573 lines
21 KiB
Python
573 lines
21 KiB
Python
# -*- encoding:utf-8 -*-
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from __future__ import print_function
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from __future__ import division
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# import warnings
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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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 ABuSymbolPd
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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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kl_pd = ABuSymbolPd.make_kl_df('usTSLA', n_folds=2)
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"""
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第七章 量化系统——入门：三只小猪股票投资的故事
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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_711():
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"""
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7.1.1 趋势跟踪和均值回复的周期重叠性
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:return:
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"""
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sns.set_context(rc={'figure.figsize': (14, 7)})
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sns.regplot(x=np.arange(0, kl_pd.shape[0]), y=kl_pd.close.values, marker='+')
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plt.show()
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from abupy import ABuRegUtil
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deg = ABuRegUtil.calc_regress_deg(kl_pd.close.values)
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plt.show()
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print('趋势角度:' + str(deg))
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start = 0
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# 前1/4的数据
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end = int(kl_pd.shape[0] / 4)
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# 将x也使用arange切割
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x = np.arange(start, end)
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# y根据start，end进行切片
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y = kl_pd.close.values[start:end]
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sns.regplot(x=x, y=y, marker='+')
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plt.show()
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start = int(kl_pd.shape[0] / 4)
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# 向前推1/4单位个时间
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end = start + int(kl_pd.shape[0] / 4)
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sns.regplot(x=np.arange(start, end), y=kl_pd.close.values[start:end],
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marker='+')
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plt.show()
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def sample_712_1():
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"""
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7.1.2 均值回复策略
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:return:
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"""
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# 头一年（[:252]）作为训练数据, 美股交易中一年的交易日有252天
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train_kl = kl_pd[:252]
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# 后一年（[252:]）作为回测数据
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test_kl = kl_pd[252:]
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# 分别画出两部分数据收盘价格曲线
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tmp_df = pd.DataFrame(
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np.array([train_kl.close.values, test_kl.close.values]).T,
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columns=['train', 'test'])
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tmp_df[['train', 'test']].plot(subplots=True, grid=True,
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figsize=(14, 7))
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plt.show()
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def sample_712_2(show=True):
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"""
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7.1.2 均值回复策略
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:return:
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"""
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train_kl = kl_pd[:252]
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test_kl = kl_pd[252:]
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# 训练数据的收盘价格均值
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close_mean = train_kl.close.mean()
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# 训练数据的收盘价格标准差
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close_std = train_kl.close.std()
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# 构造卖出信号阀值
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sell_signal = close_mean + close_std / 3
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# 构造买入信号阀值
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buy_signal = close_mean - close_std / 3
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# 可视化训练数据的卖出信号阀值，买入信号阀值及均值线
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if show:
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# 训练集收盘价格可视化
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train_kl.close.plot()
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# 水平线，买入信号线, lw代表线的粗度
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plt.axhline(buy_signal, color='r', lw=3)
