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 plotKLine(open, close, high, low, tech):
fig = plt.figure(figsize=(30, 15))
y = len(close)
date = np.linspace(0, y, y)
candleAr = []
ax1 = plt.subplot2grid((10, 4), (0, 0), rowspan=5, colspan=4)
candlestick2_ochl(ax1,
open,
close,
high,
low,
width=1,
colorup='r',
colordown='g',
alpha=0.75)
ax2 = plt.subplot2grid((10, 4), (5, 0), rowspan=4, colspan=4, sharex=ax1)
if 'ATR' in tech.keys():
ax2.plot(date, tech['ATR'], '-b')
if 'ad_ATR' in tech.keys():
ax2.plot(date, tech['ad_ATR'], '-r')
if 'my_ATR' in tech.keys():
ax2.plot(date, tech['my_ATR'], '-m')
if 'short_ATR' in tech.keys():
ax2.plot(date, tech_1['short_ATR'], '-b')
if 'long_ATR' in tech.keys():
ax2.plot(date, tech_1['long_ATR'], '-r')
if 'close' in tech.keys():
ax2.plot(date, tech_2['close'], '-b')
if 'upper' in tech.keys():
ax2.plot(date, tech_2['upper'], '-r')
if 'lower' in tech.keys():
ax2.plot(date, tech_2['lower'], '-r')
def plot_candle(stock_code):
# get stock data
stock_data = pd.read_csv('../data/' + stock_code + '/hist_k_day.csv')
data_o = stock_data['open']
data_c = stock_data['close']
data_h = stock_data['high']
data_l = stock_data['low']
# draw candle stick
fig = plt.figure()
#ax1 = fig.add_subplot(1,1,1)
ax1 = fig.add_axes([0.1, 0.2, 0.85, 0.7])
ax1.set_title(stock_code)
#ax1.set_xticks(stock_data['date'][0:80])
ax1.set_ylabel("Price")
fin.candlestick2_ochl(ax1,
stock_data['open'][0:80],
stock_data['close'][0:80],
stock_data['high'][0:80],
stock_data['low'][0:80],
width=1,
colorup='r',
colordown='g',
alpha=0.8)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
plt.grid(True)
plt.show()
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 draw_klines(df:pd.DataFrame):
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
from matplotlib import ticker
import matplotlib.dates as mdates
columns = ['datetime', 'open', 'close', 'high', 'low', 'volume']
if not set(df.columns).issuperset(columns):
raise Exception(f'请包含{columns}字段')
data = df.loc[:, columns]
data_mat = data.as_matrix().T
xdate = data['datetime'].tolist()
def mydate(x, pos):
try:
return xdate[int(x)]
except IndexError:
return ''
fig, ax1, = plt.subplots(figsize=(1200 / 72, 480 / 72))
plt.title('KLine', fontsize='large',fontweight = 'bold')
mpf.candlestick2_ochl(ax1, data_mat[1], data_mat[2], data_mat[3], data_mat[4], colordown='#53c156', colorup='#ff1717', width=0.3, alpha=1)
ax1.grid(True)
ax1.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
ax1.xaxis.set_major_locator(mdates.HourLocator())
ax1.xaxis.set_major_locator(mdates.MinuteLocator(byminute=[0, 15, 30, 45],
interval=1))
ax1.xaxis.set_major_locator(ticker.MaxNLocator(8))
def show(self, start, end):
"""
show plotted graph (Candlestick + Strategy Plot + P&L) between start and end
:param start: str, 'YYYYMMDD HH:MM:SS'
:param end: str, 'YYYYMMDD HH:MM:SS'
:return: None
"""
start = datetime.strptime(start, '%Y%m%d %H:%M:%S')
end = datetime.strptime(end, '%Y%m%d %H:%M:%S')
data_part = self.__data[(self.__data.index >= start)
& (self.__data.index <= end)]
total_value_part = self.__total_value[
(self.__total_value.index >= start)
& (self.__total_value.index <= end)]
if len(self.__plots) > 0:
plots_part = self.__plots[(self.__plots.index >= start)
& (self.__plots.index <= end)]
else:
plots_part = pd.Series(np.nan, index=data_part.index)
plt.figure(1)
ax1 = plt.subplot(211)
candlestick2_ochl(ax1,
opens=data_part['OPEN'],
closes=data_part['CLOSE'],
highs=data_part['HIGH'],
lows=data_part['LOW'])
plt.plot(plots_part)
plt.subplot(212)
plt.plot(total_value_part)
plt.show()
def animate(i):
