12对数据进行简单的统计

全局统计（global statistics）
例如我们平常接触到的平均值，中位数，标准差等等

在获得数据的基础上，我们才能进行数据的统计

import pandas as pd

def get_data(symbols,dates):
    df = pd.DataFrame(index = dates)
    if 'SPY' not in symbols:  #为了防止失去参考
        symbols.insert(0,'SPY')
        
    for symbol in symbols:
        #1读取数据
        df_temp = pd.read_csv("data/{}.csv".format(symbol),index_col = 'Date',
                              parse_dates=True,usecols=['Date','Adj Close'],
                              na_values = ['nan'])
        #2改名字
        df_temp = df_temp.rename(columns={'Adj Close':symbol})
        #3吧数据合并到一个表上
        df=df.join(df_temp,how = 'inner')
    return df
    



def test_run():
    # Define a date range
    dates = pd.date_range('2010-01-01', '2012-12-31')

    # Choose stock symbols to read
    symbols = ['SPY','XOM','GOOG', 'IBM', 'GLD']
    
    # Get stock data
    df = get_data(symbols, dates)
    
    
    #compute global statistics for each stock
    print 'mean:\n',df.mean() 
    print '\nmeadian:\n',df.median() 
    print '\nstd:\n',df.std() 


if __name__ == '__main__':
    test_run()

运行结果

借助python，我们可以轻易地进行全局统计了！

2.滚动平均值

import pandas as pd
import matplotlib.pyplot as plt

def get_data(symbols,dates):
    df = pd.DataFrame(index = dates)
    if 'SPY' not in symbols:  #为了防止失去参考
        symbols.insert(0,'SPY')
        
    for symbol in symbols:
        #1读取数据
        df_temp = pd.read_csv("data/{}.csv".format(symbol),index_col = 'Date',
                              parse_dates=True,usecols=['Date','Adj Close'],
                              na_values = ['nan'])
        #2改名字
        df_temp = df_temp.rename(columns={'Adj Close':symbol})
        #3吧数据合并到一个表上
        df=df.join(df_temp,how = 'inner')
    return df
    



def test_run():
    # Define a date range
    dates = pd.date_range('2012-01-01', '2012-12-31')

    # Choose stock symbols to read
    symbols = ['SPY']
    
    # Get stock data
    df = get_data(symbols, dates)
    
    
    ax = df['SPY'].plot(title = 'SPY rolling mean',label = 'SPY')
    
    #先算出滚动平均值
    rm_SPY=pd.rolling_mean(df['SPY'],window = 20)
    
    #对滚动平均值画图，随后加到之前画的图像中
    rm_SPY.plot(label = "Rolling mean",ax=ax)
    
    
    #加上坐标轴的名字和图例子
    ax.set_xlabel("Date")
    ax.set_ylabel("price")
    ax.legend(loc = "upper left")
    plt.show()
    

if __name__ == '__main__':
    test_run()

运行结果

import pandas as pd
import matplotlib.pyplot as plt

def get_data(symbols,dates):
    df = pd.DataFrame(index = dates)
    if 'SPY' not in symbols:  #为了防止失去参考
        symbols.insert(0,'SPY')
        
    for symbol in symbols:
        #1读取数据
        df_temp = pd.read_csv("data/{}.csv".format(symbol),index_col = 'Date',
                              parse_dates=True,usecols=['Date','Adj Close'],
                              na_values = ['nan'])
        #2改名字
        df_temp = df_temp.rename(columns={'Adj Close':symbol})
        #3吧数据合并到一个表上
        df=df.join(df_temp,how = 'inner')
    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."""
    # TODO: Compute and return rolling standard deviation
    return pd.rolling_std(values,window=window)


def get_bollinger_bands(rm, rstd):
    """Return upper and lower Bollinger Bands."""
    # TODO: Compute upper_band and lower_band
    upper_band = rm+2*rstd
    lower_band = rm-2*rstd
    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()

运行结果

这个时候，看到比较好的买点和卖点了