754 lines
25 KiB
Python
754 lines
25 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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from scipy import stats
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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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from abupy import pd_rolling_std, pd_ewm_std, pd_rolling_mean
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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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tsla_df = ABuSymbolPd.make_kl_df('usTSLA', n_folds=2)
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"""
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第五章 量化工具——可视化
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abu量化系统github地址：https://github.com/bbfamily/abu (您的star是我的动力！)
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abu量化文档教程ipython notebook：https://github.com/bbfamily/abu/tree/master/abupy_lecture
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"""
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# noinspection PyUnresolvedReferences
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def plot_demo(axs=None, just_series=False):
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"""
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绘制tsla的收盘价格曲线
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:param axs: axs为子画布，稍后会详细讲解
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:param just_series: 是否只绘制一条收盘曲线使用series，后面会用到
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:return:
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"""
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# 如果参数传入子画布则使用子画布绘制，下一节会使用
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drawer = plt if axs is None else axs
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# Series对象tsla_df.close，红色
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drawer.plot(tsla_df.close, c='r')
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if not just_series:
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# 为曲线不重叠，y变量加了10个单位tsla_df.close.values + 10
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# numpy对象tsla_df.close.index ＋ tsla_df.close.values，绿色
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drawer.plot(tsla_df.close.index, tsla_df.close.values + 10,
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c='g')
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# 为曲线不重叠，y变量加了20个单位
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# list对象，numpy.tolist()将numpy对象转换为list对象，蓝色
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drawer.plot(tsla_df.close.index.tolist(),
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(tsla_df.close.values + 20).tolist(), c='b')
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plt.xlabel('time')
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plt.ylabel('close')
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plt.title('TSLA CLOSE')
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plt.grid(True)
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def sample_511():
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"""
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5.1.1 matplotlib可视化基础
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:return:
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"""
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print('tsla_df.tail():\n', tsla_df.tail())
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plot_demo()
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plt.show()
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def sample_512():
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"""
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5.1.2 matplotlib子画布及loc的使用
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:return:
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"""
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_, axs = plt.subplots(nrows=2, ncols=2, figsize=(14, 10))
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# 画布0，loc：0 plot_demo中传入画布，则使用传入的画布绘制
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drawer = axs[0][0]
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plot_demo(drawer)
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drawer.legend(['Series', 'Numpy', 'List'], loc=0)
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# 画布1，loc：1
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drawer = axs[0][1]
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plot_demo(drawer)
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drawer.legend(['Series', 'Numpy', 'List'], loc=1)
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# 画布2，loc：2
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drawer = axs[1][0]
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plot_demo(drawer)
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drawer.legend(['Series', 'Numpy', 'List'], loc=2)
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# 画布3，loc：2， 设置bbox_to_anchor，在画布外的相对位置绘制
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drawer = axs[1][1]