global data
global timeframe
# Scraping page.
page = requests.get(
'https://bitcoinwisdom.com/markets/bitstamp/btcusd')
tree = html.fromstring(page.content)
p = float(tree.xpath('//*[@id="price"]/text()')[0])
t = dt.datetime.now()
columns = ['Time', 'Price']
data = pd.concat([data, pd.DataFrame([(t, p)], columns=columns)])
# Resamplig data.
if len(data) > 1:
df = data.set_index('Time')
opensList = list(df.Price.resample(timeframe).first())
closesList = list(df.Price.resample(timeframe).last())
highsList = list(df.Price.resample(timeframe).max())
lowsList = list(df.Price.resample(timeframe).min())
# Plotting.
ax1.clear()
finance.candlestick2_ochl(ax1,
opensList,
closesList,
highsList,
lowsList,
width=0.8,
colorup='k',
colordown='r',
alpha=0.75)
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_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 plot_k_data(self):
_, axes = plt.subplots(1, 1)
fin.candlestick2_ochl(axes, self.open, self.close,
self.high, self.low, width=0.5,
colorup='r', colordown='g')
self.ema.compute_indicator(self.close, n=24)
self.ema.plot_indicator(axes)
plt.show()
def dwAxO(ax, os, hs, ls, cs):
mpf.candlestick2_ochl(ax,
os,
hs,
ls,
cs,
width=0.6,
colorup='w',
colordown='w',
alpha=0.15)
ax.set_xlim(left=0.0)
def test_dchannel(ohlc):
"""DCHANNEL test function."""
dchannel10 = dchannel(ohlc, 10)
data = pd.concat([ohlc, dchannel10], axis=1)
# print(data)
ax1 = plt.subplot2grid((6,4), (0,0), rowspan=6, colspan=6)
ax1.grid(True)
candlestick2_ochl(ax1, ohlc["open"], ohlc["close"], ohlc["high"], ohlc["low"], width=0.7, colorup="r", colordown="g")
ax1.plot(dchannel10)
plt.title("DCHANNEL Chart")
plt.show()
def plt(self, savefig=False):
# 動作が重くならないようにクリアする
plt.clf()
fig = plt.figure(figsize=(10, 7.5))
ax = fig.add_subplot(111)
ax.set_title('code:' + str(self.code), loc='center', fontsize=20)
ax.set_xlabel('day')
ax.set_ylabel('price')
ax.autoscale_view()
ax.patch.set_facecolor('k') # 背景色
ax.patch.set_alpha(0.6) # 透明度
finance.candlestick2_ochl(ax, opens=self.ohcl["open"],\
highs=self.ohcl["high"],\
lows=self.ohcl["low"],\
closes=self.ohcl["close"],\
width=0.5, colorup='r',\
colordown='g', alpha=0.75)
# 一目均衡雲をプロットする
self.__calc_leading_span()
x_data = [x for x in range(26, 26 + len(self._span1))]
plt.plot(x_data, self._span1, color="r", alpha=0.5)
plt.plot(x_data, self._span2, color="b", alpha=0.5)
plt.fill_between(x_data,
self._span1,
self._span2,
where=self._span1 > self._span2,
facecolor='r',
alpha=0.25)
plt.fill_between(x_data,
self._span1,
self._span2,
where=self._span1 < self._span2,
facecolor='b',
alpha=0.25)
self.__plt_bolinger_band(ax)
self.__plt_envelope(ax)
self.__plt_upper_support_line(ax)
self.__plt_GMMA(ax)
self.__plt_volume(ax)
plt.xlim([100, 120])
plt.grid(True, linestyle='--', color='0.75')
# 画像を保存する
if savefig == True:
fig_name = str(self.code) + ".png"
plt.savefig(fig_name)
plt.show()
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 candleStick():
dff = callback()
dff = dff[-int(mEntry2.get()) - 90:-int(mEntry2.get())]
fig, ax = plt.subplots()
mpf.candlestick2_ochl(ax,
opens=dff['Open'],
closes=dff['Close'],
highs=dff['High'],
lows=dff['Low'],
width=2,
colorup='g',
colordown='r',
alpha=0.75)
ax.set_ylabel('Price per Share ($)')
ax.set_xlabel('Days Before Prediction ')
plt.show()
def plot_chart_data(data, currency_pair, file_name=None, ymin=None, ymax=None):
fig1, ax1 = plt.subplots()
