def STDDEV(df, n):
df = df.join(pd.Series(pd.rolling_std(df['Close'], n), name = 'STD_' + str(n)))
return df
python类rolling_std()的实例源码
Quantitative platform in test.py 文件源码
项目：Quantitative-Trading-System
作者: carlche15
项目源码
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def bband(stock, window, num_std):
data = stock.selected_line_info.values
index = stock.selected_line_info.index
ma = pd.rolling_mean(data, window)
std = pd.rolling_std(data, window)
upper = ma + std * num_std
lower = ma - std * num_std
bband_ma = pd.Series(ma, index=index)
bband_upper = pd.Series(upper, index=index)
bband_lower = pd.Series(lower, index=index)
return bband_upper,bband_lower,bband_ma
1stock_price_prediction.py 文件源码
项目：Python-Machine-Learning-By-Example
作者: PacktPublishing
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def generate_features(df):
""" Generate features for a stock/index based on historical price and performance
Args:
df (dataframe with columns "Open", "Close", "High", "Low", "Volume", "Adjusted Close")
Returns:
dataframe, data set with new features
"""
df_new = pd.DataFrame()
# 6 original features
df_new['open'] = df['Open']
df_new['open_1'] = df['Open'].shift(1)
df_new['close_1'] = df['Close'].shift(1)
df_new['high_1'] = df['High'].shift(1)
df_new['low_1'] = df['Low'].shift(1)
df_new['volume_1'] = df['Volume'].shift(1)
# 31 original features
# average price
df_new['avg_price_5'] = pd.rolling_mean(df['Close'], window=5).shift(1)
df_new['avg_price_30'] = pd.rolling_mean(df['Close'], window=21).shift(1)
df_new['avg_price_365'] = pd.rolling_mean(df['Close'], window=252).shift(1)
df_new['ratio_avg_price_5_30'] = df_new['avg_price_5'] / df_new['avg_price_30']
df_new['ratio_avg_price_5_365'] = df_new['avg_price_5'] / df_new['avg_price_365']
df_new['ratio_avg_price_30_365'] = df_new['avg_price_30'] / df_new['avg_price_365']
# average volume
df_new['avg_volume_5'] = pd.rolling_mean(df['Volume'], window=5).shift(1)
df_new['avg_volume_30'] = pd.rolling_mean(df['Volume'], window=21).shift(1)
df_new['avg_volume_365'] = pd.rolling_mean(df['Volume'], window=252).shift(1)
df_new['ratio_avg_volume_5_30'] = df_new['avg_volume_5'] / df_new['avg_volume_30']
df_new['ratio_avg_volume_5_365'] = df_new['avg_volume_5'] / df_new['avg_volume_365']
df_new['ratio_avg_volume_30_365'] = df_new['avg_volume_30'] / df_new['avg_volume_365']
# standard deviation of prices
df_new['std_price_5'] = pd.rolling_std(df['Close'], window=5).shift(1)
df_new['std_price_30'] = pd.rolling_std(df['Close'], window=21).shift(1)
df_new['std_price_365'] = pd.rolling_std(df['Close'], window=252).shift(1)
df_new['ratio_std_price_5_30'] = df_new['std_price_5'] / df_new['std_price_30']
df_new['ratio_std_price_5_365'] = df_new['std_price_5'] / df_new['std_price_365']
df_new['ratio_std_price_30_365'] = df_new['std_price_30'] / df_new['std_price_365']
# standard deviation of volumes
df_new['std_volume_5'] = pd.rolling_std(df['Volume'], window=5).shift(1)
df_new['std_volume_30'] = pd.rolling_std(df['Volume'], window=21).shift(1)
df_new['std_volume_365'] = pd.rolling_std(df['Volume'], window=252).shift(1)
df_new['ratio_std_volume_5_30'] = df_new['std_volume_5'] / df_new['std_volume_30']
df_new['ratio_std_volume_5_365'] = df_new['std_volume_5'] / df_new['std_volume_365']
df_new['ratio_std_volume_30_365'] = df_new['std_volume_30'] / df_new['std_volume_365']
# # return
df_new['return_1'] = ((df['Close'] - df['Close'].shift(1)) / df['Close'].shift(1)).shift(1)
df_new['return_5'] = ((df['Close'] - df['Close'].shift(5)) / df['Close'].shift(5)).shift(1)
df_new['return_30'] = ((df['Close'] - df['Close'].shift(21)) / df['Close'].shift(21)).shift(1)
df_new['return_365'] = ((df['Close'] - df['Close'].shift(252)) / df['Close'].shift(252)).shift(1)
df_new['moving_avg_5'] = pd.rolling_mean(df_new['return_1'], window=5)
df_new['moving_avg_30'] = pd.rolling_mean(df_new['return_1'], window=21)
df_new['moving_avg_365'] = pd.rolling_mean(df_new['return_1'], window=252)
# the target
df_new['close'] = df['Close']
df_new = df_new.dropna(axis=0)
return df_new