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plot_demo(drawer)
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drawer.legend(['Series', 'Numpy', 'List'], bbox_to_anchor=(1.05, 1),
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loc=2,
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borderaxespad=0.)
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plt.show()
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def sample_513():
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"""
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5.1.3 k线图的绘制
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:return:
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"""
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import matplotlib.finance as mpf
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__colorup__ = "red"
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__colordown__ = "green"
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# 为了示例清晰，只拿出前30天的交易数据绘制蜡烛图，
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tsla_part_df = tsla_df[:30]
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fig, ax = plt.subplots(figsize=(14, 7))
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qutotes = []
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for index, (d, o, c, h, l) in enumerate(
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zip(tsla_part_df.index, tsla_part_df.open, tsla_part_df.close,
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tsla_part_df.high, tsla_part_df.low)):
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# 蜡烛图的日期要使用matplotlib.finance.date2num进行转换为特有的数字值
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d = mpf.date2num(d)
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# 日期，开盘，收盘，最高，最低组成tuple对象val
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val = (d, o, c, h, l)
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# 加val加入qutotes
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qutotes.append(val)
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# 使用mpf.candlestick_ochl进行蜡烛绘制，ochl代表：open，close，high，low
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mpf.candlestick_ochl(ax, qutotes, width=0.6, colorup=__colorup__,
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colordown=__colordown__)
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ax.autoscale_view()
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ax.xaxis_date()
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plt.show()
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def sample_52():
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"""
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5.2 使用bokeh交互可视化
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:return:
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"""
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from abupy import ABuMarketDrawing
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ABuMarketDrawing.plot_candle_form_klpd(tsla_df, html_bk=True)
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"""
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5.3 使用pandas可视化数据
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"""
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def sample_531_1():
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"""
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5.3.1_1 绘制股票的收益，及收益波动情况 demo list
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:return:
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"""
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# 示例序列
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demo_list = np.array([2, 4, 16, 20])
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# 以三天为周期计算波动
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demo_window = 3
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# pd.rolling_std * np.sqrt
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print('pd.rolling_std(demo_list, window=demo_window, center=False) * np.sqrt(demo_window):\n',
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pd_rolling_std(demo_list, window=demo_window, center=False) * np.sqrt(demo_window))
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print('pd.Series([2, 4, 16]).std() * np.sqrt(demo_window):', pd.Series([2, 4, 16]).std() * np.sqrt(demo_window))
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print('pd.Series([4, 16, 20]).std() * np.sqrt(demo_window):', pd.Series([4, 16, 20]).std() * np.sqrt(demo_window))
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print('np.sqrt(pd.Series([2, 4, 16]).var() * demo_window):', np.sqrt(pd.Series([2, 4, 16]).var() * demo_window))
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def sample_531_2():
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"""
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5.3.1_2 绘制股票的收益，及收益波动情况
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:return:
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"""
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tsla_df_copy = tsla_df.copy()
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# 投资回报