candlestick2_ochl(ax1,
opens=[float(d["open"]) for d in data],
closes=[float(d["close"]) for d in data],
highs=[float(d["high"]) for d in data],
lows=[float(d["low"]) for d in data],
width=0.5,
colorup='green')
change = helper.calculate_difference_in_percent(float(data[0]['open']), float(data[-1]['close']))
plt.title("{0} {1:.2f}%".format(currency_pair, change))
if ymin and ymax:
plt.ylim(ymin=ymin, ymax=ymax)
plt.tight_layout()
if file_name:
plt.savefig(file_name)
plt.close()
else:
plt.show()
def plot_chart_data(data, currency_pair, file_name=None, ymin=None, ymax=None):
fig1, ax1 = plt.subplots()
candlestick2_ochl(ax1,
opens=[float(d["open"]) for d in data],
closes=[float(d["close"]) for d in data],
highs=[float(d["high"]) for d in data],
lows=[float(d["low"]) for d in data],
width=0.5,
colorup='green')
change = helper.calculate_difference_in_percent(float(data[0]['open']),
float(data[-1]['close']))
plt.title("{0} {1:.2f}%".format(currency_pair, change))
if ymin and ymax:
plt.ylim(ymin=ymin, ymax=ymax)
plt.tight_layout()
if file_name:
plt.savefig(file_name)
plt.close()
else:
plt.show()
from matplotlib.finance import candlestick2_ochl import atj.candlestick vsamplet = np.asarray([1., 2., 3., 4., 5., 6.]) lookback = 2 vt = np.asarray([1., 2., 3., 4., 5.]) vpx = np.asarray([108., 102., 103., 101., 105.]) vsz = np.asarray([10., 15., 5., 25., 20.]) res = atj.candlestick.candlestick_trades(vsamplet, lookback, vt, vpx, vsz) res = np.reshape(res, (-1, 6)) 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) np.save candlestick2_ochl(ax, res[:, 1], res[:, 2], res[:, 3], res[:, 4], width=0.2, colorup=u'k', colordown=u'r', alpha=1.0) ##plot_day_summary(ax, quotes, ticksize=3) ax.xaxis_date() ax.autoscale_view() plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right') plt.show()
#fp = FontProperties(fname=r'D:\Dev\stockdb\ipagp.ttf')
fp = FontProperties(fname=fontpath)
fig = plt.figure()
fig.subplots_adjust(bottom=0.15)
fig.subplots_adjust(hspace=0)
fig.suptitle(stockname, fontproperties=fp, fontsize=24, fontweight='bold')
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
candles = candlestick2_ochl(ax0,
opens,
closes,
highs,
lows,
width=1,
colorup='blue',
colordown='r')
textsize = 8 # size for axes text
left, height, top = 0.025, 0.06, 0.9
t1 = ax0.text(left,
top,
'%s daily' % symbol,
fontsize=textsize,
transform=ax0.transAxes)
t2 = ax0.text(left,
top - height,
'EMA(5)',
color='b',
def moving_average_convergence(x, nslow=26, nfast=12):
"""
compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
return value is emaslow, emafast, macd which are len(x) arrays
"""
emaslow = moving_average(x, nslow, type='exponential')
emafast = moving_average(x, nfast, type='exponential')
return emaslow, emafast, emafast - emaslow
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
f = web.DataReader("F", 'yahoo', start, end)
f['12_exma'] = pandas.ewma(f['Adj Close'], span=12, min_periods=12)
f['26_exma'] = pandas.ewma(f['Adj Close'], span=26, min_periods=26)
f['MACD'] = f['12_exma'] - f['26_exma']
f['singal'] = pandas.ewma(f['MACD'], span=9, min_periods=9)
f['histogram'] = f['MACD'] - f['singal']
#print f.ix['2010-01-04']
#print f[:100]
print f[:10]
f['t'] = f.index.map(dates.date2num)
fig, ax = plt.subplots()
#candlestick(ax, f[['t', 'Open', 'High', 'Low', 'Close']].values)
candlestick2_ochl(f['t'], f['Open'], f['Close'], f['High'], f['Low'])