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# 水平线，均值线
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plt.axhline(close_mean, color='black', lw=1)
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# 水平线， 卖出信号线
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plt.axhline(sell_signal, color='g', lw=3)
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plt.legend(['train close', 'buy_signal', 'close_mean', 'sell_signal'],
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loc='best')
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plt.show()
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# 将卖出信号阀值，买入信号阀值代入回归测试数据可视化
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plt.figure(figsize=(14, 7))
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# 测试集收盘价格可视化
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test_kl.close.plot()
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# buy_signal直接代入买入信号
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plt.axhline(buy_signal, color='r', lw=3)
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# 直接代入训练集均值close
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plt.axhline(close_mean, color='black', lw=1)
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# sell_signal直接代入卖出信号
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plt.axhline(sell_signal, color='g', lw=3)
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# 按照上述绘制顺序标注
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plt.legend(['test close', 'buy_signal', 'close_mean', 'sell_signal'],
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loc='best')
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plt.show()
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print('买入信号阀值:{} 卖出信号阀值:{}'.format(buy_signal, sell_signal))
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return train_kl, test_kl, buy_signal, sell_signal
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def sample_712_3(show=True):
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"""
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7.1.2 均值回复策略
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:return:
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"""
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train_kl, test_kl, buy_signal, sell_signal = sample_712_2(show=False)
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# 寻找测试数据中满足买入条件的时间序列
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buy_index = test_kl[test_kl['close'] <= buy_signal].index
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# 将找到的买入时间系列的信号设置为1，代表买入操作
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test_kl.loc[buy_index, 'signal'] = 1
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# 表7-2所示
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if show:
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print('test_kl[52:57]:\n', test_kl[52:57])
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# 寻找测试数据中满足卖出条件的时间序列
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sell_index = test_kl[test_kl['close'] >= sell_signal].index
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# 将找到的卖出时间系列的信号设置为0，代表卖出操作
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test_kl.loc[sell_index, 'signal'] = 0
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# 表7-3所示
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if show:
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print('test_kl[48:53]:\n', test_kl[48:53])
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# 由于假设都是全仓操作所以signal＝keep，即1代表买入持有，0代表卖出空仓
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test_kl['keep'] = test_kl['signal']
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# 将keep列中的nan使用向下填充的方式填充，结果使keep可以代表最终的交易持股状态
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test_kl['keep'].fillna(method='ffill', inplace=True)
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# shift(1)及np.log下面会有内容详细讲解
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test_kl['benchmark_profit'] = \
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np.log(test_kl['close'] / test_kl['close'].shift(1))
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# 仅仅为了说明np.log的意义，添加了benchmark_profit2，只为对比数据是否一致
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test_kl['benchmark_profit2'] = \
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test_kl['close'] / test_kl['close'].shift(1) - 1
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if show:
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# 可视化对比两种方式计算出的profit是一致的
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test_kl[['benchmark_profit', 'benchmark_profit2']].plot(subplots=True,
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grid=True,
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figsize=(
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14, 7))
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plt.show()
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# test_kl['close'].shift(1): test_kl['close'] / test_kl['close'].shift(1) = 今日收盘价格序列／昨日收盘价格序列
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print('test_kl[close][:5]:\n', test_kl['close'][:5])
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print('test_kl[close].shift(1)[:5]:\n', test_kl['close'].shift(1)[:5])
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# np.log
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print('np.log(220 / 218), 220 / 218 - 1.0:', np.log(220 / 218), 220 / 218 - 1.0)
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return test_kl
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def sample_712_4():
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"""
