Python matplotlib 模块，finance() 实例源码

def loadStock(stock):
    '''
        Use this to dynamically pull a stock:
    '''
    stockFile =[]
    try:
        print('Currently Pulling',stock)
        urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
        print('URL', urlToVisit)
        try:
            sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
            splitSource = sourceCode.split('\n')
            for eachLine in splitSource:
                splitLine = eachLine.split(',')
                if len(splitLine)==6:
                    if 'values' not in eachLine:
                        stockFile.append(eachLine)
        except Exception as e:
            print(str(e), 'failed to organize pulled data.')
    except Exception as e:
        print(str(e), 'failed to pull pricing data')
    return stockFile

def loadStock(stock):
    '''
        Use this to dynamically pull a stock:
    '''
    stockFile =[]
    try:
        print('Currently Pulling',stock)
        urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
        print('URL', urlToVisit)
        try:
            sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
            splitSource = sourceCode.split('\n')
            for eachLine in splitSource:
                splitLine = eachLine.split(',')
                if len(splitLine)==6:
                    if 'values' not in eachLine:
                        stockFile.append(eachLine)
        except Exception as e:
            print(str(e), 'failed to organize pulled data.')
    except Exception as e:
        print(str(e), 'failed to pull pricing data')
    return stockFile

def loadStock(stock):
    '''
        Use this to dynamically pull a stock:
    '''
    stockFile =[]
    try:
        print('Currently Pulling',stock)
        urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
        print('URL', urlToVisit)
        try:
            sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
            splitSource = sourceCode.split('\n')
            for eachLine in splitSource:
                splitLine = eachLine.split(',')
                if len(splitLine)==6:
                    if 'values' not in eachLine:
                        stockFile.append(eachLine)
        except Exception as e:
            print(str(e), 'failed to organize pulled data.')
    except Exception as e:
        print(str(e), 'failed to pull pricing data')
    return stockFile

def plot_p(df):
    import matplotlib.pyplot as plt
    from matplotlib.finance import candlestick2_ohlc
    fig, ax = plt.subplots()
    candlestick2_ohlc(ax,
                      df['price_open'].values,
                      df['price_high'].values,
                      df['price_low'].values,
                      df['price_close'].values,
                      width=0.6,
                      colorup='g',
                      colordown='r',
                      alpha=1)
    plt.show()
    print('Done.')

def save_to_file(df, filename):
    import matplotlib.pyplot as plt
    from matplotlib.finance import candlestick2_ohlc
    fig, ax = plt.subplots()
    candlestick2_ohlc(ax,
                      df['price_open'].values,
                      df['price_high'].values,
                      df['price_low'].values,
                      df['price_close'].values,
                      width=0.6,
                      colorup='g',
                      colordown='r',
                      alpha=1)
    plt.savefig(filename)
    plt.close(fig)

def weekday_candlestick(ohlc_data, ax, fmt='%b %d', freq=7, **kwargs):
    """ Wrapper function for matplotlib.finance.candlestick_ohlc
        that artificially spaces data to avoid gaps from weekends 
    ??????????
    fmt: ????
    freq: ???????
    """

    # Convert data to numpy array
    ohlc_data_arr = np.array(ohlc_data)
    ohlc_data_arr2 = np.hstack(
        [np.arange(ohlc_data_arr[:,0].size)[:,np.newaxis], ohlc_data_arr[:,1:]])
    ndays = ohlc_data_arr2[:,0]  # array([0, 1, 2, ... n-2, n-1, n])

    # Convert matplotlib date numbers to strings based on `fmt`
    dates = mdates.num2date(ohlc_data_arr[:,0])
    date_strings = []
    for date in dates:
        date_strings.append(date.strftime(fmt))

    # Plot candlestick chart
    mpf.candlestick_ohlc(ax, ohlc_data_arr2, **kwargs)

    # Format x axis
    ax.set_xticks(ndays[::freq])
    ax.set_xticklabels(date_strings[::freq], rotation=45, ha='right')
    ax.set_xlim(ndays.min(), ndays.max())