ax.xaxis_date()
#f.plot()
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
#Visualize
plt.figure()
closes = data['Close'].values[start:stop]
opens = data['Open'].values[start:stop]
highs = data['High'].values[start:stop]
lows = data['Low'].values[start:stop]
ax = plt.subplot(311)
plt.grid(True)
fnc.candlestick2_ochl(ax,
opens,
closes,
highs,
lows,
width=1,
colorup='g',
colordown='r',
alpha=0.75)
plt.subplot(312)
plt.plot(df['RSI'].values[start:stop])
plt.plot(30 * np.ones(stop - start), 'r--')
plt.plot(70 * np.ones(stop - start), 'r--')
plt.plot(df['RSI_MA1'].values[start:stop])
plt.plot(df['RSI_MA2'].values[start:stop])
plt.grid(True)
plt.subplot(313)
plt.plot(df['MACD'].values[start:stop])
highs = np.array(highs) - vert_shift
lows = np.array(lows) - vert_shift
from matplotlib.finance import candlestick2_ochl
tag_to_dist = {"T" : 19.95045714285714,"M" : 13.535514285714285,"B" : -22.043857142857142,"OH1" : 15.330485714285714,"OH2" : 1.617575,"OH3" : -2.5823,"OH4" : -8.916849999999998,"HP1" : 17.293228571428568,"HP2" : 3.939978571428571,"HP3" : -4.974257142857143,"HP4" : -12.003725,"CC" : -1.9202571428571429}
## Code for the absolute
if(True):
candlestick2_ochl(ax1,opens,closes,highs,lows,width= 1, colorup='#77d879', colordown='#db3f3f')
plt.xlabel('Z-coordinate [Angstrom]', fontsize = 24, color = fc)
plt.xticks([i for i in range(len(lines[0]))][::10],lines[0][::10], fontsize = 22)
plt.yticks(fontsize=22)
plt.ylabel('Smoothed Channel Radius [Angstrom]',fontsize = 24, color = fc)
titletag = tag.replace("min_","Minimal").replace("6AA_","Residues within 6 $\AA$").replace("4AA_","Residues within 4 $\AA$").replace("5AA_","Residues within 5 $\AA$").replace("fc"," Forward (Correction)").replace("bc", " Backward (Correction)").replace("f"," Forward").replace("b"," Backward").replace("12"," Sites AB and DE").replace("1"," Site AB").replace("2"," Site DE")
#plt.title(titletag, fontsize = 24, color = fc)
plt.subplots_adjust(left=0.09, bottom=0.20, right=0.94, top=0.90, wspace=0.2, hspace=0)
if(tag=="min_f12"):
for i,j in zip(tag_to_dist.keys(),tag_to_dist.values()):
plt.plot([j+40, j+40], [-29.5, 7.3], 'k--', lw=1, color =fc)
shif = 0.0
if(i=="CC"): shif = 0.4
begin_time = '2017-05-17'
end_time = '2018-05-17'
code = '002129'
df = ts.get_hist_data(code, start=begin_time, end=end_time)
df = df.sort_index(0)
df_idx = df.index.values
fig, ax = plt.subplots(figsize=(20, 10))
# matplotlib.finance.candlestick2_ochl(ax, opens, closes, highs, lows, width=4, colorup='r', colordown='g', alpha=0.75)
candlestick2_ochl(ax=ax,
opens=df["open"].values,
closes=df["close"].values,
highs=df["high"].values,
lows=df["low"].values,
width=0.75,
colorup='r',
colordown='g',
alpha=0.75)
ax.xaxis.set_major_locator(ticker.MaxNLocator(20))
#设置自动格式化时间
def mydate_formatter(x, pos):
try:
return df_idx[int(x)]
except IndexError:
return ''
# sma_5 = pd.ewma(np.array(data['close']), 5)
# sma_10 = pd.ewma(np.array(data['close']), 10)
# sma_20 = pd.ewma(np.array(data['close']), 20)
# sma_30 = pd.ewma(np.array(data['close']), 30)
# sma_60 = pd.ewma(np.array(data['close']), 60)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_xticks(range(0, len(data['date']), 10))
ax.set_xticklabels(data['date'][::10], rotation=45)
ax.plot(sma_5, label='MA5')
ax.plot(sma_10, label='MA10')
ax.plot(sma_20, label='MA20')
ax.plot(sma_30, label='MA30')
ax.plot(sma_60, label='MA60')
