[Python知识库] python量化交易第三章

开发: C++知识库 Java知识库 JavaScript Python PHP知识库人工智能区块链大数据移动开发嵌入式开发工具数据结构与算法开发测试游戏开发网络协议系统运维
教程: HTML教程 CSS教程 JavaScript教程 Go语言教程 JQuery教程 VUE教程 VUE3教程 Bootstrap教程 SQL数据库教程 C语言教程 C++教程 Java教程 Python教程 Python3教程 C#教程
数码: 电脑笔记本显卡显示器固态硬盘硬盘耳机手机 iphone vivo oppo 小米华为单反装机图拉丁

-> Python知识库 -> python量化交易第三章 -> 正文阅读

[Python知识库]python量化交易第三章

一.股票交易快速入门

股票交易规则

开户->炒股平台，券商（交易费）
交易版面->K线，盘口，交易价格，数量（100股）
交易费->佣金<3%,过户费1元每千股，印花税1%
A股特点—>±10%(主/中小板) ±20%(创业/科创板)

二.用shift函数计算涨跌幅

def calculate_change_pct(data):
    """
    涨跌幅=（当期收盘价-前期收盘价）/前期收盘价
    :param data:dataframe，带有收盘价
    :return:datafram，带有涨跌幅
    """
    data['close_pct']=(data['close']-data['close'].shift(1))/data['close'].shift(1)
    return data

import data.stock as st
#获取平安银行的行情数据（日K）
data=st.get_single_stock_Price('000001.XSHE','daily','2020-01-01','2020-02-01')
# print(data)

#计算涨跌幅，验证准确性
#data=st.calculate_change_pct(data)
# print(data)#多了一列close_pct
#获取周K
data=st.transfer_price_freq(data,'W')
print(data)

#计算涨跌幅，验证准确性
data=st.calculate_change_pct(data)
print(data)

三.模拟股票交易：买入，卖出

新建Strategy模块->创建周期选股策略->生成交易信号

import data.stock as st
import numpy as np
def week_period_strategy(code,time_freq,start_date,end_date):
    data=st.get_single_stock_Price(code,time_freq,start_date,end_date)
    #新建周期字段
    data['weekday']=data.index.weekday
    #周四买入
    data['buy_signal']=np.where((data['weekday']==3),1,0)
    #周一卖出
    data['sell_signal']=np.where((data['weekday']==0),-1,0)
    #整合信号
    data['buy_signal']=np.where((data['buy_signal']==1)&(data['buy_signal'].shift(1)==0),0,data['buy_signal'])
    data['sell_signal']=np.where((data['sell_signal']==-1)&(data['sell_signal'].shift(1)==-1),0,data['sell_signal'])
    data['signal']=data['buy_signal']+data['sell_signal']
    return data
if __name__=='__main__':
    data=week_period_strategy('000001.XSHE','daily','2020-01-01','2020-03-01')
    print(data[['close','weekday','buy_signal','sell_signal','signal']])

四.模拟股票交易：计算持仓收益

总盈亏=（市价-成本价)× 股数
浮动盈亏比=(市价-成本价)/成本价
成本价=买入金额/持有股数
股数=累计买入股数

import datetime
import matplotlib.pyplot as plt
import data.stock as st
import numpy as np
def compose_signal(data):
    """
    整合信号
    :param data: 
    :return: 
    """
    data['buy_signal'] = np.where((data['buy_signal'] == 1) & (data['buy_signal'].shift(1) == 0), 0, data['buy_signal'])
    data['sell_signal'] = np.where((data['sell_signal'] == -1) & (data['sell_signal'].shift(1) == -1), 0,
                                   data['sell_signal'])
    data['signal'] = data['buy_signal'] + data['sell_signal']
    return data
def calculate_prof_pct(data):
    """
    计算单次收益率:开仓，平仓（开仓的全部股数）
    """
    data = data[data['signal'] != 0]  # 筛选
    data['profit_pct'] = ((data['close'] - data['close'].shift(1)) / data['close'].shift(1))
    data = data[data['signal'] == -1]
    return data
def week_period_strategy(code,time_freq,start_date,end_date):
    data=st.get_single_stock_Price(code,time_freq,start_date,end_date)
    #新建周期字段
    data['weekday']=data.index.weekday
    #周四买入
    data['buy_signal']=np.where((data['weekday']==3),1,0)
    #周一卖出
    data['sell_signal']=np.where((data['weekday']==0),-1,0)
    #整合信号
    data=compose_signal(data)
    data=calculate_prof_pct(data)
    return data

