def get_k_day_return(code,
days):
"""
获得k日收益率，计算公式：(当日收盘价-k日前收盘价)/当日收盘价
"""
fname = data.get_filename(code)
df = data.read_data(fname)
return df['close'].pct_change(periods=days)
def get_k_day_volatility(code, days):
"""
获得k日波动率
"""
fname = data.get_filename(code)
df = data.read_data(fname)
df["return"] = df["close"].pct_change()
return df["return"].rolling(window=days).std() * np.sqrt(243)
def get_k_day_average_return(code,
days):
"""
获得k日平均收益率
"""
fname = data.get_filename(code)
df = data.read_data(fname)
df["return"] = df['close'].pct_change()
return df["return"].rolling(window=days).mean()
def get_k_day_return(code,
days=5,
start_date="2012-01-01",
end_date="2017-01-01"):
"""
获得k日收益率，计算公式：(当日收盘价-k日前收盘价)/当日收盘价
"""
fname = data.get_filename(code)
df = data.read_data(fname)
df = df[df.index >= start_date]
factor_name = "%d-day return" % (days)
df[factor_name] = df['close'].pct_change(periods=days)
return df[factor_name]
def merge_fundamental(code):
'''
因为基本面数据是季度更新，时间轴上跟stock数据不同，故先需要做merge操作
'''
fname = data.get_fundamental_filename(code)
fdf = data.read_data(fname)
fname = data.get_filename(code)
df = data.read_data(fname)
dates = df.index
df = df.merge(fdf, left_index=True, right_index=True, how='outer')
df = df.fillna(method='ffill')
df = df.loc[dates]
return df
def get_k_day_average_return(code,
days=5,
start_date="2012-01-01",
end_date="2017-01-01"):
"""
获得k日平均收益率
"""
fname = data.get_filename(code)
df = data.read_data(fname)
df = df[df.index >= start_date]
factor_name = "%d-day average return" % (days)
df["return"] = df['close'].pct_change()
df[factor_name] = df["return"].rolling(window=days).mean()
return df[factor_name]
def get_k_day_volatility(code,
days,
start_date,
end_date):
"""
获得k日波动率
"""
fname = data.get_filename(code)
df = data.read_data(fname)
df = df[df.index >= start_date]
factor_name = "%d-day volatility"%(days)
df["return"] = df["close"].pct_change()
df[factor_name] = df["return"].rolling(window=days).std() * np.sqrt(243)
return df[factor_name]