ax.legend(loc='upper left')
mpf.candlestick2_ochl(ax,
data['open'],
data['close'],
data['high'],
data['low'],
width=0.5,
colorup='r',
colordown='green',
alpha=0.6)
plt.grid()
plt.show()
from matplotlib.finance import candlestick2_ochl
import matplotlib.dates as mdates
##style.use('ggplot')
##Reading csv file and storing as dataframe in df
df = pd.read_csv('TCS.csv')
df_ohlc = df
##Mapping dates of csv file to matplotlib dates format
df_ohlc['Date'] = df_ohlc['Date'].map(mdates.datestr2num)
##df_volume['Date']=df_volume['Date'].map(mdates.datestr2num)
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=5, colspan=1)
##ax2=plt.subplot2grid((6,1),(5,0),rowspan=1,colspan=1,sharex=ax1)
ax1.set_title('TCS')
candlestick2_ochl(ax1,
df_ohlc['Open'],
df_ohlc['Close'],
df_ohlc['High'],
df_ohlc['Low'],
width=2,
colorup='g')
##ax2.fill_between(df_volume['Date'],df_volume['Total Traded Quantity'].mean(),0)
##ax1.scatter(df_ohlc['Date'],df_ohlc['Total Traded Quantity'])
plt.show()
fig.set_size_inches(18.5, 10.5)
ax1 = plt.subplot2grid((1, 1), (0, 0))
ax1.set_facecolor(bc)
ax1.spines['bottom'].set_color(fc)
ax1.spines['top'].set_color(fc)
ax1.spines['right'].set_color(fc)
ax1.spines['left'].set_color(fc)
ax1.tick_params(axis='x', colors=fc)
ax1.tick_params(axis='y', colors=fc)
candlestick2_ochl(ax1,
opens,
closes,
highs,
lows,
width=1,
colorup='#77d879',
colordown='#db3f3f')
plt.xlabel('Z-coordinate [Angstrom]', fontsize=24, color=fc)
plt.xticks([i for i in range(len(lines[0]))][::5],
lines[0][::5],
fontsize=18)
plt.yticks(fontsize=18)
plt.ylabel('Smoothed Channel Radius [Angstrom]', fontsize=24, color=fc)
titletag = tag.replace("min_", "Minimal").replace(
"6AA_", "Residues within 6 $\AA$").replace(
"4AA_", "Residues within 4 $\AA$").replace(
"5AA_", "Residues within 5 $\AA$").replace(
"fc", " Forward (Correction)").replace(
def get_relation(self, k_data):
## 判断包含关系
after_fenxing = pd.DataFrame()
temp_data = k_data[:1]
zoushi = [3] # 3-持平 4-向下 5-向上
print(temp_data.high)
for i in range(len(k_data)):
case1_1 = temp_data.high.values[-1] > k_data.high.values[
i] and temp_data.low.values[-1] < k_data.low.values[
i] # 第1根包含第2根
case1_2 = temp_data.high.values[-1] > k_data.high.values[
i] and temp_data.low.values[-1] == k_data.low.values[
i] # 第1根包含第2根
case1_3 = temp_data.high.values[-1] == k_data.high.values[
i] and temp_data.low.values[-1] < k_data.low.values[
i] # 第1根包含第2根
case2_1 = temp_data.high.values[-1] < k_data.high.values[
i] and temp_data.low.values[-1] > k_data.low.values[
i] # 第2根包含第1根
case2_2 = temp_data.high.values[-1] < k_data.high.values[
i] and temp_data.low.values[-1] == k_data.low.values[
i] # 第2根包含第1根
case2_3 = temp_data.high.values[-1] == k_data.high.values[
i] and temp_data.low.values[-1] > k_data.low.values[
i] # 第2根包含第1根
case3 = temp_data.high.values[-1] == k_data.high.values[
i] and temp_data.low.values[-1] == k_data.low.values[
i] # 第1根等于第2根
case4 = temp_data.high.values[-1] > k_data.high.values[
i] and temp_data.low.values[-1] > k_data.low.values[i] # 向下趋势
case5 = temp_data.high.values[-1] < k_data.high.values[
i] and temp_data.low.values[-1] < k_data.low.values[i] # 向上趋势
if case1_1 or case1_2 or case1_3:
if zoushi[-1] == 4:
temp_data.high.values[-1] = k_data.high.values[i]
else:
temp_data.low.values[-1] = k_data.low.values[i]
elif case2_1 or case2_2 or case2_3:
temp_temp = temp_data[-1:]
