Question

我在下面的代码中遇到了一个小问题。它工作得很好，但它不是我写的，有一部分我不明白。在我的头脑中，我需要返回rm＆amp;来自get_rolling_mean()和get_rolling_std()的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_bollinger_bands(rm, rstd)变量中的rm和rstd在何处以及如何从中获取其值？

"""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()

Answer 1

Get rollinger bands函数从用户获取变量：

 get_bollinger_bands(rm, rstd):
        upper_band = rm + (rstd * 2)
        lower_band = rm - (rstd * 2)
        return upper_band, lower_band

使用的唯一变量是函数名后面的括号之间的变量。这意味着他们将被用户所污染。

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)

函数得到滚动均值和标准都使用两个投入：值（又名x = 1,2,3和y = 2,3,4）和窗口（滚动平均值中包含的观察量）< / p>

有关其他信息，我建议使用文档：http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.rolling_mean.html

和维基百科（滚动均值和移动平均值是相同的）：https://en.wikipedia.org/wiki/Moving_average

Python中的布林带。在下面的代码中定义/如何定义rm和rstd？

1 个答案: