def plot_stock_line(code,start):
fig = plt.figure(figsize=(10,15))
# fig,(ax,ax2)=plt.subplots(2,1,sharex=True,figsize=(16,10))
ax=fig.add_axes([0,0.2,1,0.5])
ax2=fig.add_axes([0,0,1,0.2])
df = ts.bar(code,conn=api,start_date=start)
# df=df.sort_values(by='datetime')
df = df.sort_index()
df =df.reset_index()
# df = ts.get_k_data('300141',start='2018-03-01')
# df['date']=df['date'].dt.strftime('%Y-%m-%d')
df['datetime']=df['datetime'].dt.strftime('%Y-%m-%d')
sma5=talib.SMA(df['close'].values,5)
sma20=talib.SMA(df['close'].values,20)
# ax.set_xticks(range(0,len(df),20))
# # ax.set_xticklabels(df['date'][::5])
# ax.set_xticklabels(df['datetime'][::20])
candlestick2_ochl(ax,df['open'],df['close'],df['high'],df['low'],width=0.5,colorup='r',colordown='g',alpha=0.6)
# ax.set_title(code)
ax.plot(sma5)
ax.plot(sma20)
# df['vol'].plot(kind='bar')
volume_overlay(ax2,df['open'],df['close'],df['vol'],width=0.5,alpha=0.8,colordown='g',colorup='r')
ax2.set_xticks(range(0,len(df),20))
# ax.set_xticklabels(df['date'][::5])
ax2.set_xticklabels(df['datetime'][::20])
# ax2.grid(True)
# plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
# plt.grid(True)
# plt.subplots_adjust(hspace=0)
plt.show()
def drawCandlestick(index, path):
fig = plt.figure(figsize=(2, 2))
ax = fig.add_axes([0, 0.3, 1, 0.7]) # [left, bottom, width, height]
ax2 = fig.add_axes([0, 0, 1, 0.3])
# 画蜡烛图
mpf.candlestick2_ochl(ax,
data['open'][index - 20:index],
data['close'][index - 20:index],
data['high'][index - 20:index],
data['low'][index - 20:index],
width=0.5,
colorup='r',
colordown='g',
alpha=1)
# 画交易量
mpf.volume_overlay(ax2,
data['open'][index - 20:index],
data['close'][index - 20:index],
data['volume'][index - 20:index],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
# 去掉坐标轴刻度
ax.set_xticks([])
ax.set_yticks([])
ax2.set_xticks([])
ax2.set_yticks([])
plt.savefig(path + str(int(index)) + ".jpg")
def plot_k(data, code, attachs, d_channel = False):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
from matplotlib import gridspec
import os
fig = plt.figure(figsize = (10,6))
gs = gridspec.GridSpec(2,1,height_ratios=[2, 0.8])
ax = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
mpf.candlestick2_ochl(ax, data['open'], data['close'], data['high'], data['low'],
width=0.6, colorup='red', colordown='green', alpha=1)
# 绘制唐奇安通道
if d_channel:
ax.plot(data['date'], data['d_up'], color='r', label='Donchian Channel Up: {} days'.format(D_Channel['up']))
ax.plot(data['date'], data['d_down'], color='b',
label='Donchian Channel Down: {} days'.format(D_Channel['down']))
ax.legend()
ax.set_title(code)
ax.grid(True)
mpf.volume_overlay(ax2, data['open'], data['close'], data['volume'], colorup='r', colordown='g', width=0.2, alpha=1)
ax2.set_xticks(range(0, len(data['date']),2))
ax2.set_xticklabels(data['date'][::2], rotation=45)
ax2.grid(True)
plt.subplots_adjust(hspace = 0)
figPath = os.getcwd()+ '/{}.png'.format(code)
fig.savefig(figPath,dpi = 100)
# plt.show()
attachs.append(figPath)
def __plt_volume(self, ax):
ax2 = ax.twinx()
ax2.set_ylim([0, max(self.ohcl["volume"]) * 4])
ax2.set_ylabel("volume")
finance.volume_overlay(ax2,
self.ohcl["open"],
self.ohcl["close"],
self.ohcl["volume"],
colorup='r',
colordown='g',
width=0.5,
alpha=0.5)
def plot_stock_line(code, start):
fig = plt.figure(figsize=(10, 15))
# fig,(ax,ax2)=plt.subplots(2,1,sharex=True,figsize=(16,10))
ax = fig.add_axes([0, 0.2, 1, 0.5])
ax2 = fig.add_axes([0, 0, 1, 0.2])
df = ts.bar(code, conn=api, start_date=start)
# df=df.sort_values(by='datetime')
df = df.sort_index()
df = df.reset_index()
# df = ts.get_k_data('300141',start='2018-03-01')
# df['date']=df['date'].dt.strftime('%Y-%m-%d')
df['datetime'] = df['datetime'].dt.strftime('%Y-%m-%d')
sma5 = talib.SMA(df['close'].values, 5)
sma20 = talib.SMA(df['close'].values, 20)
# ax.set_xticks(range(0,len(df),20))
# # ax.set_xticklabels(df['date'][::5])
# ax.set_xticklabels(df['datetime'][::20])
candlestick2_ochl(ax,
df['open'],
df['close'],
df['high'],
df['low'],
width=0.5,
colorup='r',
colordown='g',
alpha=0.6)
# ax.set_title(code)
ax.plot(sma5)
ax.plot(sma20)
# df['vol'].plot(kind='bar')
volume_overlay(ax2,
df['open'],
df['close'],
df['vol'],
width=0.5,
alpha=0.8,
colordown='g',
colorup='r')
ax2.set_xticks(range(0, len(df), 20))
# ax.set_xticklabels(df['date'][::5])
ax2.set_xticklabels(df['datetime'][::20])
# ax2.grid(True)
# plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
# plt.grid(True)
# plt.subplots_adjust(hspace=0)
plt.show()
def plot_volume_overlay(ax,_dfquotes,begin,_locatormulti=40):
"""
plot volume bar with m5,10 line
Args:
_dfquotes (dataframe): the seurity dataframe which include'openPrice''closePrice''turnoverVol''tradeDate'
ax : matplotlib ax object
begin : the index of first volume bar, _dfquotes[begin:] will be plot
_locatormulti (int): adjust the axis's ticker display interval
Returns:
lineandbars : return volume_overlay's lineandbars objects
Examples:
>>
"""
global _tickerstart
global dfquotes
dfquotes = _dfquotes
_tickerstart = begin
lineandbars = maf.volume_overlay(ax,dfquotes[u'openPrice'][_tickerstart:].values,\
dfquotes[u'closePrice'][_tickerstart:].values,\
dfquotes[u'turnoverVol'][_tickerstart:].values,\
colorup='r',colordown='g',width=0.5,alpha=1.0)
lineandbars.set_edgecolor(lineandbars.get_facecolor())
_multilocator = int((len(dfquotes)-_tickerstart)/_locatormulti)#超过40个交易日，日期刻度单位加一天
ax.xaxis.set_major_locator(MultipleLocator(1+_multilocator))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
plt.gcf().autofmt_xdate()
_doublelist = [float(x) for x in dfquotes[u'turnoverVol'].values]
_doublenparray = np.array(_doublelist)
plotEMA([5,10],_doublenparray,_tickerstart)
for label in ax.xaxis.get_ticklabels():
label.set_rotation(90)
return lineandbars
def candlestick_vol(quotes):
SCALE = 5
COLORUP = 'red'
COLORDOWN = 'green'
fig, (ax, ax2) = plt.subplots(2, 1, sharex=True, figsize=(17, 10))
candlestick2_ochl(ax,
quotes['open'],
quotes['close'],
quotes['high'],
quotes['low'],
width=0.5,
colorup=COLORUP,
colordown=COLORDOWN,
alpha=0.6)
ax.set_xticks(range(0, len(quotes.index), SCALE))
ax.set_ylabel('Quote')
ax.grid(True)
bc = volume_overlay(ax2,
quotes['open'],
quotes['close'],
quotes['volume'],
colorup=COLORUP,
colordown=COLORDOWN,
width=0.5,
alpha=0.6)
ax2.add_collection(bc)
ax2.set_xticks(range(0, len(quotes.index), SCALE))
ax2.set_xticklabels(quotes.index[::SCALE], rotation=30)
ax2.grid(True)
plt.subplots_adjust(hspace=0.01)
plt.show()
def plot_ohlcv(self, df):
fig, ax = plt.subplots()
# Plot the candlestick
candlestick2_ohlc(ax,
df['open'],
df['high'],
df['low'],
df['close'],
width=1,
colorup='g',
colordown='r',
alpha=0.5)
# shift y-limits of the candlestick plot so that there is space
# at the bottom for the volume bar chart
pad = 0.25
yl = ax.get_ylim()
ax.set_ylim(yl[0] - (yl[1] - yl[0]) * pad, yl[1])
# Add a seconds axis for the volume overlay
ax2 = ax.twinx()
ax2.set_position(
matplotlib.transforms.Bbox([[0.125, 0.1], [0.9, 0.26]]))
# Plot the volume overlay
bc = volume_overlay(ax2,
df['open'],
df['close'],
df['volume'],
colorup='g',
alpha=0.5,
width=1)
ax.xaxis.set_major_locator(ticker.MaxNLocator(6))
def mydate(x, pos):
try:
return df.index[int(x)]
except IndexError:
return ''
ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
plt.margins(0)
plt.show()
def plot_with_price(quotes, save_to_file=None, min_max=[]):
# fig = plt.figure()
fig, ax = plt.subplots(figsize=(30, 15))
# ax.set_xlim([1,2])
# ax.set_ylim(min_max)
candlestick2_ohlc(ax,
quotes['open'],
quotes['high'],
quotes['low'],
quotes['close'],
width=0.6)
xdate = [i for i in quotes.index]
ax.xaxis.set_major_locator(ticker.MaxNLocator(6))
def mydate(x, pos):
try:
return xdate[int(x)]
except IndexError:
return ''
ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
ax2 = ax.twinx()
# ax2.set_ylim([0,5000])
# Plot the volume overlay
bc = volume_overlay(ax2,
quotes['open'],
quotes['close'],
quotes['volume'],
colorup='g',
alpha=0.1,
width=1)
ax2.add_collection(bc)
fig.autofmt_xdate()
fig.tight_layout()
# plt.show()
if (save_to_file is not None):
plt.savefig(save_to_file)
def chart(symbol):
data = df[df['<Ticker>'] == symbol][['<DTYYYYMMDD>','<Open>','<High>','<Low>','<Close>','<Volume>']]
fig = plt.figure()
ax = plt.subplot2grid((4,4), (0,0), rowspan=3, colspan=4)
data['<DTYYYYMMDD>'] = data['<DTYYYYMMDD>'].apply(lambda x: datetime.strptime(str(x), '%Y%m%d'))
data['<DTYYYYMMDD>'] = data['<DTYYYYMMDD>'].map(dt.date2num)
candlestick_ohlc(ax, data.values, width=0.5, colorup='#53c156', colordown='#ff1717')
ax.set_title(symbol)
ax.set_ylabel('Price')
ax.set_xlabel('Date')
ax.axes.get_xaxis().set_visible(False)
ax2 = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4)
bc = volume_overlay(ax2, data['<Open>'][::-1], data['<Close>'][::-1], data['<Volume>'][::-1], colorup='g', alpha=0.5, width=1)
