def candle_figure():
data = pd.read_csv('000032.csv')
open = data['open']
high = data['high']
close = data['close']
low = data['low']
dates = np.arange(len(data))
print(dates[:10])
volume = data['volume']
ticker = 'ALBB'
mondays = WeekdayLocator(MONDAY)
alldays = DayLocator()
weekFormatter = DateFormatter('%b %d')
dayFormatter = DateFormatter('%d')
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
ax.xaxis.set_minor_formatter(dayFormatter)
plot_day_summary_oclh(ax, (dates, open, close, high, low))
_candlestick(ax, (dates, open, close, high, low), width=0.6)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=80,
horizontalalignment='right')
plt.show()
def draw_kl2(df: pd.DataFrame, minute=False, day_sum=False):
# 为了示例清晰，只拿出前30天的交易数据绘制蜡烛图，
part_df = df
# fig, ax = plt.subplots(figsize=(14, 7))
fig, ax1 = plt.subplots(figsize=(6, 6))
qutotes = []
if day_sum:
# 端线图绘制
qutotes = []
for index, (d, o, c, l, h) in enumerate(
zip(part_df.time, part_df.open, part_df.close, part_df.high,
part_df.low)):
d = index if minute else dts.date2num(d)
val = (d, o, c, l, h)
qutotes.append(val)
# plot_day_summary_oclh接口，与mpf.candlestick_ochl不同，即数据顺序为开收低高
mpf.plot_day_summary_oclh(ax1,
qutotes,
ticksize=5,
colorup=__colorup__,
colordown=__colordown__)
else:
# k线图绘制
qutotes = []
for index, (d, o, c, h, l) in enumerate(
zip(part_df.time, part_df.open, part_df.close, part_df.high,
part_df.low)):
d = index if minute else dts.date2num(d)
val = (d, o, c, h, l)
qutotes.append(val)
# mpf.candlestick_ochl即数据顺序为开收高低
mpf.candlestick_ochl(ax1,
qutotes,
width=0.6,
colorup=__colorup__,
colordown=__colordown__)
# for index, (d, o, c, h, l) in enumerate(
# zip(part_df.time, part_df.open, part_df.close,
# part_df.high, part_df.low)):
# # 蜡烛图的日期要使用dts.date2num进行转换为特有的数字值
# # d = datetime.datetime.fromtimestamp(d / 1000)
# # d = dts.date2num(d)
# d = index
# # 日期，开盘，收盘，最高，最低组成tuple对象val
# val = (d, o, c, h, l)
# # 加val加入qutotes
# qutotes.append(val)
# 使用mpf.candlestick_ochl进行蜡烛绘制，ochl代表：open，close，high，low
# mpf.candlestick_ochl(ax, qutotes, width=0.6, colorup=__colorup__,
# colordown=__colordown__)
# ax.autoscale_view()
# ax.xaxis_date()
plt.show()
def draw_k_easy(self):
fig, ax = plt.subplots(figsize=(15, 5))
mpf.plot_day_summary_oclh(ax,
self.mat_data,
colorup='r',
colordown='k')
plt.grid(True)
ax.xaxis_date()
plt.title(self.name + ':' + self.code)
plt.ylabel('Price')
plt.show()
''' generamos la ema. recordar que los valores pueden
ser 0 y daria problemas en el grafico.
por eso la generamos aqui.'''