temp_data = k_data[i:i + 1]
if zoushi[-1] == 4:
temp_data.high.values[-1] = temp_temp.high.values[0]
else:
temp_data.low.values[-1] = temp_temp.low.values[0]
elif case3:
zoushi.append(3)
pass
elif case4:
zoushi.append(4)
after_fenxing = pd.concat([after_fenxing, temp_data], axis=0)
temp_data = k_data[i:i + 1]
elif case5:
zoushi.append(5)
after_fenxing = pd.concat([after_fenxing, temp_data], axis=0)
temp_data = k_data[i:i + 1]
# after_fenxing.head()
## 因为使用candlestick2函数，要求输入open、close、high、low。为了美观，处理k线的最大最小值、开盘收盘价，之后k线不显示影线。
for i in range(len(after_fenxing)):
if after_fenxing.open.values[i] > after_fenxing.close.values[i]:
after_fenxing.open.values[i] = after_fenxing.high.values[i]
after_fenxing.close.values[i] = after_fenxing.low.values[i]
else:
after_fenxing.open.values[i] = after_fenxing.low.values[i]
after_fenxing.close.values[i] = after_fenxing.high.values[i]
## 画出k线图
stock_middle_num = self.middle_num(after_fenxing)
fig, ax = plt.subplots(figsize=(50, 20))
fig.subplots_adjust(bottom=0.2)
mpf.candlestick2_ochl(ax,
list(after_fenxing.open),
list(after_fenxing.close),
list(after_fenxing.high),
list(after_fenxing.low),
width=0.6,
colorup='r',
colordown='b',
alpha=0.75)
plt.grid(True)
dates = after_fenxing.index
ax.set_xticklabels(dates) # Label x-axis with dates
# ax.autoscale_view()
ax.plot(stock_middle_num, 'k', lw=1)
ax.plot(stock_middle_num, 'ko')
plt.setp(plt.gca().get_xticklabels(), rotation=30)
plt.title(self.coin_type + "_" + self.time_type +
time.strftime('%Y-%m-%d-%H-%M-%S') + "_ftb.png")
self.K_Line_fig = fig
return after_fenxing
def dwAxStd(ax,os,hs,ls,cs):
mpf.candlestick2_ochl(ax, os,hs,ls,cs, width=0.6, colorup='r', colordown = 'g', alpha=0.75)
ax1.set_xlim(left = 0.0)
k, d = STOCH(f.High.values,
f.Low.values,
f.Close.values,
fastk_period=8,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
fig = plt.figure(figsize=(11, 8))
ax1 = fig.add_subplot(2, 1, 1)
candlestick2_ochl(ax1,
opens,
closes,
highs,
lows,
width=0.5,
colorup='r',
colordown='g')
ax1.set_xticks(np.linspace(0, l - 1, 6).astype('int'))
ax1.set_xticklabels(f.index[np.linspace(0, l - 1, 6).astype('int')])
ax1.grid(color='r', linestyle='--', linewidth=1)
ax2 = fig.add_subplot(2, 1, 2)
ax2.plot(k)
ax2.plot(d)
ax2.set_xticks(np.linspace(0, l - 1, 6).astype('int'))
ax2.set_yticks([15, 20, 30, 40, 50, 60, 70, 80, 85])
ax2.set_xticklabels(f.index[np.linspace(0, l - 1, 6).astype('int')])
ax2.grid(color='b', linestyle='--', linewidth=1)
plt.show()
#ignore the clear signal
if df1['cumsum'][n] > 0:
df1['signals'][n] = -1 * (df1['cumsum'][n - 1])
elif df1['cumsum'][n] < 0:
df1['signals'][n] = 0
#plotting the backtesting result
#first plot is Heikin-Ashi candlestick
#use candlestick function and set Heikin-Ashi O,C,H,L
#the second plot is the actual price with long/short positions as up/down arrows
ax1 = plt.subplot2grid((200, 1), (0, 0), rowspan=120, ylabel='HA price')
mpf.candlestick2_ochl(ax1,
df1['HA open'],
df1['HA close'],
df1['HA high'],
df1['HA low'],
width=1,
colorup='g',
colordown='r')
plt.grid(True)
plt.xticks([])
plt.title('Heikin-Ashi')
ax2 = plt.subplot2grid((200, 1), (120, 0),
rowspan=80,
ylabel='price',
xlabel='')
df1['Close'].plot(ax=ax2, label=ticker, c='k')
#long/short positions are attached to the real close price of the stock
#set the line width to zero
#thats why we only observe markers
millionformatter = FuncFormatter(millions)
