Reference: https: //blog.csdn.net/u014281392/article/details/73611624
Import matplotlib.pyplot AS PLT # # Import drawing module from matplotlib.pylab Import date2num # # Import values correspond to the date of conversion tool from dateutil.parser Import the parse # # introduced into the specified date format conversion tools Import mpl_finance MPF AS # # import module mpl_finance import tushare AS TS from matplotlib.pylab import date2num import datetime code = ' 000001 ' wdyx = ts.get_k_data (code, Start = ' 2019-10-01 ' , End = '2020-01-01 ' ) # date, opening, closing, high, low, volume, Code Print (wdyx [: 3 ]) DEF date_to_num (a dates): num_time = [] for DATE in a dates: date_time = datetime.datetime .strptime (DATE, ' %% Y-M-% D ' ) num_date = date2num (DATE_TIME) num_time.append (num_date) return num_time # dataframe converted into a two-dimensional array mat_wdyx = wdyx.iloc [:,:]. values num_time = date_to_num (mat_wdyx [:, 0]) mat_wdyx [:, 0] = num_time Print (mat_wdyx [:. 3 ]) # Main parameters introduced: # AX: Axes (axis) or Axes objects (axis) array of objects # QUOTES: is the incoming data, the first five columns of data to be and as a function of the predetermined holding # colorup: closing price represents> = opening price, which color chart provided # colordown: closing price represents <opening price, which color chart provided in fig, (ax1, ax2) = plt.subplots (2 , figsize = (15,8 )) mpf.candlestick_ochl (AX1, mat_wdyx, width = 0.6, colorup = ' G ' , colordown = ' R & lt ' , Alpha = 1.0 ) # set date scale rotation angle # ax1.xticks (rotation 30 =) ax1.set_title (code) ax1.set_ylabel ( '. Price ' ) ax1.grid (True) # scale of the x-axis is the date ax1.xaxis_date () plt.bar (mat_wdyx [:, 0] -0.25, mat_wdyx [:,. 5], width = 0.5 ) ax2.set_ylabel ( ' Volume ' ) # scale of the x-axis is the date ax2.xaxis_date () ax2.grid (True) plt.show ()