if __name__=='__main__':
    df=week_period_strategy('000001.XSHE','daily',None,datetime.date.today())
    print(df[['close','signal','profit_pct']])
    print(df.describe())
    #可视化
    df['profit_pct'].plot()
    plt.show()

持仓数据的计算->总盈亏，盈亏比，成本价，股数
计算单次收益率->最直接的策略收益评估指标
Matplotlib可视化->观察周期选股的收益表现

五.模拟股票交易：计算累计收益率

计算累计收益率->（1+当天收益率）的累计乘积-1

import datetime
import matplotlib.pyplot as plt
import pandas as pd

import data.stock as st
import numpy as np
def compose_signal(data):
    """
    整合信号
    :param data: 
    :return: 
    """
    data['buy_signal'] = np.where((data['buy_signal'] == 1) & (data['buy_signal'].shift(1) == 0), 0, data['buy_signal'])
    data['sell_signal'] = np.where((data['sell_signal'] == -1) & (data['sell_signal'].shift(1) == -1), 0,
                                   data['sell_signal'])
    data['signal'] = data['buy_signal'] + data['sell_signal']
    return data
def calculate_prof_pct(data):
    """
    计算单次收益率:开仓，平仓（开仓的全部股数）
    """
    data = data[data['signal'] != 0]  # 筛选
    data['profit_pct'] = ((data['close'] - data['close'].shift(1)) / data['close'].shift(1))
    data = data[data['signal'] == -1]
    return data
def calculate_cum_prof(data):
    """
    计算累计收益率
    :param data:dateframe
    :return:
    """
    data['cum_profit']=pd.DataFrame(1+data['profit_pct']).cumprod()-1
    return data
def week_period_strategy(code,time_freq,start_date,end_date):
    data=st.get_single_stock_Price(code,time_freq,start_date,end_date)
    #新建周期字段
    data['weekday']=data.index.weekday
    #周四买入
    data['buy_signal']=np.where((data['weekday']==3),1,0)
    #周一卖出
    data['sell_signal']=np.where((data['weekday']==0),-1,0)
    #整合信号
    data=compose_signal(data)#整合信号
    data=calculate_prof_pct(data)#计算收益
    data=calculate_cum_prof(data)#计算累计收益率
    return data

if __name__=='__main__':
    df=week_period_strategy('000001.XSHE','daily',None,datetime.date.today())
    print(df[['close','signal','profit_pct','cum_profit']])
    print(df.describe())
    #可视化
    df['cum_profit'].plot()
    plt.show()