thousandformatter = FuncFormatter(thousands)
#fig = plt.figure(figsize=(8, 6))
#fp = FontProperties(fname=r'D:\Dev\stockdb\ipagp.ttf')
fp = FontProperties(fname=fontpath)
fig = plt.figure()
fig.subplots_adjust(bottom=0.15)
fig.subplots_adjust(hspace=0)
fig.suptitle(stockname, fontproperties=fp, fontsize=24, fontweight='bold')
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
ax0 = plt.subplot(gs[0])
candles = candlestick2_ochl(ax0, opens, closes, highs, lows, width=1, colorup='blue',colordown='r')
textsize = 8 # size for axes text
left, height, top = 0.025, 0.06, 0.9
t1 = ax0.text(left, top, '%s daily'%symbol, fontsize=textsize,
transform=ax0.transAxes)
t2 = ax0.text(left, top-height, 'EMA(5)', color='b', fontsize=textsize, transform=ax0.transAxes)
t3 = ax0.text(left, top-2*height, 'EMA(25)', color='r', fontsize=textsize, transform=ax0.transAxes)
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' %(
time.strftime('%d-%b-%Y'),
opens[-1], highs[-1],
lows[-1], closes[-1],
volumes[-1]*1e-6,
closes[-1]-opens[-1])
t4 = ax0.text(0.4, top, s, fontsize=textsize,
def draw_k_line(self, path, title):
X = self.X
data = {}
data["open"] = np.array(X[:, 1], dtype=np.float)[-500:]
data["close"] = np.array(X[:, 4], dtype=np.float)[-500:]
data["high"] = np.array(X[:, 2], dtype=np.float)[-500:]
data["low"] = np.array(X[:, 3], dtype=np.float)[-500:]
self.average_axis_buy["Y"] = data["close"][self.average_axis_buy["X"]]
self.average_axis_sell["Y"] = data["close"][
self.average_axis_sell["X"]]
fig, ax = plt.subplots(figsize=(50, 20))
fig.subplots_adjust(bottom=0.2)
plt.grid(True)
mpf.candlestick2_ochl(ax,
data["open"],
data["close"],
data["high"],
data["low"],
width=0.6,
colorup='r',
colordown='green',
alpha=0.75)
hhv_20 = HHV(self.ma3, 50)
hhv_20 = hhv_20.series[-500:]
ax.plot(self.ma90.series[-500:])
ax.plot(self.ma3.series[-500:], color='#054E9F')
ax.plot(hhv_20, color='#436EEE')
axis_map, judge_trend_map = self.get_top_liner_point()
ax.scatter(axis_map["X"], axis_map["Y"], s=75)
ax.scatter(judge_trend_map["X"],
judge_trend_map["Y"],
s=75,
c='#FF00FF',
marker='x')
ax.scatter(self.average_axis_buy["X"],
self.average_axis_buy["Y"],
s=75,
c='#FF00FF',
marker='D')
ax.scatter(self.average_axis_sell["X"],
self.average_axis_sell["Y"] * 1.02,
s=300,
c='green',
marker='v')
if len(self.zhibiao_axis_map) > 0:
ax.scatter(self.zhibiao_axis_map["X"],
self.zhibiao_axis_map["Y"] * 0.98,
s=300,
c='r',
marker="^")
if len(self.additional_map_list) > 0:
for additional_map in self.additional_map_list:
for i, txt in enumerate(additional_map["Y"]):
ax.annotate(
CommonTools.as_num(additional_map["Z"][i], 2),
(additional_map["X"][i], additional_map["Y"][i]))
plt.setp(plt.gca().get_xticklabels(), rotation=30)
plt.title(title)
fig.savefig(path)
self.X = None
df5 = web.DataReader('VZ', "yahoo", start,
end) #VERIZON stock values, from yahoo, between times
print(df5.head())
df5.to_csv('stocks/VZ.csv')
df6 = web.DataReader('GOOG', "yahoo", start,
end) #GOOGLE stock values, from yahoo, between times
print(df6.head())
df6.to_csv('stocks/GOOG.csv')
dfGold = web.DataReader('GLD', "yahoo", start,
end) #google stock values, from yahoo, between times
print(dfGold.head())
dfGold.to_csv('stocks/GOLD.csv')
df = pd.read_csv('GOOG.csv', parse_dates=True, index_col=0)
fig, ax = plt.subplots()
mpf.candlestick2_ochl(ax,
opens=df['Open'],
closes=df['Close'],
highs=df['High'],
lows=df['Low'],