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tsla_df_copy['return'] = np.log(tsla_df['close'] / tsla_df['close'].shift(1))
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# 移动收益标准差
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tsla_df_copy['mov_std'] = pd_rolling_std(tsla_df_copy['return'],
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window=20,
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center=False) * np.sqrt(20)
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# 加权移动收益标准差，与移动收益标准差基本相同，只不过根据时间权重计算std
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tsla_df_copy['std_ewm'] = pd_ewm_std(tsla_df_copy['return'], span=20,
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min_periods=20,
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adjust=True) * np.sqrt(20)
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tsla_df_copy[['close', 'mov_std', 'std_ewm', 'return']].plot(subplots=True, grid=True)
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plt.show()
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def sample_532():
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"""
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5.3.2 绘制股票的价格与均线
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:return:
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"""
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tsla_df.close.plot()
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# ma 30
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# pd_rolling_mean(tsla_df.close, window=30).plot()
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pd_rolling_mean(tsla_df.close, window=30).plot()
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# ma 60
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# pd.rolling_mean(tsla_df.close, window=60).plot()
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pd_rolling_mean(tsla_df.close, window=60).plot()
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# ma 90
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# pd.rolling_mean(tsla_df.close, window=90).plot()
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pd_rolling_mean(tsla_df.close, window=90).plot()
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# loc='best'即自动寻找适合的位置
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plt.legend(['close', '30 mv', '60 mv', '90 mv'], loc='best')
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plt.show()
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def sample_533():
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"""
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5.3.3 其它pandas统计图形种类
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:return:
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"""
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# iloc获取所有低开高走的下一个交易日组成low_to_high_df，由于是下一个交易日
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# 所以要对满足条件的交易日再次通过iloc获取，下一个交易日index用key.values + 1
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# key序列的值即为0-len(tsla_df), 即为交易日index，详情查阅本章初tail
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low_to_high_df = tsla_df.iloc[tsla_df[
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(tsla_df.close > tsla_df.open) & (
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tsla_df.key != tsla_df.shape[
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0] - 1)].key.values + 1]
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# 通过where将下一个交易日的涨跌幅通过ceil，floor向上，向下取整
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change_ceil_floor = np.where(low_to_high_df['p_change'] > 0,
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np.ceil(
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low_to_high_df['p_change']),
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np.floor(
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low_to_high_df['p_change']))
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# 使用pd.Series包裹，方便之后绘制
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change_ceil_floor = pd.Series(change_ceil_floor)
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print('低开高收的下一个交易日所有下跌的跌幅取整和sum: ' + str(
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change_ceil_floor[change_ceil_floor < 0].sum()))
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print('低开高收的下一个交易日所有上涨的涨幅取整和sum: ' + str(
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change_ceil_floor[change_ceil_floor > 0].sum()))
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# 2 * 2: 四张子图
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_, axs = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
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# 竖直柱状图，可以看到-1的柱子最高, 图5-7左上
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change_ceil_floor.value_counts().plot(kind='bar', ax=axs[0][0])
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# 水平柱状图，可以看到-1的柱子最长, 图5-7右上
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change_ceil_floor.value_counts().plot(kind='barh', ax=axs[0][1])
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# 概率密度图，可以看到向左偏移, 图5-7左下
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change_ceil_floor.value_counts().plot(kind='kde', ax=axs[1][0])
