本文主要是介绍用python画股票行情图，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
import datetime
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.patches as patches
from CAL.PyCAL import *
quotes:行情-Dateframe类型，sec：标题
def plot_k(quotes, sec):
color_balck= ‘#0F0F0F’
color_green= ‘#00FFFF’
color_yellow = ‘#EE9A00’
color_purple = ‘#9900CC’
linewidth = 2
fig = plt.figure(figsize=(11,6))
fig.set_tight_layout(True)ax1 = fig.add_axes([0, 1, 1, 1])#K线
ax1.set_title(u'K线图', fontproperties=font, fontsize=20)
ax2 = fig.add_axes([0, 0.35, 1, 0.5], axis_bgcolor='w')#成交量
ax1.set_axisbelow(True)
ax2.set_axisbelow(True)ax1.grid(True, axis='y')
ax2.grid(True, axis='y')
ax1.set_xlim(-1, len(quotes)+1)
ax2.set_xlim(-1, len(quotes)+1)for i in range(len(quotes)):close_price = quotes.ix[i, 'closePrice']open_price = quotes.ix[i, 'openPrice']high_price = quotes.ix[i, 'highestPrice']low_price = quotes.ix[i, 'lowestPrice']vol = quotes.ix[i, 'turnoverVol']trade_date = quotes.ix[i, 'tradeDate']if close_price > open_price:#画阳线ax1.add_patch(patches.Rectangle((i-0.2, open_price), 0.4, close_price-open_price, fill=False, color='r'))ax1.plot([i, i], [low_price, open_price], 'r')ax1.plot([i, i], [close_price, high_price], 'r')ax2.add_patch(patches.Rectangle((i-0.2, 0), 0.4, vol, fill=False, color='r'))else:#画阴线ax1.add_patch(patches.Rectangle((i-0.2, open_price), 0.4, close_price-open_price, color='g'))ax1.plot([i, i], [low_price, high_price], color='g')ax2.add_patch(patches.Rectangle((i-0.2, 0), 0.4, vol, color='g'))
ax1.set_title(sec, fontproperties=font, fontsize=15, loc='left', color='r')
ax2.set_title(u'成交量', fontproperties=font, fontsize=15, loc='left', color='r')
#设置标签
ax1.set_xticks(range(0,len(quotes), 15))#位置
ax2.set_xticks(range(0,len(quotes), 15))
s1 = ax1.set_xticklabels([mdates.num2date(quotes.ix[index, 'tradeDate']).strftime('%Y-%m-%d') for index in ax1.get_xticks()])#标签内容
s1 = ax2.set_xticklabels([mdates.num2date(quotes.ix[index, 'tradeDate']).strftime('%Y-%m-%d') for index in ax2.get_xticks()])
#移动平均线
ma5 = pd.rolling_mean(np.array(quotes['closePrice'], dtype=float), window=5, min_periods=0)
ma10 = pd.rolling_mean(np.array(quotes['closePrice'], dtype=float), window=10, min_periods=0)
ma20 = pd.rolling_mean(np.array(quotes['closePrice'], dtype=float), window=20, min_periods=0)ax1.plot(ma5, color='b', linewidth=__linewidth__)
ax1.plot(ma10, color=__color_yellow__, linewidth=__linewidth__)
ax1.plot(ma20, color=__color_purple__, linewidth=__linewidth__)
#图例
ax1.annotate('MA5-', xy=(len(quotes)-30, ax1.get_yticks()[-1]), color='b', fontsize=15)
ax1.annotate('MA10-', xy=(len(quotes)-19, ax1.get_yticks()[-1]), color=__color_yellow__, fontsize=15)
ax1.annotate('MA20-', xy=(len(quotes)-8, ax1.get_yticks()[-1]), color=__color_purple__, fontsize=15)
#交易量均线
vol5 = pd.rolling_mean(np.array(quotes['turnoverVol'], dtype=float), window=5, min_periods=0)
vol10 = pd.rolling_mean(np.array(quotes['turnoverVol'], dtype=float), window=10, min_periods=0)
ax2.plot(vol5, color='b', linewidth=__linewidth__)
ax2.plot(vol10, color=__color_yellow__, linewidth=__linewidth__)return fig
quotes = DataAPI.MktMFutdGet(mainCon=u”1”, contractObject=u”cf”, startDate=u”20151101”,
endDate=u”20160501”,field=[u”closePrice”, u”openPrice”, u”highestPrice”,
u”lowestPrice”, u”tradeDate”,u”turnoverVol”], pandas=”1”)
quotes[‘tradeDate’] = quotes[‘tradeDate’].map(lambda x:mdates.date2num(datetime.datetime.strptime(x,’%Y-%m-%d’)))
fig2 = plot_k(quotes, u’棉花主力[CFM]’)
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