ema50 = fb.get_ema_periodo(50, quotes['Close'])
# select desired range of dates
quotes = quotes[(quotes.index >= date1) & (quotes.index <= date2)]
''' ajustamos los valores de la media a los valores del cierre'''
l = len(quotes['Close'])
ema50 = ema50[-l:]
fig, ax = plt.subplots(figsize=(24, 12))
plot_day_summary_oclh(ax,
zip(date2num(quotes.index.to_pydatetime()),
quotes['Open'], quotes['Close'], quotes['Low'],
quotes['High']),
ticksize=3)
plt.plot(zip(date2num(quotes.index.to_pydatetime())), ema50)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_major_formatter(daysFmt)
ax.autoscale_view()
ax.xaxis.grid(True, 'major')
ax.grid(True)
fig.autofmt_xdate()
plt.show()
quotes = pd.read_csv('AUDNZD.csv',
index_col=0,
parse_dates=True,
infer_datetime_format=True,
sep=";")
# select desired range of dates
quotes = quotes[(quotes.index >= date1) & (quotes.index <= date2)]
fig, ax = plt.subplots(figsize=(20, 10))
ax1 = plt.subplot2grid((8,6), (0,0), colspan=5, rowspan=5)
plot_day_summary_oclh(ax1, zip(date2num(quotes.index.to_pydatetime()),
quotes['apertura'], quotes['cierre'],
quotes['low'], quotes['high']),
ticksize=3)
ax2 = plt.subplot2grid((8,6), (5, 0), colspan=5) # plt.subplot(5, 1, 1, rowspan=3)
plt.plot( zip(date2num(quotes.index.to_pydatetime())), quotes['ema[5]'])
plt.plot( zip(date2num(quotes.index.to_pydatetime())), quotes['ema[50]'])
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_major_formatter(daysFmt)
ax.autoscale_view()
ax.xaxis.grid(True, 'major')
ax.grid(True)
def _do_plot_candle(date, p_open, high, low, close, volume, view_index, symbol, day_sum, save, minute):
"""
绘制不可交互的k线图
param date: 融时间序列交易日时间，pd.DataFrame.index对象
:param p_open: 金融时间序列开盘价格序列，np.array对象
:param high: 金融时间序列最高价格序列，np.array对象
:param low: 金融时间序列最低价格序列，np.array对象
:param close: 金融时间序列收盘价格序列，np.array对象
:param volume: 金融时间序列成交量序列，np.array对象
:param symbol: symbol str对象
:param day_sum: 端线图 matplotlib的版本有些有bug显示不对
:param save: 是否保存可视化结果在本地
:param minute: 是否是绘制分钟k线图
"""
# 需要内部import不然每次import abupy都有warning，特别是子进程很烦人
try:
# noinspection PyUnresolvedReferences, PyDeprecation
import mpl_finance as mpf
except ImportError:
# 2.2 才会有
# noinspection PyUnresolvedReferences, PyDeprecation
import mpl_finance as mpf
if not g_only_draw_price:
# 成交量，价格都绘制
# noinspection PyTypeChecker
fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(20, 12))
else:
# 只绘制价格
fig, ax1 = plt.subplots(figsize=(6, 6))
if day_sum:
# 端线图绘制
qutotes = []
for index, (d, o, c, l, h) in enumerate(zip(date, p_open, close, low, high)):
d = index if minute else mpldates.date2num(d)
val = (d, o, c, l, h)
qutotes.append(val)
# plot_day_summary_oclh接口，与mpf.candlestick_ochl不同，即数据顺序为开收低高
mpf.plot_day_summary_oclh(ax1, qutotes, ticksize=5, colorup=__colorup__, colordown=__colordown__)
else:
# k线图绘制
qutotes = []
for index, (d, o, c, h, l) in enumerate(zip(date, p_open, close, high, low)):
d = index if minute else mpldates.date2num(d)
val = (d, o, c, h, l)
qutotes.append(val)
# mpf.candlestick_ochl即数据顺序为开收高低
mpf.candlestick_ochl(ax1, qutotes, width=0.6, colorup=__colorup__, colordown=__colordown__)
if not g_only_draw_price:
# 开始绘制成交量
ax1.set_title(symbol)
ax1.set_ylabel('ochl')
ax1.grid(True)