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7.1.2 均值回复策略
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:return:
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"""
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test_kl = sample_712_3(show=False)
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test_kl['trend_profit'] = test_kl['keep'] * test_kl['benchmark_profit']
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test_kl['trend_profit'].plot(figsize=(14, 7))
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plt.show()
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test_kl[['benchmark_profit', 'trend_profit']].cumsum().plot(grid=True,
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figsize=(
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14, 7))
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plt.show()
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test_kl[['benchmark_profit', 'trend_profit']].cumsum().apply(
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np.exp).plot(grid=True)
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plt.show()
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# noinspection PyPep8Naming
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def sample_713():
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"""
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7.1.3 趋势跟踪策略
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:return:
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"""
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# rolling_max示例序列
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demo_list = np.array([1, 2, 1, 1, 100, 1000])
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# 对示例序列以3个为一组，寻找每一组中的最大值
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from abupy import pd_rolling_max
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# print('pd.rolling_max(demo_list, window=3):', pd.rolling_max(demo_list, window=3))
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print('pd.rolling_max(demo_list, window=3):', pd_rolling_max(demo_list, window=3))
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from abupy import pd_expanding_max
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# expanding_max示例序列
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demo_list = np.array([1, 2, 1, 1, 100, 1000])
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# print('pd.expanding_max(demo_list):', pd.expanding_max(demo_list))
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print('pd.expanding_max(demo_list):', pd_expanding_max(demo_list))
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# 当天收盘价格超过N1天内最高价格作为买入信号
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N1 = 42
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# 当天收盘价格超过N2天内最低价格作为卖出信号
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N2 = 21
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# 通过rolling_max方法计算最近N1个交易日的最高价
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# kl_pd['n1_high'] = pd.rolling_max(kl_pd['high'], window=N1)
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kl_pd['n1_high'] = pd_rolling_max(kl_pd['high'], window=N1)
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# 表7-4所示
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print('kl_pd[0:5]:\n', kl_pd[0:5])
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# expanding_max
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# expan_max = pd.expanding_max(kl_pd['close'])
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expan_max = pd_expanding_max(kl_pd['close'])
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# fillna使用序列对应的expan_max
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kl_pd['n1_high'].fillna(value=expan_max, inplace=True)
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# 表7-5所示
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print('kl_pd[0:5]:\n', kl_pd[0:5])
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from abupy import pd_rolling_min, pd_expanding_min
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# 通过rolling_min方法计算最近N2个交易日的最低价格
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# rolling_min与rolling_max类似
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# kl_pd['n2_low'] = pd.rolling_min(kl_pd['low'], window=N2)
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kl_pd['n2_low'] = pd_rolling_min(kl_pd['low'], window=N2)
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# expanding_min与expanding_max类似
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# expan_min = pd.expanding_min(kl_pd['close'])
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expan_min = pd_expanding_min(kl_pd['close'])
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# fillna使用序列对应的eexpan_min
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kl_pd['n2_low'].fillna(value=expan_min, inplace=True)
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# 当天收盘价格超过N天内的最高价或最低价, 超过最高价格作为买入信号买入股票持有
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buy_index = kl_pd[kl_pd['close'] > kl_pd['n1_high'].shift(1)].index
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kl_pd.loc[buy_index, 'signal'] = 1
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# 当天收盘价格超过N天内的最高价或最低价, 超过最低价格作为卖出信号
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sell_index = kl_pd[kl_pd['close'] < kl_pd['n2_low'].shift(1)].index
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kl_pd.loc[sell_index, 'signal'] = 0
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kl_pd.signal.value_counts().plot(kind='pie', figsize=(5, 5))
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plt.show()
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"""
