440 lines
15 KiB
Python
440 lines
15 KiB
Python
# -*- encoding:utf-8 -*-
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from __future__ import print_function
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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 seaborn as sns
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warnings.filterwarnings('ignore')
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sns.set_context(rc={'figure.figsize': (14, 7)})
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"""
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第三章 量化工具——NumPy
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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_311():
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"""
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3.1.1 并行化思想
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:return:
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"""
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# 注意 * 3的操作被运行在每一个元素上
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np_list = np.ones(5) * 3
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print('np_list:', np_list)
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# 普通的列表把*3操作认为是整体性操作
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normal_list = [1, 1, 1, 1, 1] * 3
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print('normal_list:', normal_list, len(normal_list))
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# 200支股票
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stock_cnt = 200
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# 504个交易日
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view_days = 504
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# 生成服从正态分布：均值期望＝0，标准差＝1的序列
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stock_day_change = np.random.standard_normal((stock_cnt, view_days))
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try:
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# 使用沙盒数据，目的是和书中一样的数据环境，不需要注视掉
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stock_day_change = np.load('../gen/stock_day_change.npy')
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except Exception as e:
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print('../gen/stock_day_change.npy load error:{}'.format(e))
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def sample_312():
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"""
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3.1.2 初始化操作
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:return:
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"""
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np_list = np.arange(10000)
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# 100个0
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print('np.zeros(100):\n', np.zeros(100))
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# shape：3行2列 全是0
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print('np.zeros((3, 2):\n', np.zeros((3, 2)))
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# shape： 3行2列 全是1
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print('np.ones((3, 2):\n', np.ones((3, 2)))
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# shape：x=2, y=3, z=3 值随机
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print('np.empty((2, 3, 3):\n', np.empty((2, 3, 3)))
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# 初始化序列与np_list一样的shape，值全为1
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print('np.ones_like(np_list):\n', np.ones_like(np_list))
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# 初始化序列与np_list一样的shape，值全为0
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print('np.zeros_like(np_list):\n', np.zeros_like(np_list))
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# eye得到对角线全为1的单位矩阵
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print('np.eye(3):\n', np.eye(3))
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# 打印shape (200, 504) 200行504列
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print('stock_day_change.shape:', stock_day_change.shape)
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# 打印出第一支只股票，头五个交易日的涨跌幅情况
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print('stock_day_change[0:1, :5]:\n', stock_day_change[0:1, :5])
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"""
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3.1.3 索引选取和切片选
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"""
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# tmp = a
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tmp = stock_day_change[0:2, 0:5].copy()
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# a = b
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stock_day_change[0:2, 0:5] = stock_day_change[-2:, -5:]
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# b = tmp
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stock_day_change[-2:, -5:] = tmp
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def sample_313():
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"""
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3.1.3 索引选取和切片选
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:return:
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"""
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# 0:2第一，第二支股票，0:5头五个交易日的涨跌幅数据
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print('stock_day_change[0:2, 0:5]:\n', stock_day_change[0:2, 0:5])
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# -2:倒数一，第二支股票，-5:最后五个交易日的涨跌幅数据
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print('stock_day_change[-2:, -5:]:\n', stock_day_change[-2:, -5:])
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# view result
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print('[0:2, 0:5], [-2:, -5:]:\n', stock_day_change[0:2, 0:5], stock_day_change[-2:, -5:])
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def sample_314():
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"""
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3.1.4 数据转换与规整
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:return:
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"""
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print('stock_day_change[0:2, 0:5]:\n', stock_day_change[0:2, 0:5])
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print('[0:2, 0:5].astype(int):\n', stock_day_change[0:2, 0:5].astype(int))
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# 2代表保留两位小数
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print('around 2:\n', np.around(stock_day_change[0:2, 0:5], 2))
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# 使用copy目的是不修改原始序列
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tmp_test = stock_day_change[0:2, 0:5].copy()