六.计算风险指标：最大回撤

最大回撤->在选定周期内任一历史时点往后推，产品净值走到最低点时的收益率回撤幅度，是一个非常重要的风险指标

import datetime
import matplotlib.pyplot as plt
import pandas as pd

import data.stock as st
import numpy as np
def compose_signal(data):
    """
    整合信号
    :param data: 
    :return: 
    """
    data['buy_signal'] = np.where((data['buy_signal'] == 1) & (data['buy_signal'].shift(1) == 0), 0, data['buy_signal'])
    data['sell_signal'] = np.where((data['sell_signal'] == -1) & (data['sell_signal'].shift(1) == -1), 0,
                                   data['sell_signal'])
    data['signal'] = data['buy_signal'] + data['sell_signal']
    return data
def calculate_prof_pct(data):
    """
    计算单次收益率:开仓，平仓（开仓的全部股数）
    """
    data = data[data['signal'] != 0]  # 筛选
    data['profit_pct'] = ((data['close'] - data['close'].shift(1)) / data['close'].shift(1))
    data = data[data['signal'] == -1]
    return data
def calculate_cum_prof(data):
    """
    计算累计收益率
    :param data:dateframe
    :return:
    """
    data['cum_profit']=pd.DataFrame(1+data['profit_pct']).cumprod()-1
    return data
def caculate_max_drawdown(data):
    """
    计算最大回撤比
    :param data:
    :return:
    """
    #选取时间周期（时间窗口）
    window=252
    #选取时间周期中的最大净值
    data['roll_max']=data['close'].rolling(window=252,min_periods=1).max()
    #计算当天的回撤比=谷值/峰值-1
    data['daily_dd']=data['close']/data['roll_max']-1
    #选取时间周期内的最大回撤比，即最大回撤
    data['max_dd']=data['daily_dd'].rolling(window,min_periods=1).min()
    return data
def week_period_strategy(code,time_freq,start_date,end_date):
    data=st.get_single_stock_Price(code,time_freq,start_date,end_date)
    #新建周期字段
    data['weekday']=data.index.weekday
    #周四买入
    data['buy_signal']=np.where((data['weekday']==3),1,0)
    #周一卖出
    data['sell_signal']=np.where((data['weekday']==0),-1,0)
    #整合信号
    data=compose_signal(data)#整合信号
    data=calculate_prof_pct(data)#计算收益
    data=calculate_cum_prof(data)#计算累计收益率
    data=caculate_max_drawdown(data)#最大回撤
    return data

if __name__=='__main__':
    # df=week_period_strategy('000001.XSHE','daily',None,datetime.date.today())
    # print(df[['close','signal','profit_pct','cum_profit']])
    # print(df.describe())
    # #可视化
    # df['cum_profit'].plot()
    # plt.show()
    #查看平安银行最大回撤
    df=week_period_strategy('000001.XSHE','daily','2006-01-01','2021-01-01')
    df=caculate_max_drawdown(df)
    print(df[['close','roll_max','daily_dd','max_dd']])
    df[['daily_dd','max_dd']].plot()
    plt.show()

七.计算风险收益指标：夏普比率

7.1什么是夏普率
Sharpe Ratio：夏普指数，衡量的是一项投资在对其调整风险后，相对于无风险资产的收益表现。
在1966年，由威廉·F·夏普提出,美国金融经济学者,曾在1990年获得诺贝尔经济学奖.
夏普比率是投资者额外承受的每一单位的风险所获得的额外收益,夏普率越高越好

import datetime
import matplotlib.pyplot as plt
import pandas as pd

import data.stock as st
import numpy as np
def compose_signal(data):
    """
    整合信号
    :param data: 
    :return: 
    """
    data['buy_signal'] = np.where((data['buy_signal'] == 1) & (data['buy_signal'].shift(1) == 0), 0, data['buy_signal'])
    data['sell_signal'] = np.where((data['sell_signal'] == -1) & (data['sell_signal'].shift(1) == -1), 0,
                                   data['sell_signal'])
    data['signal'] = data['buy_signal'] + data['sell_signal']
    return data
def calculate_prof_pct(data):
    """
    计算单次收益率:开仓，平仓（开仓的全部股数）
    """
    data = data[data['signal'] != 0]  # 筛选
    data['profit_pct'] = ((data['close'] - data['close'].shift(1)) / data['close'].shift(1))
    data = data[data['signal'] == -1]
    return data
def calculate_cum_prof(data):
    """
    计算累计收益率
    :param data:dateframe
    :return:
    """
    data['cum_profit']=pd.DataFrame(1+data['profit_pct']).cumprod()-1
    return data
def caculate_max_drawdown(data):
    """
    计算最大回撤比
    :param data:
    :return:
    """
    #选取时间周期（时间窗口）
    window=252
    #选取时间周期中的最大净值
    data['roll_max']=data['close'].rolling(window=252,min_periods=1).max()
    #计算当天的回撤比=谷值/峰值-1
    data['daily_dd']=data['close']/data['roll_max']-1
    #选取时间周期内的最大回撤比，即最大回撤
    data['max_dd']=data['daily_dd'].rolling(window,min_periods=1).min()
    return data
def calculate_sharpe(data):
    """
    计算夏普比率，返回的是年化的夏普
    :param data:dateframe，stock
    :return:float
    """
    #公式:sharpe=（回报率的均值-无风险利率）/回报率的标准差
    #因子项
    daily_return=data['close'].pct_change()
    avg_return=daily_return.mean()
    sd_return=daily_return.std()
    #计算夏普:每日收益率*252=每年收益率
    sharpe=avg_return/sd_return
    sharpe_year=sharpe*np.sqrt(252)
    return sharpe,sharpe_year
def week_period_strategy(code,time_freq,start_date,end_date):
    data=st.get_single_stock_Price(code,time_freq,start_date,end_date)
    #新建周期字段
    data['weekday']=data.index.weekday
    #周四买入
    data['buy_signal']=np.where((data['weekday']==3),1,0)
    #周一卖出
    data['sell_signal']=np.where((data['weekday']==0),-1,0)
    #整合信号
    data=compose_signal(data)#整合信号
    data=calculate_prof_pct(data)#计算收益
    data=calculate_cum_prof(data)#计算累计收益率
    data=caculate_max_drawdown(data)#最大回撤
    return data