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# 圆饼图，可以看到－1所占的比例最高, -2的比例也大于＋2，图5-7右下
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change_ceil_floor.value_counts().plot(kind='pie', ax=axs[1][1])
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plt.show()
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def sample_54_1():
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"""
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5.4 使用seaborn可视化数据
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:return:
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"""
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sns.distplot(tsla_df['p_change'], bins=80)
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plt.show()
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sns.boxplot(x='date_week', y='p_change', data=tsla_df)
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plt.show()
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sns.jointplot(tsla_df['high'], tsla_df['low'])
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plt.show()
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def sample_54_2():
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"""
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5.4 使用seaborn可视化数据
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:return:
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"""
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change_df = pd.DataFrame({'tsla': tsla_df.p_change})
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# join usGOOG
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change_df = change_df.join(pd.DataFrame({'goog': ABuSymbolPd.make_kl_df('usGOOG', n_folds=2).p_change}),
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how='outer')
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# join usAAPL
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change_df = change_df.join(pd.DataFrame({'aapl': ABuSymbolPd.make_kl_df('usAAPL', n_folds=2).p_change}),
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how='outer')
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# join usFB
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change_df = change_df.join(pd.DataFrame({'fb': ABuSymbolPd.make_kl_df('usFB', n_folds=2).p_change}),
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how='outer')
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# join usBIDU
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change_df = change_df.join(pd.DataFrame({'bidu': ABuSymbolPd.make_kl_df('usBIDU', n_folds=2).p_change}),
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how='outer')
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change_df = change_df.dropna()
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# 表5-2所示
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print('change_df.head():\n', change_df.head())
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# 使用corr计算数据的相关性
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corr = change_df.corr()
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_, ax = plt.subplots(figsize=(8, 5))
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# sns.heatmap热力图展示每组股票涨跌幅的相关性
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sns.heatmap(corr, ax=ax)
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plt.show()
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"""
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5.5 实例1:可视化量化策略的交易区间，卖出原因
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"""
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def sample_55_1():
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"""
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5.5 可视化量化策略的交易区间，卖出原因
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:return:
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"""
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def plot_trade(buy_date, sell_date):
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# 找出2014-07-28对应时间序列中的index作为start
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start = tsla_df[tsla_df.index == buy_date].key.values[0]
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# 找出2014-09-05对应时间序列中的index作为end
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end = tsla_df[tsla_df.index == sell_date].key.values[0]
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# 使用5.1.1封装的绘制tsla收盘价格时间序列函数plot_demo
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# just_series＝True, 即只绘制一条曲线使用series数据
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plot_demo(just_series=True)
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# 将整个时间序列都填充一个底色blue，注意透明度alpha=0.08是为了
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# 之后标注其他区间透明度高于0.08就可以清楚显示
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plt.fill_between(tsla_df.index, 0, tsla_df['close'], color='blue',
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alpha=.08)
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# 标注股票持有周期绿色，使用start和end切片周期
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# 透明度alpha=0.38 > 0.08
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plt.fill_between(tsla_df.index[start:end], 0,