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# 将第一个元素改成nan
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tmp_test[0][0] = np.nan
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print('tmp_test:\n', tmp_test)
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def sample_315():
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"""
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3.1.5 逻辑条件进行数据筛选
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:return:
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"""
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# 找出上述切片内涨幅超过0.5的股票时段, 通过输出结果你可以看到返回的是boolean的数组
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mask = stock_day_change[0:2, 0:5] > 0.5
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print('mask:\n', mask)
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tmp_test = stock_day_change[0:2, 0:5].copy()
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# 使用上述的mask数组筛选出符合条件的数组, 即中筛选mask中对应index值为True的
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print('tmp_test[mask]:\n', tmp_test[mask])
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tmp_test[tmp_test > 0.5] = 1
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print('tmp_test:\n', tmp_test)
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tmp_test = stock_day_change[-2:, -5:]
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print('tmp_test2:\n', tmp_test)
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print('tmp_test[(tmp_test > 1) | (tmp_test < -1)]:\n', tmp_test[(tmp_test > 1) | (tmp_test < -1)])
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# noinspection PyTypeChecker
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def sample_316():
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"""
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3.1.6 通用序列函数
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:return:
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"""
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# np.all判断序列中的所有元素是否全部是true, 即对bool序列进行与操作
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# 本例实际判断stock_day_change[0:2, 0:5]中是否全是上涨的
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print('np.all(stock_day_change[0:2, 0:5] > 0):\n', np.all(stock_day_change[0:2, 0:5] > 0))
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# np.any判断序列中是否有元素为true, 即对bool序列进行或操作
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# 本例实际判断stock_day_change[0:2, 0:5]中是至少有一个是上涨的
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print('np.any(stock_day_change[0:2, 0:5] > 0):\n', np.any(stock_day_change[0:2, 0:5] > 0))
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# 对两个序列对应的元素两两比较，maximum结果集取大,相对使用minimum为取小的结果集
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print('np.maximum(stock_day_change[0:2, 0:5], stock_day_change[-2:, -5:]):\n',
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np.maximum(stock_day_change[0:2, 0:5], stock_day_change[-2:, -5:]))
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change_int = stock_day_change[0:2, 0:5].astype(int)
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print('change_int:\n', change_int)
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# 序列中数值值唯一且不重复的值组成新的序列
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print('np.unique(change_int):\n', np.unique(change_int))
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# axis＝1
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print('np.diff(stock_day_change[0:2, 0:5]):\n', np.diff(stock_day_change[0:2, 0:5]))
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# 唯一区别 axis=0
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print('np.diff(stock_day_change[0:2, 0:5], axis=0):\n', np.diff(stock_day_change[0:2, 0:5], axis=0))
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tmp_test = stock_day_change[-2:, -5:]
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print('np.where(tmp_test > 0.5, 1, 0):\n', np.where(tmp_test > 0.5, 1, 0))
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print('np.where(tmp_test > 0.5, 1, tmp_test):\n', np.where(tmp_test > 0.5, 1, tmp_test))
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# 序列中的值大于0.5并且小于1的赋值为1，否则赋值为0
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print('np.where(np.logical_and(tmp_test > 0.5, tmp_test < 1), 1, 0):\n',
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np.where(np.logical_and(tmp_test > 0.5, tmp_test < 1), 1, 0))
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# 序列中的值大于0.5或者小于－0.5的赋值为1，否则赋值为0
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print('np.where(np.logical_or(tmp_test > 0.5, tmp_test < -0.5), 1, 0):\n',
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np.where(np.logical_or(tmp_test > 0.5, tmp_test < -0.5), 1, 0))
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"""
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3.1.7 数据本地序列化操作
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"""
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stock_day_change = np.load('../gen/stock_day_change.npy')
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np.save('../gen/stock_day_change', stock_day_change)
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"""
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3.2 统计概念与函数使用
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"""
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stock_day_change_four = stock_day_change[:4, :4]
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def sample_320():
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"""
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3.2.0 统计概念与函数使用
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:return:
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"""
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print('stock_day_change_four:\n', stock_day_change_four)
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def sample_321():
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"""
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3.2.1 统计基础函数使用
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:return:
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"""
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print('最大涨幅 {}'.format(np.max(stock_day_change_four, axis=1)))
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print('最大跌幅 {}'.format(np.min(stock_day_change_four, axis=1)))
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print('振幅幅度 {}'.format(np.std(stock_day_change_four, axis=1)))
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print('平均涨跌 {}'.format(np.mean(stock_day_change_four, axis=1)))
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print('最大涨幅 {}'.format(np.max(stock_day_change_four, axis=0)))
