def week_period_strategy(code, time_freq, start_date, end_date):
"""
周期选股（周四买，周一卖）
:param code:
:param time_freq:
:param start_date:
:param end_date:
:return:
"""
data = st.get_single_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)
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
#!/usr/bin/env python
# encoding: utf-8
'''
@author: DeltaF
@software: pycharm
@file: stock.py
@time: 2021/3/5 02:08
@desc: 获取价格，并且计算涨跌幅
'''
import data.stock as st
# 获取平安银行的行情数据（日K）
data = st.get_single_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) # 多了一列close_pct
# print(data[['close', 'short_ma', 'long_ma', 'buy_signal', 'sell_signal','signal']])
# 删除多余的columns
data.drop(labels=['buy_signal', 'sell_signal'], axis=1)
return data
if __name__ == '__main__':
# 股票列表
stocks = ['000001.XSHE', '000858.XSHE', '002594.XSHE']
# 存放累计收益率
cum_profits = pd.DataFrame()
# 循环获取数据
for code in stocks:
df = st.get_single_price(code, 'daily', '2016-01-01', '2021-01-01')
df = ma_strategy(df) # 调用双均线策略
cum_profits[code] = df['cum_profit'].reset_index(drop=True) # 存储累计收益率
# 折线图
df['cum_profit'].plot(label=code)
# 筛选有信号点
# df = df[df['signal'] != 0]
# 预览数据
print("开仓次数：", int(len(df)))
# print(df[['close', 'signal', 'pro 、fit_pct', 'cum_profit']])
# 预览
print(cum_profits)
# 可视化
# cum_profits.plot()
plt.legend()
#!/usr/bin/env python
# encoding: utf-8
'''
@author: DeltaF
@software: pycharm
@file: stock.py
@time: 2021/3/3 00:29
@desc: 用于调用股票行情数据的脚本
'''
import data.stock as st
import pandas as pd
# 初始化变量
code = '000001.XSHG'
# 调用一只股票的行情数据
data = st.get_single_price(code=code,
time_freq='daily',
start_date='2021-02-01',
end_date='2021-03-01')
# 存入csv
st.export_data(data=data, filename=code, type='price')
# 从csv中获取数据
data = st.get_csv_data(code=code, type='price')
print(data)
# 实时更新数据：假设每天更新日K数据 > 存到csv文件里面 > data.to_csv(append)
data['buy_signal'] = np.where((data['weekday'] == 3), 1, 0)
# 周一卖出
data['sell_signal'] = np.where((data['weekday'] == 0), -1, 0)
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())
# df['profit_pct'].plot()
# plt.show()
# 查看平安银行最大回撤
df = st.get_single_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()
# 计算夏普比率
# df = st.get_single_price('000001.XSHE', 'daily', '2006-01-01', '2021-01-01')
# sharpe = calculate_sharpe(df)
# print(sharpe)
@file: comp_sharpe_ratio.py
@time: 2021/3/16 22:20
@desc:
'''
import data.stock as st
import strategy.base as stb
import pandas as pd
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_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()