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tsla_df['close'][start:end], color='green',
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alpha=.38)
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# 设置y轴的显示范围，如果不设置ylim，将从0开始作为起点显示，效果不好
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plt.ylim(np.min(tsla_df['close']) - 5,
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np.max(tsla_df['close']) + 5)
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# 使用loc='best'
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plt.legend(['close'], loc='best')
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# 标注交易区间2014-07-28到2014-09-05, 图5-12所示
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plot_trade('2014-07-28', '2014-09-05')
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plt.show()
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def plot_trade_with_annotate(buy_date, sell_date, annotate):
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"""
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:param buy_date: 交易买入日期
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:param sell_date: 交易卖出日期
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:param annotate: 卖出原因
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:return:
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"""
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# 标注交易区间buy_date到sell_date
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plot_trade(buy_date, sell_date)
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# annotate文字，asof：从tsla_df['close']中找到index:sell_date对应值
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plt.annotate(annotate,
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xy=(sell_date, tsla_df['close'].asof(sell_date)),
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arrowprops=dict(facecolor='yellow'),
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horizontalalignment='left', verticalalignment='top')
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plot_trade_with_annotate('2014-07-28', '2014-09-05',
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'sell for stop loss')
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plt.show()
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def sample_55_2():
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"""
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5.5 可视化量化策略的交易区间，卖出原因
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:return:
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"""
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def plot_trade(buy_date, sell_date):
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# 找出2014-07-28对应时间序列中的index作为start
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start = tsla_df[tsla_df.index == buy_date].key.values[0]
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# 找出2014-09-05对应时间序列中的index作为end
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end = tsla_df[tsla_df.index == sell_date].key.values[0]
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# 使用5.1.1封装的绘制tsla收盘价格时间序列函数plot_demo
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# just_series＝True, 即只绘制一条曲线使用series数据
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plot_demo(just_series=True)
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# 将整个时间序列都填充一个底色blue，注意透明度alpha=0.08是为了
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# 之后标注其他区间透明度高于0.08就可以清楚显示
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plt.fill_between(tsla_df.index, 0, tsla_df['close'], color='blue',
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alpha=.08)
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# 标注股票持有周期绿色，使用start和end切片周期，透明度alpha=0.38 > 0.08
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if tsla_df['close'][end] < tsla_df['close'][start]:
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# 如果赔钱了显示绿色
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plt.fill_between(tsla_df.index[start:end], 0,
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tsla_df['close'][start:end], color='green',
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alpha=.38)
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is_win = False
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else:
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# 如果挣钱了显示红色
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plt.fill_between(tsla_df.index[start:end], 0,
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tsla_df['close'][start:end], color='red',
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alpha=.38)
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is_win = True
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# 设置y轴的显示范围，如果不设置ylim，将从0开始作为起点显示
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plt.ylim(np.min(tsla_df['close']) - 5,
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np.max(tsla_df['close']) + 5)
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# 使用loc='best'
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plt.legend(['close'], loc='best')
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# 将是否盈利结果返回
