用Python做股市数据分析（一）

import matplotlib.pyplot as plt   #Import matplotlib
#This line is necessary for the plot to appear in a Jupyter notebook
%matplotlib inline
#Control the default size of figures in this Jupyter notebook
%pylab inline
pylab.rcParams['figure.figsize'] = (15, 9)   #Change the size of plots

apple["Adj Close"].plot(grid = True) #Plot the adjusted closing price of AAPL

Populating the interactive namespace from numpy and matplotlib

from matplotlib.dates importDateFormatter, WeekdayLocator,
    DayLocator, MONDAY
from matplotlib.finance importcandlestick_ohlc
def pandas_candlestick_ohlc(dat, stick = "day", otherseries =None):
    """
    :param dat: pandas DataFrame object with datetime64 index, and float columns "Open", "High", "Low", and "Close", likely created via DataReader from "yahoo"
    :param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include "day", "week", "month", and "year", ("day" default), and any numeric input indicates the number of trading days included in a period
    :param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines
    This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.
    """
    mondays = WeekdayLocator(MONDAY)        #major ticks on the mondays
    alldays = DayLocator()              #minor ticks on the days
    dayFormatter = DateFormatter('%d')      #e.g., 12
    #Create a new DataFrame which includes OHLC data for each period specified by stick input
    transdat = dat.loc[:,["Open", "High", "Low", "Close"]]
    if (type(stick) ==str):
        if stick == "day":
            plotdat =transdat
            stick = 1 #Used for plotting
        elif stick in ["week", "month", "year"]:
            if stick == "week":
                transdat["week"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) #Identify weeks
            elif stick == "month":
                transdat["month"] = pd.to_datetime(transdat.index).map(lambda x: x.month) #Identify months
            transdat["year"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) #Identify years
            grouped = transdat.groupby(list(set(["year",stick]))) #Group by year and other appropriate variable
            plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) #Create empty data frame containing what will be plotted
            for name, group ingrouped:
                plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
                                            "High": max(group.High),
                                            "Low": min(group.Low),
                                            "Close": group.iloc[-1,3]},
                                           index =[group.index[0]]))
            if stick == "week": stick = 5
            elif stick == "month": stick = 30
            elif stick == "year": stick = 365
    elif (type(stick) == int and stick >= 1):
        transdat["stick"] = [np.floor(i / stick) for i inrange(len(transdat.index))]
        grouped = transdat.groupby("stick")
        plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) #Create empty data frame containing what will be plotted
        for name, group ingrouped:
            plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
                                        "High": max(group.High),
                                        "Low": min(group.Low),
                                        "Close": group.iloc[-1,3]},
                                       index =[group.index[0]]))
    else:
        raise ValueError('Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer')
    #Set plot parameters, including the axis object ax used for plotting
    fig, ax =plt.subplots()
    fig.subplots_adjust(bottom=0.2)
    if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
        weekFormatter = DateFormatter('%b %d')  #e.g., Jan 12
ax.xaxis.set_major_locator(mondays)
        ax.xaxis.set_minor_locator(alldays)
    else:
        weekFormatter = DateFormatter('%b %d, %Y')
    ax.xaxis.set_major_formatter(weekFormatter)
    ax.grid(True)
    #Create the candelstick chart
    candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat["Open"].tolist(), plotdat["High"].tolist(),
                      plotdat["Low"].tolist(), plotdat["Close"].tolist())),
                      colorup = "black", colordown = "red", width = stick * .4)
    #Plot other series (such as moving averages) as lines
    if otherseries !=None:
        if type(otherseries) !=list:
            otherseries =[otherseries]
        dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid =True)
    ax.xaxis_date()
    ax.autoscale_view()
    plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
    plt.show()
pandas_candlestick_ohlc(apple)

microsoft = web.DataReader("MSFT", "yahoo", start, end)
google = web.DataReader("GOOG", "yahoo", start, end)
#Below I create a DataFrame consisting of the adjusted closing price of these stocks, first by making a list of these objects and using the join method
stocks = pd.DataFrame({"AAPL": apple["Adj Close"],
                      "MSFT": microsoft["Adj Close"],
                      "GOOG": google["Adj Close"]})
stocks.head()

#Let's use NumPy's log function, though math's log function would work just as well
importnumpy as np
stock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1))) #shift moves dates back by 1.
stock_change.head()

apple["20d"] = np.round(apple["Close"].rolling(window = 20, center = False).mean(), 2)
pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-08-07',:], otherseries = "20d")

start = datetime.datetime(2010,1,1)
apple = web.DataReader("AAPL", "yahoo", start, end)
apple["20d"] = np.round(apple["Close"].rolling(window = 20, center = False).mean(), 2)
pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-08-07',:], otherseries = "20d")

apple["50d"] = np.round(apple["Close"].rolling(window = 50, center = False).mean(), 2)
apple["200d"] = np.round(apple["Close"].rolling(window = 200, center = False).mean(), 2)
pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-08-07',:], otherseries = ["20d", "50d", "200d"])