width=2,
colorup='g',
colordown='r',
alpha=0.75)
df['Adj Close'].plot()
plt.show()
# 1.3 K线图绘制
__colorup__ = 'red'
__colordown__ = 'green'
tsla_part_df = tsla_df[-30:]
fig, ax = plt.subplots()
# 用来存放等待绘制的数据
qutotes = []
# for index,(d,o,c,h,l) in enumerate(zip(tsla_part_df.index,tsla_part_df.open,tsla_part_df.close,tsla_part_df.high,tsla_part_df.low)):
# # 把日期数据从日期格式转换为数字格式
# d=mpf.date2num(d)
# # 存储数据
# val=(d,o,c,h,l)
# qutotes.append(val)
#利用mpf进行绘图
mpf.candlestick2_ochl(ax,
tsla_part_df.open,
tsla_part_df.close,
tsla_part_df.high,
tsla_part_df.low,
width=0.6,
colorup=__colorup__,
colordown=__colordown__)
ax.autoscale_view()
# 使用这个方法将x轴的数据改成时间已经不行了
# ax.xaxis_date(tsla_part_df.index.values.tolist())
# 需要这样来进行处理
ax.set_xticks(range(0, len(tsla_part_df['date']), 5))
ax.set_xticklabels(tsla_part_df['date'][::5])
plt.show()
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)
def gen_plot(dataframe,startidx,endidx):
#dataframe is a pandas dataframe with open, high, low, close and volume for each time interval
open=dataframe['Open'][startidx:endidx].tolist()
high=dataframe['High'][startidx:endidx].tolist()
low=dataframe['Low'][startidx:endidx].tolist()
close=dataframe['Close'][startidx:endidx].tolist()
volume=dataframe['Volume'][startidx:endidx].tolist()
date=dataframe['Date'][startidx:endidx].tolist()
num_ticks=6
def mydate(x,pos):
try:
return date[int(x)]
except IndexError:
return ''
#####
# plot just price
#####
'''
fig = plt.figure()
ax = plt.subplot()
candlestick2_ochl(ax,open,low,high,close,width=0.5,colorup='b',colordown='r',alpha=0.75)
ax.xaxis.set_major_locator(ticker.MaxNLocator(num_ticks))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
fig.autofmt_xdate()
fig.tight_layout()
ax.set_ylabel('Price')
ax.set_xlabel('Date')
ax.set_xlim(-1.0,len(date)-1.0)
ax.xaxis.set_major_locator(ticker.MaxNLocator(num_ticks))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
ax.grid()
plt.show()
'''
#####
# plot price with volume by price
#####
fig = plt.figure()
ax4 = plt.subplot()
candlestick2_ochl(ax4,open,low,high,close,width=0.5,colorup='b',colordown='r',alpha=0.75)
ax4.xaxis.set_major_locator(ticker.MaxNLocator(num_ticks))
ax4.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
fig.autofmt_xdate()
fig.tight_layout()
ax4.set_ylabel('Price')
ax4.set_xlabel('Date')
ax4.set_xlim(-1.0,len(date)-1.0)
ax4.grid()
# plt.show()
#plot horizontal bar chart with volume
#generate volume by price, using value at close
min_price=min(close)
max_price=max(close)
###############
###############
#set the nbins for qty of volume bars
nbins=75
barheight=5
num_ticks_vol=5
###############
###############
dy=(max_price-min_price)/nbins
ys = np.arange(min_price,max_price+dy,dy).round(decimals=2)
pairs=[]
bins=[0 for x in range(len(ys))]
for i in range(len(close)):
for z in range(len(ys)-1):
if ys[z] <= close[i] < ys[z+1]:
bins[z]=bins[z]+volume[i]
def setbins(x,pos):
try:
return ys[int(x)]
except IndexError:
return ''
ax3 = ax4.twinx()
ax3 = ax4.twiny()
# ax3.yaxis.set_major_formatter(ticker.FuncFormatter(setbins))
ax3.barh(ys,bins,align='center',height=barheight,color='gray',alpha=0.5)
# ax3.set_yticks(ys)
# ax3.yaxis.set_major_locator(ticker.MaxNLocator(num_ticks_vol))
ax3.yaxis.set_visible(False)
ax3.xaxis.set_visible(False)
# ax3.set_yticklabels(ys)
# ax3.grid()
plt.show()
#####
# plot price with volume in second view below
#####
'''