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return is_win
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def plot_trade_with_annotate(buy_date, sell_date):
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"""
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:param buy_date: 交易买入日期
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:param sell_date: 交易卖出日期
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:return:
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"""
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# 标注交易区间buy_date到sell_date
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is_win = plot_trade(buy_date, sell_date)
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# 根据is_win来判断是否显示止盈还是止损卖出
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plt.annotate(
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'sell for stop win' if is_win else 'sell for stop loss',
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xy=(sell_date, tsla_df['close'].asof(sell_date)),
|
||
arrowprops=dict(facecolor='yellow'),
|
||
horizontalalignment='left', verticalalignment='top')
|
||
|
||
# 区间2014-07-28到2014-09-05
|
||
plot_trade_with_annotate('2014-07-28', '2014-09-05')
|
||
# 区间2015-01-28到2015-03-11
|
||
plot_trade_with_annotate('2015-01-28', '2015-03-11')
|
||
# 区间2015-04-10到2015-07-10
|
||
plot_trade_with_annotate('2015-04-10', '2015-07-10')
|
||
# 区间2015-10-2到2015-10-14
|
||
plot_trade_with_annotate('2015-10-2', '2015-10-14')
|
||
# 区间2016-02-10到2016-04-11
|
||
plot_trade_with_annotate('2016-02-10', '2016-04-11')
|
||
plt.show()
|
||
|
||
|
||
"""
|
||
5.6 实例2:标准化两个股票的观察周期
|
||
"""
|
||
|
||
goog_df = ABuSymbolPd.make_kl_df('usGOOG', n_folds=2)
|
||
|
||
|
||
def plot_two_stock(tsla, goog, axs=None):
|
||
# 如果有传递子画布，使用子画布，否则plt
|
||
drawer = plt if axs is None else axs
|
||
# tsla red
|
||
drawer.plot(tsla, c='r')
|
||
# google greeen
|
||
drawer.plot(goog, c='g')
|
||
# 显示网格
|
||
drawer.grid(True)
|
||
# 图例标注
|
||
drawer.legend(['tsla', 'google'], loc='best')
|
||
|
||
|
||
def sample_56_1():
|
||
"""
|
||
5.6 标准化两个股票的观察周期
|
||
:return:
|
||
"""
|
||
# mean:打印均值，median：打印中位数
|
||
print(round(goog_df.close.mean(), 2), round(goog_df.close.median(), 2))
|
||
# 表5-3所示
|
||
print('goog_df.tail():\n', goog_df.tail())
|
||
|
||
plot_two_stock(tsla_df.close, goog_df.close)
|
||
plt.title('TSLA and Google CLOSE')
|
||
# x轴时间
|
||
plt.xlabel('time')
|
||
# y轴收盘价格
|
||
plt.ylabel('close')
|
||
plt.show()
|
||
|
||
|
||
def sample_56_2():
|
||
"""
|
||
5.6 标准化两个股票的观察周期
|
||
:return:
|
||
"""
|
||
|
||
# noinspection PyShadowingNames
|
||
def two_mean_list(one, two, type_look='look_max'):
|
||
"""
|
||
只针对俩个输入的均值归一化
|
||
:param one:
|
||
:param two:
|
||
:param type_look:
|
||
:return:
|
||
"""
|
||
one_mean = one.mean()
|
||
two_mean = two.mean()
|
||
if type_look == 'look_max':
|
||
"""
|
||
向较大的均值序列看齐
|
||
"""
|
||
one, two = (one, one_mean / two_mean * two) \
|
||
if one_mean > two_mean else (
|
||
one * two_mean / one_mean, two)
|
||
elif type_look == 'look_min':
|
||
"""
|
||
向较小的均值序列看齐
|
||
"""
|
||
one, two = (one * two_mean / one_mean, two) \
|
||
if one_mean > two_mean else (
|
||
one, two * one_mean / two_mean)
|
||
return one, two
|
||
|
||
def regular_std(group):
|
||
# z-score规范化也称零-均值规范化
|
||
return (group - group.mean()) / group.std()
|
||
|
||
def regular_mm(group):
|
||
# 最小-最大规范化
|
||
return (group - group.min()) / (group.max() - group.min())
|
||
|
||
# 2行2列，4个画布
|
||
_, axs = plt.subplots(nrows=2, ncols=2, figsize=(14, 10))
|
||
|
||
# 第一个regular_std, 如图5-16左上所示
|
||
drawer = axs[0][0]
|
||
plot_two_stock(regular_std(tsla_df.close), regular_std(goog_df.close),
|
||
drawer)
|
||
drawer.set_title('(group - group.mean()) / group.std()')
|
||
|
||
# 第二个regular_mm，如图5-16右上所示
|
||
drawer = axs[0][1]
|
||
plot_two_stock(regular_mm(tsla_df.close), regular_mm(goog_df.close),
|
||
drawer)
|
||
drawer.set_title(
|
||
'(group - group.min()) / (group.max() - group.min())')
|
||
|
||
# 第三个向较大的序列看齐，如图5-16左上所示
|
||
drawer = axs[1][0]
|
||
one, two = two_mean_list(tsla_df.close, goog_df.close,
|
||
type_look='look_max')
|
||
plot_two_stock(one, two, drawer)
|
||
drawer.set_title('two_mean_list type_look=look_max')
|
||
|
||
# 第四个向较小的序列看齐，如图5-16右下所示
|
||
drawer = axs[1][1]
|
||
one, two = two_mean_list(tsla_df.close, goog_df.close,
|
||
type_look='look_min')
|
||
plot_two_stock(one, two, drawer)
|
||
drawer.set_title('two_mean_list type_look=look_min')
|
||
plt.show()
|
||
|
||
|
||
def sample_56_3():
|
||
"""
|
||
5.6 标准化两个股票的观察周期
|
||
:return:
|
||
"""
|
||
_, ax1 = plt.subplots()
|
||
ax1.plot(tsla_df.close, c='r', label='tsla')
|
||
# 第一个ax的标注
|
||
ax1.legend(loc=2)
|
||
ax1.grid(False)
|
||
# 反向y轴 twinx
|
||
ax2 = ax1.twinx()
|
||
ax2.plot(goog_df.close, c='g', label='google')
|
||
# 第二个ax的标志
|
||
ax2.legend(loc=1)
|
||
plt.show()
|
||
|
||
|
||
# noinspection PyTypeChecker
|
||
def sample_571_1():
|
||
"""
|
||
5.7.1 黄金分割线的定义方式
|
||
:return:
|
||
"""
|
||
# 收盘价格序列中的最大值
|
||
cs_max = tsla_df.close.max()
|
||
# 收盘价格序列中的最小值
|
||
cs_min = tsla_df.close.min()
|
||
|
||
sp382 = (cs_max - cs_min) * 0.382 + cs_min
|
||
sp618 = (cs_max - cs_min) * 0.618 + cs_min
|
||
