def weekday_candlestick(ohlc_data, ax, fmt='%b %d', freq=7, **kwargs):
""" Wrapper function for matplotlib.finance.candlestick_ohlc
that artificially spaces data to avoid gaps from weekends
去除非交易时间的空隙
fmt: 日期格式
freq: 日期显示的间隔
"""
freq = int(freq)
# Convert data to numpy array
ohlc_data_arr = np.array(ohlc_data)
ohlc_data_arr2 = np.hstack(
[np.arange(ohlc_data_arr[:,0].size)[:,np.newaxis], ohlc_data_arr[:,1:]])
ndays = ohlc_data_arr2[:,0] # array([0, 1, 2, ... n-2, n-1, n])
# Convert matplotlib date numbers to strings based on `fmt`
dates = mdates.num2date(ohlc_data_arr[:,0])
date_strings = []
for date in dates:
date_strings.append(date.strftime(fmt))
# Plot candlestick chart
mpf.candlestick_ohlc(ax, ohlc_data_arr2, **kwargs)
# Format x axis
ax.set_xticks(ndays[::freq])
ax.set_xticklabels(date_strings[::freq], rotation=45, ha='right')
ax.set_xlim(ndays.min(), ndays.max())
def main():
days = readstkData(daylinefilespath, stock_b_code, startdate, enddate)
# 日期从dateframe指数下降
daysreshape = days.reset_index()
# 转换浮点数
daysreshape['DateTime'] = mdates.date2num(daysreshape['DateTime'].astype(
dt.date))
# 整理数据，为了展示K线的阴线和阳线使用matplotlib的蜡烛图
daysreshape.drop('Volume', axis=1, inplace=True)
daysreshape = daysreshape.reindex(
columns=['DateTime', 'Open', 'High', 'Low', 'Close'])
Av1 = movingaverage(daysreshape.Close.values, MA1)
Av2 = movingaverage(daysreshape.Close.values, MA2)
SP = len(daysreshape.DateTime.values[MA2 - 1:])
fig = plt.figure(facecolor='#07000d', figsize=(15, 10))
ax1 = plt.subplot2grid((6, 4), (1, 0),
rowspan=4,
colspan=4,
axisbg='#07000d')
candlestick_ohlc(ax1,
daysreshape.values[-SP:],
width=.6,
colorup='#ff1717',
colordown='#53c156')
Label1 = str(MA1) + ' SMA'
Label2 = str(MA2) + ' SMA'
ax1.plot(daysreshape.DateTime.values[-SP:],
Av1[-SP:],
'#e1edf9',
label=Label1,
linewidth=1.5)
ax1.plot(daysreshape.DateTime.values[-SP:],
Av2[-SP:],
'#4ee6fd',
label=Label2,
linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
plt.show()
def prepare(self, chart_data):
# 캔버스를 초기화하고 4개의 차트를 그릴 준비
self.fig, self.axes = plt.subplots(nrows=4,
ncols=1,
facecolor='w',
sharex=True)
for ax in self.axes:
# 보기 어려운 과학적 표기 비활성화
ax.get_xaxis().get_major_formatter().set_scientific(False)
ax.get_yaxis().get_major_formatter().set_scientific(False)
# 차트 1. 일봉 차트
self.axes[0].set_ylabel('Env.') # y 축 레이블 표시
#거래량 가시화
x = np.arange(len(chart_data))
volume = np.array(chart_data)[:, -1].tolist()
self.axes[0].bar(x, volume, color='b', alpha=0.3)
#OHLC = open high low close
ax = self.axes[0].twinx()
ohlc = np.hstack((x.reshape(-1, 1), np.array(chart_data)[:, 1:-1]))
candlestick_ohlc(ax, ohlc, colorup='r', colordown='b')
rate_increase_in_vol.append(df.iloc[i]['Volume']-df.iloc[i-1]['Volume'])
rate_increase_in_close.append(df.iloc[i]['Close']-df.iloc[i-1]['Close'])
i+=1
df['Increase_in_vol']=rate_increase_in_vol
df['Increase_in_close']=rate_increase_in_close
df['Increase_in_vol'].plot()
df['Increase_in_close'].plot()
df_ohlc= df['Close'].resample('10D').ohlc()
df_volume=df['Volume'].resample('10D').sum()
df_ohlc.reset_index(inplace=True)
df_ohlc['Date']=df_ohlc['Date'].map(mdates.date2num)
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.xaxis_date()
candlestick_ohlc(ax1,df_ohlc.values, width=2, colorup='g')
ax2.fill_between(df_volume.index.map(mdates.date2num),df_volume.values,0)
#
#
df3=df
df3.fillna(0, inplace=True)
y_df=df3[['Close','Volume']]
col_y=y_df.columns
y_df=df3[['Close','Volume']]
y_df_mod=y_df.drop(['Close','Volume'],axis=1)
y_df_mod.column
Drop_cols=col_y
Drop_cols=Drop_cols.tolist()
Drop_cols.append('Date')
X_df=df3.drop(Drop_cols,axis=1)
X_df.columns