ax2.add_collection(bc)
# ax2.xaxis_date()
# ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
# fig.autofmt_xdate()
# ax2.grid(True)
plt.show()
return data
def plot(self, widget):
self.widget = widget
finance.volume_overlay(widget, self.open, self.close, self.volume,
self.colorup, self.colordown, self.width)
transform=axMiddle.transAxes)
# now do the moviing average. I'll use a convolution to simulate a
# real moving average
ma5 = movavg(vopens, 5)
ma20 = movavg(vopens, 20)
axMiddle.plot(vind[5-1:], ma5, 'b', linewidth=1)
axMiddle.plot(vind[20-1:], ma20, 'r', linewidth=1)
axMiddle.set_ylim((20, 32))
axMiddle.set_yticks((25,30))
# Now do the volume overlay
bars = volume_overlay(axMiddleVol, opens, closes, volumes, alpha=0.5)
axMiddleVol.set_ylim((0, 3*max(vvolumes))) # use only a third of the viewlim
if 1: ############### Lower axes #################
# make up two signals; I don't know what the signals are in real life
# so I'll just illustrate the plotting stuff
s1 = random_signal(len(vind), 10)
s2 = random_signal(len(vind), 20)
axLower.plot(vind, s1, color=purple)
axLower.plot(vind, s2, color='k', linewidth=1.0)
s3 = s2-s1
axLower.plot(vind, s3, color='#cccc99') # wheat
bars = index_bar(axLower, s3, width=2, alpha=0.5,
fontsize=textsize,
transform=axMiddle.transAxes)
# now do the moviing average. I'll use a convolution to simulate a
# real moving average
ma5 = movavg(vopens, 5)
ma20 = movavg(vopens, 20)
axMiddle.plot(vind[5 - 1:], ma5, 'b', linewidth=1)
axMiddle.plot(vind[20 - 1:], ma20, 'r', linewidth=1)
axMiddle.set_ylim((20, 32))
axMiddle.set_yticks((25, 30))
# Now do the volume overlay
bars = volume_overlay(axMiddleVol, opens, closes, volumes, alpha=0.5)
axMiddleVol.set_ylim(
(0, 3 * max(vvolumes))) # use only a third of the viewlim
if 1: ############### Lower axes #################
# make up two signals; I don't know what the signals are in real life
# so I'll just illustrate the plotting stuff
s1 = random_signal(len(vind), 10)
s2 = random_signal(len(vind), 20)
axLower.plot(vind, s1, color=purple)
axLower.plot(vind, s2, color='k', linewidth=1.0)
s3 = s2 - s1
axLower.plot(vind, s3, color='#cccc99') # wheat
bars = index_bar(axLower,
#new_xticks = [datetime.date.isoformat(num2date(d)) for d in xt]
#ax1.set_xticklabels(new_xticks, rotation=45, horizontalalignment='right')
# shift y-limits of the candlestick plot so that there is space at the bottom for the volume bar chart
pad = 0.5
yl = ax1.get_ylim()
ax1.set_ylim(yl[0] - (yl[1] - yl[0]) * pad, yl[1])
xl = ax1.get_xlim()
ax1.set_xlim(xl[0], 3 * xl[1])
ax2 = ax1.twinx()
bc = volume_overlay(ax2,
input_df.open,
input_df.close,
input_df.volume,
colorup='#0d8cec',
colordown='#ff82c7',
alpha=0.1,
width=1)
ax2.add_collection(bc)
'''
ax3 = plt.subplot(212, sharex=ax1)
ax3.grid()
ax3.plot(input_df.close, color="black")
ax3.plot(log_bband_middle, color="red")
ax3.fill_between(log_bband_middle.index, log_bband_upper.values.flatten(), log_bband_lower.values.flatten(),
facecolor="dodgerblue", alpha=0.5)
ax3.legend(["Close", "Middle", "Bollinger (2-SD)"], loc="upper left", fontsize=10)
ax3.set_ylabel("Log price")
ax3.set_xlabel("Time")
BB20[:, 0],
BB20[:, 2],
facecolor='#cccccc',
alpha=0.5)
ax0.set_xticks(tickindex)
ax0.set_xticklabels(ticknames)
ax0.format_coord = format_coord1
ax0.legend(loc='best', shadow=True, fancybox=True)
ax0.set_ylabel('Price($)', fontsize=16)
ax0.set_title(ticker, fontsize=24, fontweight='bold')
ax0.grid(True)
ax1 = plt.subplot(gs[1], sharex=ax0)
vc = volume_overlay(ax1, r.open, r.close, r.volume, colorup='g', width=1)
ax1.set_xticks(tickindex)
ax1.set_xticklabels(ticknames)
ax1.format_coord = format_coord2
ax1.tick_params(axis='x', direction='out', length=5)
ax1.yaxis.set_major_formatter(millionformatter)
ax1.yaxis.tick_right()
ax1.yaxis.set_label_position("right")
ax1.set_ylabel('Volume', fontsize=16)
ax1.grid(True)
plt.setp(ax0.get_xticklabels(), visible=False)
plt.show()
def __plot(self, stock, figsize, qfq, index, index_overlay=False):
stock_data = stock.qfq_data if qfq else stock.hist_data
stock_data = stock_data.sort_index(ascending=True, inplace=False)
index_data = index.hist_data.sort_index(ascending=True)
data_err_found = False
fp = FontProperties(fname='simsun.ttc')
fig = plt.figure(figsize=figsize, dpi=100)
fig.subplots_adjust(left=.10, bottom=.09, right=.93, top=.95, wspace=.20, hspace=0)
gs = gridspec.GridSpec(3, 1, height_ratios=[4, 1, 1])
# draw hist price diagram
ax_price = plt.subplot(gs[0])
candlestick2_ochl(ax_price, stock_data.open, stock_data.high, stock_data.low, stock_data.close,
width=.75, colorup='g', colordown='r', alpha=0.75)
for i, factor in enumerate(stock_data.factor):
if i != 0 and round(factor, 2) != round(stock_data.factor[i-1], 2):
ax_price.annotate('Q(f=%.3f)' % factor,
xy=(i, stock_data.open[i]), xycoords='data',
xytext=(0, stock_data.high.max()/10), textcoords='offset points', ha='center', va='bottom',
bbox=dict(boxstyle='round,pad=0.2', fc='blue', alpha=0.3),
arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"),
fontsize=10, color='c')
for i, date in enumerate(stock_data.index):
if i == stock_data.index.size -1:
break
next_date = stock_data.index[i + 1]
if date not in index_data.index or next_date not in index_data.index:
logging.warning('%s: data date %s or %s is not in index %s, probably additional wrong data'
% (stock, date, next_date, index.name))
data_err_found = True
break
index_loc = index_data.index.get_loc(date)
if index_data.index[index_loc+1] != stock_data.index[i+1]:
suspended_days = index_data.index.get_loc(next_date) - index_loc
ax_price.annotate('suspend %ddays [%s - %s]' % (suspended_days, date, stock_data.index[i+1]),
xy=(i, stock_data.open[i]), xycoords='data',
xytext=(0, stock_data.high.max()/10), textcoords='offset points', ha='center', va='bottom',
bbox=dict(boxstyle='round,pad=0.2', fc='yellow', alpha=0.3),
arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"),
fontsize=10, color='y')
left, height, top = 0.025, 0.03, 0.9
t1 = ax_price.text(left, top, '%s-%s,%s,%s' % (stock.code, stock.name, stock.area, stock.industry),
fontproperties=fp, fontsize=8, transform=ax_price.transAxes)
ax_price.text(left, top - height, 'pe=%.2f' % (stock.pe if stock.pe else 0.0), fontsize=8, transform=ax_price.transAxes)
ax_price.text(left, top - 2*height, 'nmc=%.2f亿' % (stock.nmc/10000 if stock.nmc else 0.0), fontproperties=fp, fontsize=8, transform=ax_price.transAxes)
ax_price.text(left, top - 3*height, 'mktcap=%.2f亿' % (stock.mktcap/10000 if stock.mktcap else 0.0), fontproperties=fp, fontsize=8, transform=ax_price.transAxes)
if not qfq:
ax_price.text(left, top - 4*height, 'EMA(5)', color='b', fontsize=8, transform=ax_price.transAxes)
ax_price.text(left, top - 5*height, 'EMA(10)', color='y', fontsize=8, transform=ax_price.transAxes)
ax_price.text(left, top - 6*height, 'EMA(20)', color='g', fontsize=8, transform=ax_price.transAxes)
ax_price.text(left, top - 7*height, 'EMA(30)', color='r', fontsize=8, transform=ax_price.transAxes)
ax_price.plot(stock_data.ma5.values, color='b', lw=1)
ax_price.plot(stock_data.ma10.values, color='y', lw=1)
ax_price.plot(stock_data.ma20.values, color='g', lw=1)
ax_price.plot(stock.ma30.sort_index(ascending=True).close.values, color='r', lw=1)
#ax_price.plot(stock.ma60.sort_index(ascending=True).close.values, color='r', lw=1)
#ax_price.plot(stock.ma120.sort_index(ascending=True).close.values, color='r', lw=1)
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
stock_data.index[-1],
stock_data.open[-1], stock_data.high[-1], stock_data.low[-1], stock_data.close[-1],
stock_data.volume[-1] * 1e-6,
stock_data.close[-1] - stock_data.open[-1])
ax_price.text(0.5, top, s, fontsize=8, transform=ax_price.transAxes)
plt.ylabel('Price')
plt.ylim(ymin=stock_data.low.min()-stock_data.low.min()/30, ymax=stock_data.high.max()+stock_data.high.max()/30)
ax_price.grid(True)
if qfq:
plt.title('Forward Adjusted History Price')
else:
plt.title('History Price')
xrange = range(0, stock_data.index.size, max(int(stock_data.index.size / 5), 5))
plt.xticks(xrange, [stock_data.index[loc] for loc in xrange])
plt.setp(ax_price.get_xticklabels(), visible=False)
# draw index overlay
if index_overlay:
common_index = index_data.index.intersection(stock_data.index)
common_data = index_data.join(DataFrame(index=common_index), how='inner')
common_data.sort_index(ascending=True, inplace=True)
ax_index = ax_price.twinx()
candlestick2_ochl(ax_index, common_data.open, common_data.high, common_data.low, common_data.close,
width=.75, colorup='g', colordown='r', alpha=0.35)
ax_index.set_ylabel('Index(%s)' % index.name, fontproperties=fp)
ax_index.set_ylim(ymin=common_data.low.min(), ymax=common_data.high.max())
# draw hist volume diagram
ax_volume = plt.subplot(gs[1], sharex=ax_price)
volume_overlay(ax_volume, stock_data.open, stock_data.close, stock_data.volume,
width=.75, colorup='g', colordown='r', alpha=0.75)
ax_volume.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: '%1.1fM' % (x*1e-6)
if stock_data.volume.max()>1e6 else '%1.1fK' % (x*1e-3)))
if not qfq:
ax_volume.plot(stock_data.v_ma5.values, color='b', lw=1)
ax_volume.plot(stock_data.v_ma10.values, color='y', lw=1)
ax_volume.plot(stock_data.v_ma20.values, color='r', lw=1)
plt.setp(ax_volume.get_xticklabels(), visible=False)
ax_volume.yaxis.set_ticks_position('both')
ax_volume.set_ylabel('Volume')
ax_volume.grid(True)
# draw hist turnover diagram
ax_turnover = plt.subplot(gs[2], sharex=ax_price)
volume_overlay(ax_turnover, stock_data.open, stock_data.close, stock_data.turnover,
width=.75, colorup='g', colordown='r', alpha=0.75)
ax_turnover.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: '%s' % (stock_data.index[x])))
ax_turnover.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: '%.2f%%' % (x)))
for label in ax_turnover.xaxis.get_ticklabels():
label.set_rotation(0)
ax_turnover.set_xlabel('Date')
ax_turnover.yaxis.set_ticks_position('both')
ax_turnover.set_ylabel('Turnover')
ax_turnover.grid(True)
# plt.legend(prop=fp)
# show error warning
if data_err_found:
ax_price.text(1.01, 0.02, '(>_<) ', color='r', fontsize=10, transform=ax_price.transAxes)
self._ax_price = ax_price
self._ax_volume = ax_volume
self._ax_turnover = ax_turnover
ax0.legend(loc='best')
ax0.set_ylabel(u'株価', fontproperties=fp, fontsize=16)
#ax0.xaxis_date()
#ax0.autoscale_view()
ax1 = plt.subplot(gs[1], sharex=ax0)
#vc = volume_overlay3(ax1, quotes, colorup='k', colordown='r', width=4, alpha=1.0)
#volume_overlay(ax1, opens, closes, volumes, colorup='g', alpha=0.5, width=1)
#ax1.set_xticks(ds)
vc = volume_overlay(ax1,
opens,
closes,
volumes,
colorup='g',
alpha=0.5,
width=1)
ax1.add_collection(vc)
ax1.xaxis.set_major_locator(get_locator())
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(millionformatter)
ax1.yaxis.tick_right()
ax1.set_ylabel(u'出来高', fontproperties=fp, fontsize=16)
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), rotation=30, horizontalalignment='left')
plt.legend(prop=fp)
def Ohlc(Open,High,Low,Close,Volume,MACD):
# 出来高,MACDのy軸ラベルとチャート,出来高のx軸のラベルを非表示
ax.tick_params(labelbottom="off")
ax2.tick_params(labelbottom="off",labelleft='off')
mpf.candlestick2_ohlc(ax,Open,High,Low,Close,width=0.8,colorup='#008800',colordown='#ff0000',alpha=0.75)
mpf.volume_overlay(ax2,Open,Close,Volume,width=1,colorup='#008800',colordown='#ff0000')
rotation=90) # 2017.12.31
ax.set_xlim([0, df.shape[0]]) # 横軸の範囲はデータの個数(df.shape[0]個)までに変更しておく
ax.set_ylabel("Price")
plt.grid(color='y', linestyle='-')
# ローソク足を上側75%に収める
bottom, top = ax.get_ylim()
ax.set_ylim(bottom - (top - bottom) / 4, top)
# 出来高のチャートをプロット
ax2 = ax.twinx()
volume_overlay(ax2,
df["Open"],
df["Adj Close"],
df["Volume"],
width=1,
colorup="g",
colordown="g")
ax2.set_xlim([0, df.shape[0]])
# 出来高チャートは下側25%に収める
ax2.set_ylim([0, df["Volume"].max() * 4])
ax2.set_ylabel("Volume")
formatter = EngFormatter()
ax2.yaxis.set_major_formatter(formatter)
plt.show()
""" Plot Adj Close Graph"""
# 5days, 25days移動平均の計算
MA5 = stock_name['Adj Close'].rolling(window=5).mean()
MA25 = stock_name['Adj Close'].rolling(window=25).mean()
ax0.plot(range(len(r.date)), BB20[:,0], color='black', lw=2, ls='--', label='BBU (20)')
ax0.plot(range(len(r.date)), BB20[:,1], color='black', lw=2, ls='--', label='BBM (20)')
ax0.plot(range(len(r.date)), BB20[:,2], color='black', lw=2, ls='--', label='BBD (20)')
ax0.fill_between(range(len(r.date)), BB20[:,0],BB20[:,2], facecolor='#cccccc', alpha=0.5)
ax0.set_xticks(tickindex)
ax0.set_xticklabels(ticknames)
ax0.format_coord=format_coord1
ax0.legend(loc='best', shadow=True, fancybox=True)
ax0.set_ylabel('Price($)', fontsize=16)
ax0.set_title(ticker, fontsize=24, fontweight='bold')
ax0.grid(True)
ax1 = plt.subplot(gs[1], sharex=ax0)
vc = volume_overlay(ax1, r.open, r.close, r.volume, colorup='g', width=1)
ax1.set_xticks(tickindex)
ax1.set_xticklabels(ticknames)
ax1.format_coord=format_coord2
ax1.tick_params(axis='x',direction='out',length=5)
ax1.yaxis.set_major_formatter(millionformatter)
ax1.yaxis.tick_right()
ax1.yaxis.set_label_position("right")
ax1.set_ylabel('Volume', fontsize=16)
ax1.grid(True)
plt.setp(ax0.get_xticklabels(), visible=False)
plt.show()
