简单量化短期的商品情绪

import datetime

from WindPy import w
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# 启动Wind开放接口
w.start()

def s_plot(code, start_date):
    # 获取沪深300指数上市以来的行情数据
    wind_df = w.wsd(code, "close", start_date, datetime.date.today().strftime("%Y-%m-%d"))
    tpd_df = pd.DataFrame(wind_df.Data[0], columns=['close'])
    tpd_df.index = wind_df.Times
    tpd_df['shift_close'] = tpd_df['close'].shift(1)
    tpd_df['daily_return'] = tpd_df['close'] / tpd_df['shift_close'] - 1
    rolling_20day_return = tpd_df['daily_return'].rolling(20).apply(lambda x:(x+1).prod())
    analysis_df = pd.DataFrame(rolling_20day_return)
    analysis_df['0.025_bot'] = analysis_df['daily_return'].quantile(0.0255)
    analysis_df['0.05_bot'] = analysis_df['daily_return'].quantile(0.05)
    analysis_df['0.95_bot'] = analysis_df['daily_return'].quantile(0.95)
    analysis_df['0.975_bot'] = analysis_df['daily_return'].quantile(0.975)
    analysis_df['close'] = tpd_df['close']
    # plt.figure()
    # analysis_df['daily_return'].hist(bins=200, figsize=(19, 9))
    # plt.show()
    print('current code is : %s' % code)
    plt.figure()
    analysis_df[['daily_return', '0.025_bot', '0.05_bot', '0.95_bot', '0.975_bot']].plot(figsize=(19, 9))
    plt.show()

    plt.figure()
    analysis_df['close'].plot(figsize=(19, 9))
    plt.show()

base_code = ['UR', 'AU', 'IM', 'IC', 'IH', 'AG', 'AP', 'SM', 'JD', 'IF', 'PB', 'SA', 'TS', 'SI', 'CJ', 'TF', 'CY', 'RS', 'T', 'PK', 'CF', 'SF', 'C',
            'CS', 'CU', 'LH', 'SS', 'L', 'SN', 'PP', 'AL', 'MA', 'PG', 'NI', 'V', 'RU', 'SP', 'SR', 'A','M','B','ZN', 'Y', 'RB','EB','HC','PF',
            'FG', 'TA','BU','EG', 'OI','P','RM','JM', 'I', 'J', 'LU', 'SC','FU']

start_date = '2015-01-01'
for code in base_code:
    s_plot('%sFI.WI'% code, start_date)