AbuPickStockPriceMinMax
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from abupy import AbuPickStockMaster
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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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"""
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第八章 量化系统——开发
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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_811():
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"""
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8.1.1 买入因子的实现
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:return:
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"""
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# buy_factors 60日向上突破，42日向上突破两个因子
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buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
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{'xd': 42, 'class': AbuFactorBuyBreak}]
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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kl_pd_manager = AbuKLManager(benchmark, capital)
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# 获取TSLA的交易数据
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kl_pd = kl_pd_manager.get_pick_time_kl_pd('usTSLA')
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abu_worker = AbuPickTimeWorker(capital, kl_pd, benchmark, buy_factors, None)
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abu_worker.fit()
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orders_pd, action_pd, _ = ABuTradeProxy.trade_summary(abu_worker.orders, kl_pd, draw=True)
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ABuTradeExecute.apply_action_to_capital(capital, action_pd, kl_pd_manager)
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capital.capital_pd.capital_blance.plot()
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plt.show()
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def sample_812():
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"""
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8.1.2 卖出因子的实现
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:return:
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"""
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# 120天向下突破为卖出信号
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sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
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# 趋势跟踪策略止盈要大于止损设置值，这里0.5，3.0
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sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
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# 暴跌止损卖出因子形成dict
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sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
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# 保护止盈卖出因子组成dict
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sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
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# 四个卖出因子同时生效，组成sell_factors
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sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
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# buy_factors 60日向上突破，42日向上突破两个因子
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buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
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{'xd': 42, 'class': AbuFactorBuyBreak}]
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
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['usTSLA'], benchmark, buy_factors, sell_factors, capital, show=True)
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def sample_813():
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"""
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8.1.3 滑点买入卖出价格确定及策略实现
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:return:
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"""
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from abupy import AbuSlippageBuyBase
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# 修改g_open_down_rate的值为0.02
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g_open_down_rate = 0.02
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# noinspection PyClassHasNoInit
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class AbuSlippageBuyMean2(AbuSlippageBuyBase):
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def fit_price(self):
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if (self.kl_pd_buy.open / self.kl_pd_buy.pre_close) < (
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1 - g_open_down_rate):
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# 开盘下跌K_OPEN_DOWN_RATE以上，单子失效
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print(self.factor_name + 'open down threshold')
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return np.inf
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# 买入价格为当天均价
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self.buy_price = np.mean(
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[self.kl_pd_buy['high'], self.kl_pd_buy['low']])
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return self.buy_price
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# 只针对60使用AbuSlippageBuyMean2
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buy_factors2 = [{'slippage': AbuSlippageBuyMean2, 'xd': 60, 'class': AbuFactorBuyBreak},
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{'xd': 42, 'class': AbuFactorBuyBreak}]
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sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
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sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
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sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
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sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
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sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
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['usTSLA'], benchmark, buy_factors2, sell_factors, capital, show=True)
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def sample_814(show=True):
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"""
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8.1.4 对多支股票进行择时
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:return:
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"""
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sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
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sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
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sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
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sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
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sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
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benchmark = AbuBenchmark()
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buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
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{'xd': 42, 'class': AbuFactorBuyBreak}]
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choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS']
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capital = AbuCapital(1000000, benchmark)
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orders_pd, action_pd, all_fit_symbols_cnt = ABuPickTimeExecute.do_symbols_with_same_factors(choice_symbols,
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benchmark, buy_factors,
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sell_factors, capital,
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show=False)
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metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
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metrics.fit_metrics()
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if show:
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print('orders_pd[:10]:\n', orders_pd[:10].filter(
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['symbol', 'buy_price', 'buy_cnt', 'buy_factor', 'buy_pos', 'sell_date', 'sell_type_extra', 'sell_type',