if not minute:
ax1.xaxis_date()
if view_index is not None:
# 开始绘制买入交易日，卖出交易日，重点突出的点位
e_list = date.tolist()
# itertools.cycle循环使用备选的颜色
for v, csColor in zip(view_index, itertools.cycle(K_PLT_MAP_STYLE)):
try:
v_ind = e_list.index(v)
except Exception as e:
# logging.exception(e)
logging.debug(e)
# 向前倒一个
v_ind = len(close) - 1
label = symbol + ': ' + str(date[v_ind])
ax1.plot(v, close[v_ind], 'ro', markersize=12, markeredgewidth=4.5,
markerfacecolor='None', markeredgecolor=csColor, label=label)
# 因为candlestick_ochl 不能label了，所以使用下面的显示文字
# noinspection PyUnboundLocalVariable
ax2.plot(v, 0, 'ro', markersize=12, markeredgewidth=0.5,
markerfacecolor='None', markeredgecolor=csColor, label=label)
plt.legend(loc='best')
# 成交量柱子颜色，收盘价格 > 开盘，即红色
# noinspection PyTypeChecker
bar_red = np.where(close >= p_open, volume, 0)
# 成交量柱子颜色，开盘价格 > 收盘。即绿色
# noinspection PyTypeChecker
bar_green = np.where(p_open > close, volume, 0)
date = date if not minute else np.arange(0, len(date))
ax2.bar(date, bar_red, facecolor=__colorup__)
ax2.bar(date, bar_green, facecolor=__colordown__)
ax2.set_ylabel('volume')
ax2.grid(True)
ax2.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=30)
else:
ax1.grid(False)
if save:
# 保存可视化结果在本地
from pylab import savefig
save_dir = os.path.join(K_SAVE_CACHE_PNG_ROOT, ABuDateUtil.current_str_date())
png_dir = os.path.join(save_dir, symbol)
ABuFileUtil.ensure_dir(png_dir)
r_cnt = 0
while True:
png_name = '{}{}.png'.format(png_dir, '' if r_cnt == 0 else '-{}'.format(r_cnt))
if not ABuFileUtil.file_exist(png_name):
break
r_cnt += 1
# noinspection PyUnboundLocalVariable
savefig(png_name)
fig.clf()
plt.close(fig)
else:
"""
save 了就不show了
"""
plt.show()
fig.subplots_adjust(bottom=0.5)
mpf.candlestick_ochl(ax,
mat_wdyx,
width=0.6,
colorup='g',
colordown='r',
alpha=1.0)
plt.grid(True)
# 设置日期刻度旋转的角度
plt.xticks(rotation=30)
plt.title('wanda yuanxian 17')
plt.xlabel('Date')
plt.ylabel('Price')
# x轴的刻度为日期
ax.xaxis_date()
fig, ax = plt.subplots(figsize=(15, 5))
mpf.plot_day_summary_oclh(ax, mat_wdyx, colorup='g', colordown='r')
plt.grid(True)
ax.xaxis_date()
plt.title('wandayuanxian 17')
plt.ylabel('Price')
fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(15, 8))
mpf.candlestick_ochl(ax1, mat_wdyx, width=1.0, colorup='g', colordown='r')
ax1.set_title('wandayuanxian')
ax1.set_ylabel('Price')
ax1.grid(True)
ax1.xaxis_date()
plt.bar(mat_wdyx[:, 0] - 0.25, mat_wdyx[:, 5], width=0.5)
ax2.set_ylabel('Volume')
ax2.grid(True)
quotes = pd.read_csv('data/yahoofinance-INTC-19950101-20040412.csv',
index_col=0,
parse_dates=True,
infer_datetime_format=True)
"""
dateparse = lambda x: pd.datetime.strptime(x, '%Y%m%d')
quotes = pd.read_csv(
'/Users/momantang/work/cobrass/data/000001.sh.csv',
parse_dates=['trade_date'],
)
# select desired range of dates
# quotes = quotes[(quotes.index >= date1) & (quotes.index <= date2)]
print(quotes.dtypes)
fig, ax = plt.subplots()
plot_day_summary_oclh(ax,
zip(quotes['trade_date'], quotes['open'],
quotes['close'], quotes['low'], quotes['high']),
ticksize=3)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_major_formatter(daysFmt)
ax.autoscale_view()
ax.xaxis.grid(True, 'major')
ax.grid(True)
fig.autofmt_xdate()
plt.show()
