Python中的宝林带。rm和rstd在以下代码中的定义位置/方式?
我对下面截取的代码有一个小问题。它工作完美,但它不是我写的,有一部分我不明白。在我的头脑中,我需要从Python中的宝林带。rm和rstd在以下代码中的定义位置/方式?,python,python-3.x,pandas,Python,Python 3.x,Pandas,我对下面截取的代码有一个小问题。它工作完美,但它不是我写的,有一部分我不明白。在我的头脑中,我需要从get\u rolling\u mean()和get\u rolling\u std()返回rm&rstd,但这并不是真的发生在这里。所以我的问题是:我知道它有效,但它是如何工作的 get\u bollinger\u bands(rm,rstd)变量中的rm和rstd从何处以及如何获取其值 """Bollinger Bands.""" import os import pandas as pd
get\u rolling\u mean()
和get\u rolling\u std()
返回rm&rstd,但这并不是真的发生在这里。所以我的问题是:我知道它有效,但它是如何工作的
get\u bollinger\u bands(rm,rstd)
变量中的rm和rstd从何处以及如何获取其值
"""Bollinger Bands."""
import os
import pandas as pd
import matplotlib.pyplot as plt
def symbol_to_path(symbol, base_dir="data"):
"""Return CSV file path given ticker symbol."""
return os.path.join(base_dir, "{}.csv".format(str(symbol)))
def get_data(symbols, dates):
"""Read stock data (adjusted close) for given symbols from CSV files."""
df = pd.DataFrame(index=dates)
if 'SPY' not in symbols: # add SPY for reference, if absent
symbols.insert(0, 'SPY')
for symbol in symbols:
df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])
df_temp = df_temp.rename(columns={'Adj Close': symbol})
df = df.join(df_temp)
if symbol == 'SPY': # drop dates SPY did not trade
df = df.dropna(subset=["SPY"])
return df
def plot_data(df, title="Stock prices"):
"""Plot stock prices with a custom title and meaningful axis labels."""
ax = df.plot(title=title, fontsize=12)
ax.set_xlabel("Date")
ax.set_ylabel("Price")
plt.show()
def get_rolling_mean(values, window):
"""Return rolling mean of given values, using specified window size."""
return pd.rolling_mean(values, window=window)
def get_rolling_std(values, window):
"""Return rolling standard deviation of given values, using specified window size."""
return pd.rolling_std(values, window=window)
def get_bollinger_bands(rm, rstd):
"""Return upper and lower Bollinger Bands."""
upper_band = rm + (rstd * 2)
lower_band = rm - (rstd * 2)
return upper_band, lower_band
def test_run():
# Read data
dates = pd.date_range('2012-01-01', '2012-12-31')
symbols = ['SPY']
df = get_data(symbols, dates)
# Compute Bollinger Bands
# 1. Compute rolling mean
rm_SPY = get_rolling_mean(df['SPY'], window=20)
# 2. Compute rolling standard deviation
rstd_SPY = get_rolling_std(df['SPY'], window=20)
# 3. Compute upper and lower bands
upper_band, lower_band = get_bollinger_bands(rm_SPY, rstd_SPY)
# Plot raw SPY values, rolling mean and Bollinger Bands
ax = df['SPY'].plot(title="Bollinger Bands", label='SPY')
rm_SPY.plot(label='Rolling mean', ax=ax)
upper_band.plot(label='upper band', ax=ax)
lower_band.plot(label='lower band', ax=ax)
# Add axis labels and legend
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc='upper left')
plt.show()
if __name__ == "__main__":
test_run()
Get rollinger bands函数从用户获取其变量:
get_bollinger_bands(rm, rstd):
upper_band = rm + (rstd * 2)
lower_band = rm - (rstd * 2)
return upper_band, lower_band
唯一使用的变量是函数名后括号之间的变量。这意味着它们将由用户输入
def get_rolling_mean(values, window):
return pd.rolling_mean(values, window=window)
def get_rolling_std(values, window):
return pd.rolling_std(values, window=window)
函数获得滚动平均值和std都使用两个输入:值(aka x=1,2,3和y=2,3,4)和窗口(滚动平均值中包含的观察量)
如需更多信息,我建议使用以下文档:
和维基百科(滚动平均数和移动平均数是一样的):