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'profit']))
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print('action_pd[:10]:\n', action_pd[:10])
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metrics.plot_returns_cmp(only_show_returns=True)
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return metrics
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def sample_815():
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"""
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8.1.5 自定义仓位管理策略的实现
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:return:
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"""
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metrics = sample_814(False)
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print('\nmetrics.gains_mean:{}, -metrics.losses_mean:{}'.format(metrics.gains_mean, -metrics.losses_mean))
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from abupy import AbuKellyPosition
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# 42d使用AbuKellyPosition，60d仍然使用默认仓位管理类
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buy_factors2 = [{'xd': 60, 'class': AbuFactorBuyBreak},
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{'xd': 42, 'position': AbuKellyPosition, 'win_rate': metrics.win_rate,
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'gains_mean': metrics.gains_mean, 'losses_mean': -metrics.losses_mean,
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'class': AbuFactorBuyBreak}]
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sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
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sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
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sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
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sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
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sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
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benchmark = AbuBenchmark()
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choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS']
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capital = AbuCapital(1000000, benchmark)
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orders_pd, action_pd, all_fit_symbols_cnt = ABuPickTimeExecute.do_symbols_with_same_factors(choice_symbols,
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benchmark, buy_factors2,
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sell_factors, capital,
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show=False)
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print(orders_pd[:10].filter(['symbol', 'buy_cnt', 'buy_factor', 'buy_pos']))
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def sample_816():
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"""
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8.1.6 多支股票使用不同的因子进行择时
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:return:
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"""
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# 选定noah和sfun
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target_symbols = ['usSFUN', 'usNOAH']
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# 针对sfun只使用42d向上突破作为买入因子
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buy_factors_sfun = [{'xd': 42, 'class': AbuFactorBuyBreak}]
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# 针对sfun只使用60d向下突破作为卖出因子
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sell_factors_sfun = [{'xd': 60, 'class': AbuFactorSellBreak}]
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# 针对noah只使用21d向上突破作为买入因子
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buy_factors_noah = [{'xd': 21, 'class': AbuFactorBuyBreak}]
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# 针对noah只使用42d向下突破作为卖出因子
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sell_factors_noah = [{'xd': 42, 'class': AbuFactorSellBreak}]
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factor_dict = dict()
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# 构建SFUN独立的buy_factors，sell_factors的dict
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factor_dict['usSFUN'] = {'buy_factors': buy_factors_sfun, 'sell_factors': sell_factors_sfun}
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# 构建NOAH独立的buy_factors，sell_factors的dict
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factor_dict['usNOAH'] = {'buy_factors': buy_factors_noah, 'sell_factors': sell_factors_noah}
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# 初始化资金
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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# 使用do_symbols_with_diff_factors执行
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orders_pd, action_pd, all_fit_symbols = ABuPickTimeExecute.do_symbols_with_diff_factors(
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target_symbols, benchmark, factor_dict, capital)
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print('pd.crosstab(orders_pd.buy_factor, orders_pd.symbol):\n', pd.crosstab(orders_pd.buy_factor, orders_pd.symbol))
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def sample_817():
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"""
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8.1.7 使用并行来提升择时运行效率
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:return:
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"""
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# 要关闭沙盒数据环境，因为沙盒里就那几个股票的历史数据, 下面要随机做50个股票
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from abupy import EMarketSourceType
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abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
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abupy.env.disable_example_env_ipython()
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# 关闭沙盒后，首先基准要从非沙盒环境换取，否则数据对不齐，无法正常运行
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benchmark = AbuBenchmark()
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# 当传入choice_symbols为None时代表对整个市场的所有股票进行回测
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# noinspection PyUnusedLocal
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choice_symbols = None
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# 顺序获取市场后300支股票
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# noinspection PyUnusedLocal
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choice_symbols = ABuMarket.all_symbol()[-50:]
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# 随机获取300支股票
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choice_symbols = ABuMarket.choice_symbols(50)
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capital = AbuCapital(1000000, benchmark)
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sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
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sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
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sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
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sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
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sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
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buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
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{'xd': 42, 'class': AbuFactorBuyBreak}]
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orders_pd, action_pd, _ = AbuPickTimeMaster.do_symbols_with_same_factors_process(
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choice_symbols, benchmark, buy_factors, sell_factors,
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capital)
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metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
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metrics.fit_metrics()
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metrics.plot_returns_cmp(only_show_returns=True)
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abupy.env.enable_example_env_ipython()