ema60 = df.close.ewm(span=60).mean() # ① 종가의 12주 지수 이동평균
ema130 = df.close.ewm(span=130).mean() # ② 종가의 12주 지수 이동평균
macd = ema60 - ema130 # ③ MACD선
signal = macd.ewm(span=45).mean() # ④ 신호선(MACD의 9주 지수 이동평균)
macdhist = macd - signal # ⑤ MACD 히스토그램
df = df.assign(ema130=ema130, ema60=ema60, macd=macd, signal=signal,
macdhist=macdhist).dropna()
df['number'] = df.index.map(mdates.date2num) # ⑥
ohlc = df[['number','open','high','low','close']]
plt.figure(figsize=(9, 7))
p1 = plt.subplot(2, 1, 1)
plt.title('Triple Screen Trading - First Screen (아이에스동서)')
plt.grid(True)
candlestick_ohlc(p1, ohlc.values, width=.6, colorup='red',
colordown='blue') # ⑦
p1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
plt.plot(df.number, df['ema130'], color='c', label='EMA130')
plt.legend(loc='best')
p2 = plt.subplot(2, 1, 2)
plt.grid(True)
p2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
plt.bar(df.number, df['macdhist'], color='m', label='MACD-Hist')
plt.plot(df.number, df['macd'], color='b', label='MACD')
plt.plot(df.number, df['signal'], 'g--', label='MACD-Signal')
plt.legend(loc='best')
plt.show()
quotes = [(1312323, 18.00, 190, 100, 99, 5454545)]
# print(np.random.randint((10, 50, 2)))
dates = np.array([])
volumns = np.array([])
for item in quotes:
dates = np.append(dates, item[0])
volumns = np.append(volumns, item[5])
left, width = 0.1, 0.8
rect_vol = [left, 0.1, width, 0.26]
rect_main = [left, 0.4, width, 0.5]
fig = plt.figure()
ax_vol = fig.add_axes(rect_vol)
ax_vol.fill_between(dates, volumns, color='y')
ax_vol.xaxis_date()
plt.setp(ax_vol.get_xticklabels(), rotation=30, horizontalalignment='right')
ax_main = fig.add_axes(rect_main)
ax_main.axes.get_xaxis().set_visible(False)
# 绘制股票K线图
candlestick_ohlc(ax_main, quotes, width=0.6, colorup='r', colordown='g')
ax_main.set_title('Stock INTC Price and Volumn')
plt.show()
def main():
days = get_data('000001.SZ', start_date, '2020-06-30')
data = days.reset_index()
data['trade_date'] = mdates.date2num(data['trade_date'])
data.drop(['ts_code', 'change', 'pct_chg', 'vol', 'amount', 'pre_close'],
axis=1,
inplace=True)
data = data.reindex(columns=['trade_date', 'open', 'high', 'low', 'close'])
'''
data.drop(['ts_code', 'change', 'pct_chg', 'amount', 'pre_close'], axis=1, inplace=True)
# data = data.reindex(columns=['Date', 'Open', 'High', 'Low', 'Close','Volume'])
show_func(data.head())
'''
Av1 = talib.MA(data.close.values, MA1)
Av2 = talib.MA(data.close.values, MA2)
# Av1 = movingaverage(data.close.values, MA1)
# Av2 = movingaverage(data.close.values, MA2)
SP = len(data.trade_date.values[MA2 - 1:])
fig = plt.figure(facecolor='#07000d', figsize=(15, 10))
ax1 = plt.subplot2grid((6, 4), (1, 0),
rowspan=4,
colspan=4,
facecolor='#07000d')
mpf.candlestick_ohlc(ax1,
data.values[-SP:],
width=.6,
colorup='#ff1717',
colordown='#53c156')
# mpf.plot(data)
Label1 = str(MA1) + ' SMA'
Label2 = str(MA2) + ' SMA'
ax1.plot(data.trade_date.values[-SP:],
Av1[-SP:],
'#e1edf9',
label=Label1,
linewidth=1.5)
ax1.plot(data.trade_date.values[-SP:],
Av2[-SP:],
'#4ee6fd',
label=Label2,
linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
# 绘制成交量图
volumeMin = 0
# 先通过twinx来表达和ax1公用一个x轴
ax1v = ax1.twinx()
# 通过fill——between这个函数来表达准备把这个数据全部弄成某个颜色
ax1v.fill_between(data.trade_date.values[-SP:],
volumeMin,
days.vol.values[-SP:],
facecolor='#00ffe8',
alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
###Edit this to 3, so it's a bit larger
ax1v.set_ylim(0, 3 * days.vol.values.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
# 我们画一下RSI图
# plot an RSI indicator on top
maLeg = plt.legend(loc=9,
ncol=2,
prop={'size': 7},
fancybox=True,
borderaxespad=0.)
