有时候,市场的情绪会演绎到比较夸张的位置,其实很简单的指标就可以量化,比如过去20个交易日的累计涨跌幅,同时计算过去滚动涨跌幅的分位数就大致可以判断市场情绪。
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)