print('视觉上的382: ' + str(round(sp382, 2)))
|
||
print('视觉上的618: ' + str(round(sp618, 2)))
|
||
|
||
sp382_stats = stats.scoreatpercentile(tsla_df.close, 38.2)
|
||
sp618_stats = stats.scoreatpercentile(tsla_df.close, 61.8)
|
||
|
||
print('统计上的382: ' + str(round(sp382_stats, 2)))
|
||
print('统计上的618: ' + str(round(sp618_stats, 2)))
|
||
|
||
|
||
# noinspection PyTypeChecker
|
||
def sample_571_2():
|
||
"""
|
||
5.7.1 黄金分割线的定义方式
|
||
:return:
|
||
"""
|
||
from collections import namedtuple
|
||
|
||
# 收盘价格序列中的最大值
|
||
cs_max = tsla_df.close.max()
|
||
# 收盘价格序列中的最小值
|
||
cs_min = tsla_df.close.min()
|
||
|
||
sp382 = (cs_max - cs_min) * 0.382 + cs_min
|
||
sp618 = (cs_max - cs_min) * 0.618 + cs_min
|
||
sp382_stats = stats.scoreatpercentile(tsla_df.close, 38.2)
|
||
sp618_stats = stats.scoreatpercentile(tsla_df.close, 61.8)
|
||
|
||
def plot_golden():
|
||
# 从视觉618和统计618中筛选更大的值
|
||
above618 = np.maximum(sp618, sp618_stats)
|
||
# 从视觉618和统计618中筛选更小的值
|
||
below618 = np.minimum(sp618, sp618_stats)
|
||
# 从视觉382和统计382中筛选更大的值
|
||
above382 = np.maximum(sp382, sp382_stats)
|
||
# 从视觉382和统计382中筛选更小的值
|
||
below382 = np.minimum(sp382, sp382_stats)
|
||
|
||
# 绘制收盘价
|
||
plt.plot(tsla_df.close)
|
||
# 水平线视觉382
|
||
plt.axhline(sp382, c='r')
|
||
# 水平线统计382
|
||
plt.axhline(sp382_stats, c='m')
|
||
# 水平线视觉618
|
||
plt.axhline(sp618, c='g')
|
||
# 水平线统计618
|
||
plt.axhline(sp618_stats, c='k')
|
||
|
||
# 填充618 red
|
||
plt.fill_between(tsla_df.index, above618, below618,
|
||
alpha=0.5, color="r")
|
||
# 填充382 green
|
||
plt.fill_between(tsla_df.index, above382, below382,
|
||
alpha=0.5, color="g")
|
||
|
||
# 最后使用namedtuple包装上，方便获取
|
||
return namedtuple('golden', ['above618', 'below618', 'above382',
|
||
'below382'])(
|
||
above618, below618, above382, below382)
|
||
|
||
golden = plot_golden()
|
||
|
||
# 根据绘制顺序标注名称
|
||
plt.legend(['close', 'sp382', 'sp382_stats', 'sp618', 'sp618_stats'],
|
||
loc='best')
|
||
plt.show()
|
||
|
||
print('理论上的最高盈利: {}'.format(golden.above618 - golden.below382))
|
||
|
||
return golden
|
||
|
||
|
||
def sample_572():
|
||
"""
|
||
5.7.2 多维数据绘制示例
|
||
:return:
|
||
"""
|
||
from itertools import product
|
||
|
||
buy_rate = [0.20, 0.25, 0.30]
|
||
sell_rate = [0.70, 0.80, 0.90]
|
||
|
||
def find_percent_point(percent, y_org, want_max):
|
||
"""
|
||
:param percent: 比例
|
||
:param y_org: close价格序列
|
||
:param want_max: 是否返回大的值
|
||
:return:
|
||
"""
|
||
cs_max = y_org.max()
|
||
cs_min = y_org.min()
|
||
|
||
# 如果want_max 就使用maximum否则minimum
|
||
maxmin_mum = np.maximum if want_max else np.minimum
|
||
# 每次都计算统计上和视觉上，根据want_max返回大的值above，或小的值below
|
||
return maxmin_mum(
|
||
# 统计上的计算
|
||
stats.scoreatpercentile(y_org, np.round(percent * 100, 1)),
|
||
# 视觉上的计算
|
||
(cs_max - cs_min) * percent + cs_min)
|
||
|
||
# 存储结果list
|
||
result = list()
|
||
# 先将0.382, 0.618这一组放入结果队列中
|
||
|
||
golden = sample_571_2()
|
||
result.append(
|
||
(0.382, 0.618, round(golden.above618 - golden.below382, 2)))
|
||
|
||
# 将buy_rate和sell_rate做笛卡尔积排列各种组合
|
||
for (buy, sell) in product(buy_rate, sell_rate):
|
||
# 如果是买入比例want_max为False，因为只计算理论最高盈利，只需要最below
|
||
profit_below = find_percent_point(buy, tsla_df.close, False)
|
||
# 如果是卖出比例want_max为True，因为只计算理论最高盈利，只需要最above
|
||
profit_above = find_percent_point(sell, tsla_df.close, True)
|
||
# 最终将买入比例，卖出比例，理论最高盈利append
|
||
result.append((buy, sell,
|
||
round(profit_above - profit_below, 2)))
|
||
# 最后使用np.array套上result
|
||
result = np.array(result)
|
||
print('result:\n', result)
|
||
|
||
# 1. 通过scatter点图
|
||
cmap = plt.get_cmap('jet', 20)
|
||
cmap.set_under('gray')
|
||
fig, ax = plt.subplots(figsize=(8, 5))
|
||
# scatter点图，result[:, 0]:x，result[:, 1]:y, result[:, 2]:c
|
||
cax = ax.scatter(result[:, 0], result[:, 1], c=result[:, 2],
|
||
cmap=cmap, vmin=np.min(result[:, 2]),
|
||
vmax=np.max(result[:, 2]))
|
||
fig.colorbar(cax, label='max profit', extend='min')
|
||
plt.grid(True)
|
||
plt.xlabel('buy rate')
|
||
plt.ylabel('sell rate')
|
||
plt.show()
|
||
|
||
# 2. 通过mpl_toolkits.mplot3d
|
||
# noinspection PyUnresolvedReferences
|
||
from mpl_toolkits.mplot3d import Axes3D
|
||
|
||
fig = plt.figure(figsize=(9, 6))
|
||
ax = fig.gca(projection='3d')
|
||
ax.view_init(30, 60)
|
||
ax.scatter3D(result[:, 0], result[:, 1], result[:, 2], c='r', s=50,
|
||
cmap='spring')
|
||
ax.set_xlabel('buy rate')
|
||
ax.set_ylabel('sell rate')
|
||
ax.set_zlabel('max profit')
|
||
plt.show()
|
||
|
||
|
||
# noinspection PyTypeChecker
|
||
def sample_581():
|
||
"""
|
||
5.8.1 MACD指标的可视化
|
||
:return:
|
||
"""
|
||
from abupy import nd
|
||
nd.macd.plot_macd_from_klpd(tsla_df)
|
||
|
||
|
||
def sample_582_1():
|
||
"""
|
||
5.8.2_1 ATR指标的可视化, 使用talib
|
||
:return:
|
||
"""
|
||
from abupy import nd
|
||
nd.atr.plot_atr_from_klpd(tsla_df)
|
||
|
||
if __name__ == "__main__":
|
||
sample_511()
|
||
# sample_512()
|
||
# sample_513()
|
||
# sample_52()
|
||
# sample_531_1()
|
||
# sample_531_2()
|
||
# sample_532()
|
||
# sample_533()
|
||
# sample_54_1()
|
||
# sample_54_2()
|
||
# sample_55_1()
|
||
# sample_55_2()
|
||
# sample_56_1()
|
||
# sample_56_2()
|
||
# sample_56_3()
|
||
# sample_571_1()
|
||
# sample_571_2()
|
||
# sample_572()
|
||
# sample_581()
|
||
# sample_582_1()
|