def plot(self, widget):
import matplotlib.finance as finance
self.widget = widget
finance.volume_overlay(widget, self.open, self.close, self.volume,
self.colorup, self.colordown, self.width)
def kplot(df: pd.DataFrame, picturePath: str):
"""根据给的120day的交易日数据画出K线图
:param df: 传进的DataFrame是120天的交易日日数据
:param picturePath: 图片保存路径
"""
df['ii'] = range(len(df))
ohlc = df['ii'].map(lambda x: tuple(df.iloc[
x][['时间', '开盘价(元)', '最高价(元)', '最低价(元)', '收盘价(元)']])).tolist()
# 生成了120个(i,time,open,high,low,close)的tuple 这里注意这里处理了nan
weekday_ohlc = [
tuple([i] +
list([0 if str(x) == 'nan' else float(x) for x in item[1:]]))
for i, item in enumerate(ohlc)
]
week_line = [
0 if str(x) == 'nan' else float(x) for x in df['周線'].tolist()
] # 一列的list
month_line = [
0 if str(x) == 'nan' else float(x) for x in df['月線'].tolist()
]
quarter_line = [
0 if str(x) == 'nan' else float(x) for x in df['季線'].tolist()
]
half_line = [
0 if str(x) == 'nan' else float(x) for x in df['半年線'].tolist()
]
year_line = [0 if str(x) == 'nan' else float(x) for x in df['年線'].tolist()]
open = [0 if str(x) == 'nan' else float(x) for x in df['开盘价(元)'].tolist()]
close = [0 if str(x) == 'nan' else float(x) for x in df['收盘价(元)'].tolist()]
volume = [
0 if str(x) == 'nan' else float(x) for x in df['成交量(股)'].tolist()
]
kdj_k = [0 if str(x) == 'nan' else float(x) for x in df['KDJ-K'].tolist()]
kdj_d = [0 if str(x) == 'nan' else float(x) for x in df['KDJ-D'].tolist()]
kdj_j = [0 if str(x) == 'nan' else float(x) for x in df['KDJ-J'].tolist()]
fig = plt.figure(figsize=(18, 10)) # 设置形状的宽度和高度
p1 = fig.add_axes([0, 0.6, 1, 0.6])
p1.axes.get_xaxis().set_visible(False)
plt.xlim(0, len(weekday_ohlc) - 1) # 设置一下x轴的范围
candlestick_ohlc(p1, weekday_ohlc, width=0.6, colorup='r', colordown='g')
# 年线
plt.plot(week_line, linewidth=1.5, color='orange')
plt.plot(month_line, linewidth=1.5, color='cyan')
plt.plot(quarter_line, linewidth=1.5, color='magenta')
plt.plot(half_line, linewidth=1.5, color='navy')
plt.plot(year_line, linewidth=1.5, color='olive')
p2 = fig.add_axes([0, 0.4, 1, 0.2])
p2.axes.get_xaxis().set_visible(False)
plt.xlim(0, len(weekday_ohlc) - 1) # 再次设置一下x轴的范围
vc = volume_overlay(p2,
open,
close,
volume,
colorup='g',
alpha=0.5,
width=1) # 成交量
p3 = fig.add_axes([0, 0.2, 1, 0.2])
p3.axes.get_xaxis().set_visible(False) # 关闭x轴
plt.xlim(0, len(weekday_ohlc) - 1) # 再次设置一下x轴的范围
plt.plot(kdj_k, linewidth=2, color='yellow')
plt.plot(kdj_d, linewidth=2, color='blue')
plt.plot(kdj_j, linewidth=2, color='purple')
plt.savefig(picturePath, bbox_inches='tight', pad_inches=0) #保存图片
plt.close()
def plot(self, widget, colorup = 'r', colordown = 'b', width = 1):
"""docstring for plot"""
finance.volume_overlay(widget, self.open, self.close, self.volume, colorup, colordown, width)
data = pd.read_csv('sh1.csv')
num = 100 #画前num个交易日的图
fig = plt.figure(figsize=(15, 8))
ax = fig.add_axes([0, 0.2, 1, 0.5])
ax2 = fig.add_axes([0, 0, 1, 0.2])
mpf.candlestick2_ochl(ax,
data['open'][:num],
data['close'][:num],
data['high'][:num],
data['low'][:num],
width=0.5,
colorup='r',
colordown='g',
alpha=1)
ax.set_xticks(range(0, num, 10))
ax.grid(True)
mpf.volume_overlay(ax2,
data['open'][:num],
data['close'][:num],
data['volume'][:num],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax2.set_xticks(range(0, num, 10))
ax2.set_xticklabels(data['date'][::10], rotation=30)
ax2.grid(True)
#plt.subplots_adjust(hspace=0)
def plot(self, widget):
import matplotlib.finance as finance
self.widget = widget
finance.volume_overlay(widget, self.open, self.close, self.volume,
self.colorup, self.colordown, self.width)
def createFig(self):
"""
生成图片：
1、K线图，包含唐奇安通道，成交量, 入场位置
2、ATR图
3、MACD图
:return:
"""
for code, data in self.stock_data.items():
fig = plt.figure(figsize=(10, 6))
gs = gridspec.GridSpec(4, 1, height_ratios=[2, 0.4, 0.8, 0.8])
ax = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
ax3 = plt.subplot(gs[2])
ax4 = plt.subplot(gs[3])
# 绘制K线图
mpf.candlestick2_ochl(ax,
data['open'],
data['close'],
data['high'],
data['low'],
width=0.6,
colorup='red',
colordown='green',
alpha=1)
# 绘制入场点
entry_date = self.portfolio[code]['entry_date']
entry_date = pd.to_datetime(entry_date, format='%Y-%m-%d')
print entry_date
entry_price = self.portfolio[code]['entry_price']
# for i in range(len(entry_date)):
# ax.annotate("{}".format(entry_price[i]), xy=(entry_date[i], entry_price[i] * 0.95), xytext=(entry_date[i], entry_price[i]*0.9),