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print('最大涨幅股票{}'.format(np.argmax(stock_day_change_four, axis=0)))
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print('最大跌幅股票{}'.format(np.argmin(stock_day_change_four, axis=0)))
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print('最大跌幅 {}'.format(np.min(stock_day_change_four, axis=0)))
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print('振幅幅度 {}'.format(np.std(stock_day_change_four, axis=0)))
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print('平均涨跌 {}'.format(np.mean(stock_day_change_four, axis=0)))
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def sample_322():
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"""
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3.2.2 统计基础概念
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:return:
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"""
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a_investor = np.random.normal(loc=100, scale=50, size=(100, 1))
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b_investor = np.random.normal(loc=100, scale=20, size=(100, 1))
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# a交易者
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print('a交易者期望{0:.2f}元, 标准差{1:.2f}, 方差{2:.2f}'.format(
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a_investor.mean(), a_investor.std(), a_investor.var()))
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# b交易者
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print('b交易者期望{0:.2f}元, 标准差{1:.2f}, 方差{2:.2f}'.format(
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b_investor.mean(), b_investor.std(), b_investor.var()))
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# a交易者期望
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a_mean = a_investor.mean()
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# a交易者标注差
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a_std = a_investor.std()
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# 收益绘制曲线
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plt.plot(a_investor)
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# 水平直线 上线
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plt.axhline(a_mean + a_std, color='r')
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# 水平直线 均值期望线
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plt.axhline(a_mean, color='y')
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# 水平直线 下线
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plt.axhline(a_mean - a_std, color='g')
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plt.show()
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b_mean = b_investor.mean()
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b_std = b_investor.std()
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# b交易者收益绘制曲线
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plt.plot(b_investor)
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# 水平直线 上线
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plt.axhline(b_mean + b_std, color='r')
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# 水平直线 均值期望线
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plt.axhline(b_mean, color='y')
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# 水平直线 下线
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plt.axhline(b_mean - b_std, color='g')
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plt.show()
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def sample_331():
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"""
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3.3.1 正态分布基础概念
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:return:
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"""
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import scipy.stats as scs
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# 均值期望
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stock_mean = stock_day_change[0].mean()
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# 标准差
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stock_std = stock_day_change[0].std()
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print('股票0 mean均值期望:{:.3f}'.format(stock_mean))
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print('股票0 std振幅标准差:{:.3f}'.format(stock_std))
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# 绘制股票0的直方图
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plt.hist(stock_day_change[0], bins=50, normed=True)
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# linspace从股票0 最小值－> 最大值生成数据
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fit_linspace = np.linspace(stock_day_change[0].min(),
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stock_day_change[0].max())
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# 概率密度函数(PDF，probability density function)
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# 由均值，方差，来描述曲线，使用scipy.stats.norm.pdf生成拟合曲线
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pdf = scs.norm(stock_mean, stock_std).pdf(fit_linspace)
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print(pdf)
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# plot x, y
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plt.plot(fit_linspace, pdf, lw=2, c='r')
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plt.show()
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def sample_332():
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"""
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3.3.2 实例1：正态分布买入策略
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:return:
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"""
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# 保留后50天的随机数据作为策略验证数据
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keep_days = 50
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# 统计前454, 切片切出0-454day，view_days = 504
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stock_day_change_test = stock_day_change[:stock_cnt, 0:view_days - keep_days]
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# 打印出前454跌幅最大的三支，总跌幅通过np.sum计算，np.sort对结果排序
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print('np.sort(np.sum(stock_day_change_test, axis=1))[:3]:', np.sort(np.sum(stock_day_change_test, axis=1))[:3])
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# 使用np.argsort针对股票跌幅进行排序，返回序号，即符合买入条件的股票序号
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stock_lower_array = np.argsort(np.sum(stock_day_change_test, axis=1))[:3]
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# 输符合买入条件的股票序号
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print('stock_lower_array:', stock_lower_array)
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def show_buy_lower(p_stock_ind):
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"""