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将信号操作序列移动一个单位，代表第二天再将操作信号执行，转换得到持股状态
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这里不shift(1)也可以，代表信号产生当天执行，但是由于收盘价格是在收盘后
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才确定的，计算突破使用了收盘价格，所以使用shift(1)更接近真实情况
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"""
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kl_pd['keep'] = kl_pd['signal'].shift(1)
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kl_pd['keep'].fillna(method='ffill', inplace=True)
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# 计算基准收益
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kl_pd['benchmark_profit'] = np.log(
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kl_pd['close'] / kl_pd['close'].shift(1))
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# 计算使用趋势突破策略的收益
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kl_pd['trend_profit'] = kl_pd['keep'] * kl_pd['benchmark_profit']
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# 可视化收益的情况对比
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kl_pd[['benchmark_profit', 'trend_profit']].cumsum().plot(grid=True,
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figsize=(
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14, 7))
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plt.show()
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"""
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7.2 仓位控制管理¶
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注意以下代码，由于有使用np.random.binomial进行随机，所以生成的数据结果与书中的会不一样
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"""
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def sample_722_1(show=True):
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"""
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7.2.2 一支股票的时间简史: 第一阶段
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:return:
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"""
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# 这个股票第一阶段走势函数gen_stock_price_array
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def gen_stock_price_array():
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# 第一阶段走势涵盖股票上市后前100天走势情况
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trade_day = 100
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# 股票的初始价格是1元钱，即初始化100个初始价格是1元钱的np array
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price_array = np.ones(trade_day)
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# 以时间驱动100个交易日，生成100个交易日走势
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for ind in np.arange(0, trade_day - 1):
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if ind == 0:
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# 第一个交易日50%的概率结果是win: win = np.random.binomial(1, 0.5)
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# 第一个交易日100%的概率win
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win = np.random.binomial(1, 1)
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else:
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# 非第一个交易日它的涨跌与只与前一天的涨跌相关,如果前一天是上涨的
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# 那么它今天仍然是涨，如果它前一天是下跌的，那它今天就是跌
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win = price_array[ind] > price_array[ind - 1]
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if win:
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# 每次上涨只能上涨5%
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price_array[ind + 1] = (1 + 0.05) * price_array[ind]
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else:
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# 每次下跌只能下跌5%
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price_array[ind + 1] = (1 - 0.05) * price_array[ind]
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return price_array
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# 运行两次，生成两种走势
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price_array1 = gen_stock_price_array()
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price_array1_ex = gen_stock_price_array()
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if show:
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_, axs = plt.subplots(nrows=1, ncols=2, figsize=(14, 5))
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# 图7-13 左图
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axs[0].plot(price_array1)
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# 图7-13 右图
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axs[1].plot(price_array1_ex)
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plt.show()
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return price_array1
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def sample_722_2(show=True):
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"""
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7.2.2 一支股票的时间简史: 第二阶段
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:return:
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"""
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price_array1 = sample_722_1(show=False)
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# 这个股票第二阶段走势函数gen_stock_price_array2
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# noinspection PyChainedComparisons
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def gen_stock_price_array2():
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# 第二阶段走势共覆盖了252个交易日，即一年的走势
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trade_day = 252
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# np.concatenate连结之前100天的走势和新的252天走势
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# np.ones(trade_day) * price_array1[-1]：即新的走势使用上一阶段走势最后