# arrowprops=dict(facecolor='R', shrink = 0.05),
# horizontalalignment='left', verticalalignment='top')
# ax.axhline(entry_price[i], xmin = 1 - self.delta_days * 1.0 /self.back_days,color="y", linestyle="-.")
# 绘制唐奇安通道
data['date'] = pd.to_datetime(data['date'], format='%Y-%m-%d')
print data['date']
#datetime.datetime.strptime(last, '%Y-%m-%d')
ax.xaxis.set_major_formatter(mdate.DateFormatter('%b %d', None))
ax.plot(data['date'],
data['d_up'],
color='r',
label='Up: {} days'.format(self.D_Channel['up']))
ax.plot(data['date'],
data['d_down'],
color='b',
label='Down: {} days'.format(self.D_Channel['down']))
ax.legend(loc=0)
ax.set_title(code)
ax.grid(True)
# 绘制成交量图
mpf.volume_overlay(ax2,
data['open'],
data['close'],
data['volume'],
colorup='r',
colordown='g',
width=0.2,
alpha=1)
ax2.grid(True)
# 绘制MACD图
ax3.plot(data['diff'], color='y', label='diff')
ax3.plot(data['dea'], color='b', label='dea')
ax3.legend(loc=0)
ax3.grid(True)
# 绘制ATR图
ax4.plot(data['date'], data['atr'], color='r', label='atr')
ax4.legend(loc=0)
ax4.set_xticks(range(0, len(data['date']), 5))
ax4.set_xticklabels(data['date'][::5], rotation=45)
ax4.grid(True)
plt.subplots_adjust(hspace=0.09)
# 保存图片
figPath = os.getcwd() + '/{}.png'.format(code)
fig.savefig(figPath, dpi=150, bbox_inches='tight')
self.fig_attachs.append(figPath)
ax0.autoscale_view()
ax0.plot(ma5, color='b', lw=2)
ax0.plot(ma25, color='r', lw=2)
ax0.legend(loc='best')
ax0.set_ylabel(u'株価', fontproperties = fp, fontsize=16)
#ax0.xaxis_date()
#ax0.autoscale_view()
ax1 = plt.subplot(gs[1], sharex=ax0)
#vc = volume_overlay3(ax1, quotes, colorup='k', colordown='r', width=4, alpha=1.0)
#volume_overlay(ax1, opens, closes, volumes, colorup='g', alpha=0.5, width=1)
#ax1.set_xticks(ds)
vc = volume_overlay(ax1, opens, closes, volumes, colorup='g', alpha=0.5, width=1)
ax1.add_collection(vc)
ax1.xaxis.set_major_locator(get_locator())
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(millionformatter)
ax1.yaxis.tick_right()
ax1.set_ylabel(u'出来高', fontproperties = fp, fontsize=16)
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), rotation=30, horizontalalignment='left')
plt.legend(prop = fp);
plt.show()
def gen_candlestick(d_frame,
mode='c',
period_list=[],
title='',
file_name='~/tmp_plot.png',
plot_period=None,
plot_volume=True,
fig_ratio=None,
sr_list=None,
plot_columns=[],
plot_columns_subplot=[]):
# Vars
l_bar = ''.join(['-'] * 60)
def_fig_dim = plt.rcParams['figure.figsize']
def_font_size = plt.rcParams['font.size']
def_bars = 50
def_n_locs = 6
# Check period list
if period_list == None:
period_list = []
# endif
if sr_list == None:
sr_list = []
# endif
# Make a copy
d_frame_c_c = d_frame.copy()
# Slice the frame which needs to be plotted
if plot_period:
d_frame_c = d_frame_c_c[-plot_period:].copy()
fig_r = int(plot_period * 1.0 / def_bars) + 1
else:
d_frame_c = d_frame_c_c.copy()
fig_r = 1.0
# endif
# Check if fig_ratio is passed directly
if fig_ratio:
fig_ratio = float(fig_ratio)
else:
fig_ratio = fig_r
# endif
# Get date list and rmean function
xdate = [datetime.datetime.fromtimestamp(t) for t in d_frame_c['T']]
rmean = utils.g_rmean_f(type='e')
def mydate(x, pos):
try:
return xdate[int(x)]
except IndexError:
return ''
# endtry
# enddef
def r_ns_tr(p_f, indx=-1, rnd=2):
return str(round(p_f.iloc[indx], rnd))
# enddef
# Calculate new figure dimention
new_fig_dim = [fig_ratio * x for x in def_fig_dim]
sub_plot_1 = 211 if len(plot_columns_subplot) > 0 else 111
sub_plot_2 = 212 if len(plot_columns_subplot) > 0 else 111
# Pre-processing
fig = plt.figure(figsize=new_fig_dim)
ax = fig.add_subplot(sub_plot_1)
plt.xticks(rotation=45)
plt.xlabel("Date")
plt.ylabel("Price")
#plt.title(title)
# Title for close
title2 = '{}:{}'.format('C', r_ns_tr(d_frame_c['c']))
# Plot candlestick
candlestick2_ohlc(ax,
d_frame_c['o'],
d_frame_c['h'],
d_frame_c['l'],
d_frame_c['c'],
width=0.6)
## Plot mas
for period_this in period_list:
label = 'ema_' + str(period_this)