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:param p_stock_ind: 股票序号,即在stock_day_change中的位置
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:return:
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"""
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# 设置一个一行两列的可视化图表
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_, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 5))
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# view_days504 - keep_days50 = 454
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# 绘制前454天股票走势图，np.cumsum()：序列连续求和
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axs[0].plot(np.arange(0, view_days - keep_days),
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stock_day_change_test[p_stock_ind].cumsum())
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# [view_days504 - keep_days50 = 454 : view_days504]
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# 从第454天开始到504天的股票走势
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cs_buy = stock_day_change[p_stock_ind][
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view_days - keep_days:view_days].cumsum()
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# 绘制从第454天到504天股票走势图
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axs[1].plot(np.arange(view_days - keep_days, view_days), cs_buy)
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# 返回从第454天开始到第504天计算盈亏的盈亏序列的最后一个值
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return cs_buy[-1]
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# 最后输出的盈亏比例
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profit = 0
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# 跌幅最大的三支遍历序号
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for stock_ind in stock_lower_array:
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# profit即三支股票从第454天买入开始计算，直到最后一天的盈亏比例
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profit += show_buy_lower(stock_ind)
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plt.show()
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# str.format 支持{:.2f}形式保留两位小数
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print('买入第 {} 支股票，从第454个交易日开始持有盈亏:{:.2f}%'.format(
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stock_lower_array, profit))
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def sample_342():
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"""
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3.4.2 实例2：如何在交易中获取优势
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:return:
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"""
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# 设置100个赌徒
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gamblers = 100
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def casino(win_rate, win_once=1, loss_once=1, commission=0.01):
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"""
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赌场：简单设定每个赌徒一共有1000000一共想在赌场玩10000000次，
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但是你要是没钱了也别想玩了
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win_rate: 输赢的概率
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win_once: 每次赢的钱数
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loss_once: 每次输的钱数
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commission: 手续费这里简单的设置了0.01 1%
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"""
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my_money = 1000000
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play_cnt = 10000000
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commission = commission
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for _ in np.arange(0, play_cnt):
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# 使用伯努利分布根据win_rate来获取输赢
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w = np.random.binomial(1, win_rate)
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if w:
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# 赢了 +win_once
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my_money += win_once
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else:
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# 输了 -loss_once
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my_money -= loss_once
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# 手续费
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my_money -= commission
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if my_money <= 0:
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# 没钱就别玩了，不赊账
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break
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return my_money
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"""
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如果有numba使用numba进行加速, 这个加速效果非常明显，不使用numba非常非常非常慢
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"""
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import numba as nb
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casino = nb.jit(casino)
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print('heaven_moneys....')
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# 100个赌徒进场天堂赌场，胜率0.5，赔率1，还没手续费
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heaven_moneys = [casino(0.5, commission=0) for _ in
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np.arange(0, gamblers)]
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print('cheat_moneys....')
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# 100个赌徒进场开始，胜率0.4，赔率1，没手续费
|
||
cheat_moneys = [casino(0.4, commission=0) for _ in
|
||
np.arange(0, gamblers)]
|
||
|
||
print('commission_moneys....')
|
||
# 100个赌徒进场开始，胜率0.5，赔率1，手续费0.01
|
||
commission_moneys = [casino(0.5, commission=0.01) for _ in
|
||
np.arange(0, gamblers)]
|
||
|
||
print('casino(0.5, commission=0.01, win_once=1.02, loss_once=0.98.....')
|
||
# 100个赌徒进场开始，胜率0.5，赔率1.04，手续费0.01
|
||
f1_moneys = [casino(0.5, commission=0.01, win_once=1.02, loss_once=0.98)
|
||
for _ in np.arange(0, gamblers)]
|
||
|
||
print('casino(0.45, commission=0.01, win_once=1.02, loss_once=0.98.....')
|
||
# 100个赌徒进场开始，胜率0.45，赔率1.04，手续费0.01
|
||
f2_moneys = [casino(0.45, commission=0.01, win_once=1.02, loss_once=0.98)
|
||
for _ in np.arange(0, gamblers)]
|
||
|
||
_ = plt.hist(heaven_moneys, bins=30)
|
||
plt.show()
|
||
_ = plt.hist(cheat_moneys, bins=30)
|
||
plt.show()
|
||
_ = plt.hist(commission_moneys, bins=30)
|
||
plt.show()
|
||
_ = plt.hist(f1_moneys, bins=30)
|
||
plt.show()
|
||
_ = plt.hist(f2_moneys, bins=30)
|
||
plt.show()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sample_311()
|
||
# sample_312()
|
||
# sample_313()
|
||
# sample_314()
|
||
# sample_315()
|
||
# sample_316()
|
||
# sample_320()
|
||
# sample_321()
|
||
# sample_322()
|
||
# sample_331()
|
||
# sample_332()
|
||
# sample_342()
|