if __name__=='__main__':
    # df=week_period_strategy('000001.XSHE','daily',None,datetime.date.today())
    # print(df[['close','signal','profit_pct','cum_profit']])
    # print(df.describe())
    # #可视化
    # df['cum_profit'].plot()
    # plt.show()
    #查看平安银行最大回撤
    # df=st.get_single_stock_Price('000001.XSHE','daily','2006-01-01','2021-01-01')
    # df=caculate_max_drawdown(df)
    # print(df[['close','roll_max','daily_dd','max_dd']])
    # df[['daily_dd','max_dd']].plot()
    # plt.show()
    #获取计算夏普比率
    df = st.get_single_stock_Price('000001.XSHE', 'daily', '2006-01-01', '2021-01-01')
    sharpe=calculate_sharpe(df)
    print(sharpe)

八.比较3只股票的夏普指数

import pandas as pd

import data.stock as st
import strategy.base as stb
import matplotlib.pyplot as plt

#获取3只股票的数据:比亚迪，宁德时代，隆基
codes=['002594.XSHE','300750.XSHE','601012.XSHG']
#容器：存放夏普
sharpes=[]
for code in codes:
    data=st.get_single_stock_Price(code,'daily','2018-10-01','2021-01-01')
    print(data.head())

#计算每只股票的夏普比率
    daily_sharpe,annual_sharpe=stb.calculate_sharpe(data)
    sharpes.append([code,annual_sharpe])#存放[[c1,s1],[c2,s2].....]
    print(sharpes)
#可视化3只股票并比较
sharpes=pd.DataFrame(sharpes,columns=['code','sharpe']).set_index('code')
print(sharpes)
#绘制bar图
sharpes.plot.bar(title='compare Annual Sharpe Ratio')
plt.xticks(rotation=30)
plt.show()

Python知识库最新文章

Python中String模块

【Python】 14-CVS文件操作

python的panda库读写文件

使用Nordic的nrf52840实现蓝牙DFU过程

【Python学习记录】numpy数组用法整理

Python学习笔记

python字符串和列表

python如何从txt文件中解析出有效的数据

Python编程从入门到实践自学/3.1-3.2

python变量

加:2022-02-06 13:47:28 更:2022-02-06 13:49:27

360图书馆购物三丰科技阅读网日历万年历 2024年11日历

-2024/11/16 0:25:42-

图片自动播放器
↓图片自动播放器↓

TxT小说阅读器
↓语音阅读,小说下载,古典文学↓

一键清除垃圾
↓轻轻一点,清除系统垃圾↓

图片批量下载器
↓批量下载图片,美女图库↓

网站联系: qq:121756557 email:121756557@qq.com IT数码