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# 一天的价格初始化这个252个交易日的新序列
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price_array = np.concatenate(
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(price_array1, np.ones(trade_day) * price_array1[-1]), axis=0)
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# concatenate操作之后：price_array有352个元素
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# len(price_array1) - 1：即ind 99开始时间驱动生成第二阶段的252个交易日
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for ind in np.arange(len(price_array1) - 1, len(price_array) - 1):
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# 获取当前交易日为基准的四个交易日数据
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last4 = price_array[ind - 3:ind + 1]
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if len(last4) == 4 and last4[-1] > last4[-2] and last4[-2] > last4[-3] and last4[-3] > last4[-4]:
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# 连续上涨3天, 第四及之后天下跌的概率为55%
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win = np.random.binomial(1, 0.45)
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elif len(last4) == 4 and last4[-1] < last4[-2] and last4[-2] < last4[-3] and last4[-3] < last4[-4]:
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# 连续下跌3天, 第四及之后天上涨的概率为80%
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win = np.random.binomial(1, 0.8)
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else:
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# 涨跌与只与前一天的涨跌相关，如果前一天是上涨的，
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# 那么它今天仍然是涨，如果它前一天是下跌的，那它今天就是跌
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win = price_array[ind] > price_array[ind - 1]
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if win:
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# 每次上涨只能上涨5%
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price_array[ind + 1] = (1 + 0.05) * price_array[ind]
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else:
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# 每次下跌只能下跌5%
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price_array[ind + 1] = (1 - 0.05) * price_array[ind]
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return price_array
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if show:
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import itertools
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# 生成9个子画布 3*3
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_, axs = plt.subplots(nrows=3, ncols=3, figsize=(15, 15))
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# 将 3 * 3转换成一个线性list
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axs_list = list(itertools.chain.from_iterable(axs))
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for ax in axs_list:
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# 使用gen_stock_price_array2生成9组不同的股票走势图,使用子画布绘制
|
||
ax.plot(gen_stock_price_array2())
|
||
plt.show()
|
||
|
||
price_array2 = gen_stock_price_array2()
|
||
if show:
|
||
plt.plot(price_array2)
|
||
plt.show()
|
||
|
||
return price_array2
|
||
|
||
|
||
def sample_722_3(show=True):
|
||
"""
|
||
7.2.2 一支股票的时间简史: 第三阶段
|
||
:return:
|
||
"""
|
||
price_array2 = sample_722_2(show=False)
|
||
|
||
# 这个股票第三阶段走势函数gen_stock_price_array3
|
||
def gen_stock_price_array3():
|
||
trade_day = 252 * 3
|
||
# np.concatenate连结之前352天的走势和新的交易日走势
|
||
# np.ones(trade_day) * price_array2[-1]：即新的走势使用上一阶段走势最后
|
||
# 一天的价格初始化len(trade_day)个交易日的新序列
|
||
price_array = np.concatenate(
|
||
(price_array2, np.ones(trade_day) * price_array2[-1]), axis=0)
|
||
|
||
# concatenate操作之后：price_array352+len(trade_day)个元素
|
||
# len(price_array2) - 1：即从ind 351开始时间驱动生成第三阶段的交易日数据
|
||
for ind in np.arange(len(price_array2) - 1, len(price_array) - 1):
|
||
# 获取当前交易日为基准的四个交易日数据
|
||
last4 = price_array[ind - 3:ind + 1]
|
||
# noinspection PyChainedComparisons
|
||
if len(last4) == 4 and last4[-1] >= last4[-2] \
|
||
and last4[-2] >= last4[-3] and last4[-3] >= last4[-4]:
|
||
# 连续上涨3天, 第四及之后天下跌的概率为55%
|
||
win = np.random.binomial(1, 0.45)
|
||
elif len(last4) == 4 and last4[-1] < last4[-2] \
|
||
and last4[-2] < last4[-3] and last4[-3] < last4[-4]:
|
||
|
||
# 连续下跌3天, 第四及之后上涨的概率为80%
|
||
win = np.random.binomial(1, 0.8)
|
||
if not win:
|
||
# 发生了灾难性的股价下跌，股价下跌50%
|
||
price_array[ind + 1] = (1 - 0.50) * price_array[ind]
|
||
# 直接continue了
|
||
continue
|
||
else:
|
||
# 涨跌与只与前一天的涨跌相关，如果前一天是上涨的
|
||
# 那么它今天仍然是涨，如果它前一天是下跌的，那它今天就是跌
|
||
win = price_array[ind] >= price_array[ind - 1]
|
||
|
||
if win:
|
||
# 每次上涨只能上涨5%
|
||
price_array[ind + 1] = (1 + 0.05) * price_array[ind]
|
||
else:
|
||
# 每次下跌只能下跌5%
|
||
price_array[ind + 1] = (1 - 0.05) * price_array[ind]
|
||
|
||
# 股价小于0.1元股价归0，即退市
|
||
if price_array[ind + 1] <= 0.1:
|
||
price_array[ind + 1:] = 0
|
||
# 退市
|
||
break
|
||
|
||
return price_array
|
||
|
||
# price_array3即为第三阶段股票走势
|
||
price_array3 = gen_stock_price_array3()
|
||
if show:
|
||
plt.plot(price_array3)
|
||
plt.show()
|
||
return price_array3
|
||
|
||
|
||
"""
|
||
7.2.3 三只小猪股票投资的故事
|
||
"""
|
||
|
||
|
||
def sample_723():
|
||
"""
|
||
7.2.3 三只小猪股票投资的故事
|
||
:return:
|
||
"""
|
||
price_array3 = sample_722_3(show=False)
|
||
|
||
# noinspection PyChainedComparisons,PyShadowingNames
|
||
def execute_trade(cash, buy_rate):
|
||
commission = 5 # 手续费
|
||
stock_cnt = 0 # 持有股票数
|
||
keep_day = 0 # 持股天数
|
||
# 资产结果序列
|
||
capital = []
|
||
# 从第353天开始，即从index 353开始直到最后一天
|
||
for ind in np.arange(352, len(price_array3) - 1):
|
||
if stock_cnt > 0:
|
||
# 如果持有股票，增加持股天数
|
||
keep_day += 1
|
||
if stock_cnt > 0 and keep_day == 3:
|
||
# 当连续持有股票三天后卖出股票
|
||
cash += price_array3[ind] * stock_cnt
|
||
cash -= commission # 手续费
|
||
if cash <= 0:
|
||
# 如果没钱了，一切就都结束了
|
||
capital.append(0)
|
||
print('爆仓了!')