d_s = utils.s_mode(d_frame_c, mode)
d_frame_c[label] = rmean(d_s, period_this)
d_frame_c.reset_index(inplace=True, drop=True)
d_frame_c[label].plot(ax=ax)
title2 = title2 + ' {}:{}'.format(label, r_ns_tr(d_frame_c[label]))
# endfor
# Add sr lines if passed
for l_this in sr_list:
d_frame_c[str(l_this)] = [l_this] * len(d_frame_c.index)
d_frame_c[str(l_this)].plot(ax=ax)
# endfor
# Plot specific columns if passed
for column_t in plot_columns:
d_frame_c.reset_index(inplace=True, drop=True)
d_frame_c[column_t].plot(ax=ax)
# endfor
# Plot overlay columns
if len(plot_columns_subplot) > 0:
ax3 = fig.add_subplot(sub_plot_2)
for column_t in plot_columns_subplot:
d_frame_c[column_t].reset_index(inplace=True, drop=True)
d_frame_c[column_t].plot(ax=ax3)
# endfor
# Set axes
ax3.xaxis.set_major_locator(ticker.MaxNLocator(def_n_locs * fig_ratio))
ax3.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
# endif
if plot_volume:
# Plot volume
v_data = [0 if j == 'n/a' else j for j in d_frame_c['v']]
ax2 = ax.twinx()
bc = volume_overlay(ax2,
d_frame_c['o'],
d_frame_c['c'],
v_data,
colorup='g',
alpha=0.2,
width=0.6)
ax2.add_collection(bc)
# endif
# Post-processing
# Set grid
plt.grid()
# Set titles
font_size = int(fig_ratio * def_font_size * 0.7)
plt.title(title + '\n{}\n'.format(l_bar) + '\n'.join(wrap(title2, 60)),
fontsize=font_size)
# Set axes
ax.xaxis.set_major_locator(ticker.MaxNLocator(def_n_locs * fig_ratio))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
fig.autofmt_xdate()
#fig.tight_layout()
# Plot figure
plt.savefig(os.path.expanduser(file_name))
# Clear plot to save memory
plt.close()
t4 = axMiddle.text(0.4, top, s, fontsize=textsize, transform=axMiddle.transAxes)
# now do the moviing average. I'll use a convolution to simulate a
# real moving average
ma5 = movavg(r.adj_close, 5)
ma20 = movavg(r.adj_close, 20)
axMiddle.plot(vind[5 - 1 :], ma5, "b", linewidth=1)
axMiddle.plot(vind[20 - 1 :], ma20, "r", linewidth=1)
axMiddle.set_ylim((300, 800))
axMiddle.set_yticks(np.arange(800, 800, 100))
# Now do the volume overlay
# todo - this is broken
bars = volume_overlay(axMiddleVol, r.open, r.close, r.volume, alpha=0.5)
# axMiddleVol.set_ylim(0, 3*r.volume.max()) # use only a third of the viewlim
if 1: ############### Lower axes #################
# make up two signals; I don't know what the signals are in real life
# so I'll just illustrate the plotting stuff
s1 = random_signal(N, 10)
s2 = random_signal(N, 20)
axLower.plot(vind, s1, color=purple)
axLower.plot(vind, s2, color="k", linewidth=1.0)
s3 = s2 - s1
axLower.plot(vind, s3, color="#cccc99") # wheat
bars = index_bar(axLower, s3, width=2, alpha=0.5, facecolor="#3087c7", edgecolor="#cccc99")
transform=axMiddle.transAxes)
# now do the moviing average. I'll use a convolution to simulate a
# real moving average
ma5 = movavg(r.adj_close, 5)
ma20 = movavg(r.adj_close, 20)
axMiddle.plot(vind[5 - 1:], ma5, 'b', linewidth=1)
axMiddle.plot(vind[20 - 1:], ma20, 'r', linewidth=1)
axMiddle.set_ylim((300, 800))
axMiddle.set_yticks(np.arange(800, 800, 100))
# Now do the volume overlay
# todo - this is broken
bars = volume_overlay(axMiddleVol, r.open, r.close, r.volume, alpha=0.5)
#axMiddleVol.set_ylim(0, 3*r.volume.max()) # use only a third of the viewlim
if 1: ############### Lower axes #################
# make up two signals; I don't know what the signals are in real life
# so I'll just illustrate the plotting stuff
s1 = random_signal(N, 10)
s2 = random_signal(N, 20)
axLower.plot(vind, s1, color=purple)
axLower.plot(vind, s2, color='k', linewidth=1.0)
s3 = s2 - s1
axLower.plot(vind, s3, color='#cccc99') # wheat
bars = index_bar(axLower,
s3,
def plot(self, widget):
finance.volume_overlay(widget, self.open, self.close, self.volume,
self.colorup, self.colordown, self.width)
def plot(self, widget, colorup = 'r', colordown = 'b', width = 1):
"""docstring for plot"""
finance.volume_overlay(widget, self.open, self.close, self.volume, colorup, colordown, width)