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"""
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注意所有选股结果等等与书中的结果不一致，因为要控制沙盒数据体积小于50mb， 所以沙盒数据有些symbol只有两年多一点，与原始环境不一致，
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直接达不到选股的min_xd，所以这里其实可以`abupy.env.disable_example_env_ipython()`关闭沙盒环境，直接上真实数据。
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"""
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def sample_821_1():
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"""
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8.2.1_1 选股使用示例
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:return:
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"""
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# 选股条件threshold_ang_min=0.0, 即要求股票走势为向上上升趋势
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stock_pickers = [{'class': AbuPickRegressAngMinMax,
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'threshold_ang_min': 0.0, 'reversed': False}]
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# 从这几个股票里进行选股，只是为了演示方便
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# 一般的选股都会是数量比较多的情况比如全市场股票
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choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
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'usTSLA', 'usWUBA', 'usVIPS']
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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kl_pd_manager = AbuKLManager(benchmark, capital)
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stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager,
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choice_symbols=choice_symbols,
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stock_pickers=stock_pickers)
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stock_pick.fit()
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# 打印最后的选股结果
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print('stock_pick.choice_symbols:', stock_pick.choice_symbols)
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# 从kl_pd_manager缓存中获取选股走势数据，注意get_pick_stock_kl_pd为选股数据，get_pick_time_kl_pd为择时
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kl_pd_noah = kl_pd_manager.get_pick_stock_kl_pd('usNOAH')
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# 绘制并计算角度
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deg = ABuRegUtil.calc_regress_deg(kl_pd_noah.close)
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print('noah 选股周期内角度={}'.format(round(deg, 3)))
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def sample_821_2():
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"""
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8.2.1_2 ABuPickStockExecute
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:return:
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"""
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stock_pickers = [{'class': AbuPickRegressAngMinMax,
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'threshold_ang_min': 0.0, 'threshold_ang_max': 10.0,
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'reversed': False}]
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choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
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'usTSLA', 'usWUBA', 'usVIPS']
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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kl_pd_manager = AbuKLManager(benchmark, capital)
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print('ABuPickStockExecute.do_pick_stock_work:\n', ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark,
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capital, stock_pickers))
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kl_pd_sfun = kl_pd_manager.get_pick_stock_kl_pd('usSFUN')
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print('sfun 选股周期内角度={}'.format(round(ABuRegUtil.calc_regress_deg(kl_pd_sfun.close), 3)))
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def sample_821_3():
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"""
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8.2.1_3 reversed
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:return:
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"""
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# 和上面的代码唯一的区别就是reversed=True
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stock_pickers = [{'class': AbuPickRegressAngMinMax,
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'threshold_ang_min': 0.0, 'threshold_ang_max': 10.0, 'reversed': True}]
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choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
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'usTSLA', 'usWUBA', 'usVIPS']
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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print('ABuPickStockExecute.do_pick_stock_work:\n',
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ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers))
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def sample_822():
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"""
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8.2.2 多个选股因子并行执行
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:return:
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"""
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# 选股list使用两个不同的选股因子组合，并行同时生效
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stock_pickers = [{'class': AbuPickRegressAngMinMax,
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'threshold_ang_min': 0.0, 'reversed': False},
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{'class': AbuPickStockPriceMinMax, 'threshold_price_min': 50.0,
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'reversed': False}]
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choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
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'usTSLA', 'usWUBA', 'usVIPS']
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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print('ABuPickStockExecute.do_pick_stock_work:\n',
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ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers))
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def sample_823():
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"""
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8.2.3 使用并行来提升回测运行效率
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:return:
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"""
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from abupy import EMarketSourceType
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abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
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abupy.env.disable_example_env_ipython()
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benchmark = AbuBenchmark()
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capital = AbuCapital(1000000, benchmark)
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# 首先随抽取50支股票
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choice_symbols = ABuMarket.choice_symbols(50)
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# 股价在15-50之间
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stock_pickers = [
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{'class': AbuPickStockPriceMinMax, 'threshold_price_min': 15.0,
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'threshold_price_max': 50.0, 'reversed': False}]
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cs = AbuPickStockMaster.do_pick_stock_with_process(capital, benchmark,
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stock_pickers,
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choice_symbols)
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print('len(cs):', len(cs))
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print('cs:\n', cs)
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||
if __name__ == "__main__":
|
||
sample_811()
|
||
# sample_812()
|
||
# sample_813()
|
||
# sample_814()
|
||
# sample_815()
|
||
# sample_816()
|
||
# sample_817()
|
||
|
||
# sample_821_1()
|
||
# sample_821_2()
|
||
# sample_821_3()
|
||
# sample_822()
|
||
# sample_823()
|