|
||
break
|
||
# 卖出后重置持股天数和持有股票数量
|
||
keep_day = 0
|
||
stock_cnt = 0
|
||
|
||
# 获取当前交易日为基准5个交易日数据，5个交易日价格->4个交易日的涨跌情况
|
||
last5 = price_array3[ind - 4:ind + 1]
|
||
# 买入条件：
|
||
# example: last5 = [82.4 86.5 82.2 78.1 74.2]
|
||
# 1. 没持有股票：stock_cnt == 0
|
||
# 2. last5序列last5[1] > last5[0] 86.5 > 82.4, 即第一个交易日上涨
|
||
# 3. last5序列后三个交易日连续下跌[-1]<[-2],[-2]<[-3],[-3]<[-4]
|
||
if stock_cnt == 0 and len(last5) == 5 \
|
||
and last5[1] > last5[0] \
|
||
and last5[-1] < last5[-2] and last5[-2] < last5[-3] and last5[-3] < last5[-4]:
|
||
cash -= commission # 手续费
|
||
# 按照资金仓位管理buy_rate买入
|
||
buy_cash = (cash * buy_rate)
|
||
cash -= buy_cash
|
||
stock_cnt += buy_cash / price_array3[ind]
|
||
|
||
if stock_cnt < 1:
|
||
# 如果没钱了，一切就都结束了
|
||
capital.append(0)
|
||
print('爆仓了!')
|
||
break
|
||
keep_day = 0
|
||
|
||
# 资产结果序列加入当日结果
|
||
capital.append(cash + (stock_cnt * price_array3[ind]))
|
||
return capital
|
||
|
||
pig_one_cash = 10000
|
||
# 1.0全仓买入
|
||
buy_rate = 1.0
|
||
pig_one_capital = execute_trade(pig_one_cash, buy_rate)
|
||
print('猪老大最终资产：{}'.format(pig_one_capital[-1]))
|
||
print('猪老大资产最高峰值：{}'.format(max(pig_one_capital)))
|
||
plt.plot(pig_one_capital)
|
||
plt.show()
|
||
|
||
pig_two_cash = 10000
|
||
# fwin0.8 -floss0.2 = 0.6 60%仓位买入
|
||
buy_rate = 0.8 - 0.2
|
||
pig_two_capital = execute_trade(pig_two_cash, buy_rate)
|
||
print('猪老二最终资产：{}'.format(pig_two_capital[-1]))
|
||
print('猪老二资产最高峰值：{}'.format(max(pig_two_capital)))
|
||
plt.plot(pig_two_capital)
|
||
plt.show()
|
||
|
||
pig_three_cash = 10000
|
||
# 最终buy_rate＝0.13即13%仓位
|
||
buy_rate = 0.8 - 0.2 / (0.15 / 0.5)
|
||
pig_three_capital = execute_trade(pig_three_cash, buy_rate)
|
||
print('猪老三最终资产：{}'.format(pig_three_capital[-1]))
|
||
print('猪老三资产最高峰值：{}'.format(max(pig_three_capital)))
|
||
plt.plot(pig_three_capital)
|
||
plt.show()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sample_711()
|
||
# sample_712_1()
|
||
# sample_712_2()
|
||
# sample_712_3()
|
||
# sample_712_4()
|
||
# sample_713()
|
||
# sample_722_1()
|
||
# sample_722_2()
|
||
# sample_722_3()
|
||
# sample_723()
|