maLeg.get_frame().set_alpha(0.4)
textEd = pylab.gca().get_legend().get_texts()
pylab.setp(textEd[0:5], color='w')
ax0 = plt.subplot2grid((6, 4), (0, 0),
sharex=ax1,
rowspan=1,
colspan=4,
facecolor='#07000d')
# rsi = rsiFunc(data.Close.values)
rsi = talib.RSI(data.close.values)
rsiCol = '#c1f9f7'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(data.trade_date.values[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
ax0.axhline(70, color=negCol)
ax0.axhline(30, color=posCol)
ax0.fill_between(data.trade_date.values[-SP:],
rsi[-SP:],
70,
where=(rsi[-SP:] >= 70),
facecolor=negCol,
edgecolor=negCol,
alpha=0.5)
ax0.fill_between(data.trade_date.values[-SP:],
rsi[-SP:],
30,
where=(rsi[-SP:] <= 30),
facecolor=posCol,
edgecolor=posCol,
alpha=0.5)
ax0.set_yticks([30, 70])
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#5998ff")
ax0.spines['top'].set_color("#5998ff")
ax0.spines['left'].set_color("#5998ff")
ax0.spines['right'].set_color("#5998ff")
ax0.tick_params(axis='y', colors='w')
ax0.tick_params(axis='x', colors='w')
plt.ylabel('RSI')
# MACD图
# plot an MACD indicator on bottom
ax2 = plt.subplot2grid((6, 4), (5, 0),
sharex=ax1,
rowspan=1,
colspan=4,
facecolor='#07000d')
fillcolor = '#00ffe8'
nslow = 26
nfast = 12
nema = 9
emaslow, emafast, macd = talib.MACD(data.close.values,
fastperiod=nfast,
slowperiod=nslow,
signalperiod=nema)
ax2.plot(data.trade_date.values[-SP:],
emaslow[-SP:],
color='#4ee6fd',
lw=2)
ax2.plot(data.trade_date.values[-SP:],
emafast[-SP:],
color='#e1edf9',
lw=1)
# bar = np.where(macd,2*macd,0)
'''
bar_red = np.where(macd > 0, 2 * macd, 0) # 绘制BAR>0 柱状图
bar_green = np.where(macd < 0, 2 * macd, 0) # 绘制BAR<0 柱状图
show_func(bar_red)
ax2.bar(data.trade_date.values[-SP:], bar_red[-SP:], facecolor='red')
ax2.bar(data.trade_date.values[-SP:], bar_green[-SP:], facecolor='green')
'''
# ax2.bar(data.trade_date.values[-SP:], macd[-SP:],facecolor=fillcolor)
ax2.fill_between(data.trade_date.values[-SP:],
macd[-SP:] * 2,
0,
alpha=0.5,
facecolor=fillcolor,
edgecolor=fillcolor)
# ax2.fill_between(data.trade_date.values[-SP:], macd[-SP:] - ema9[-SP:], 0, alpha=0.5, facecolor=fillcolor,edgecolor=fillcolor)
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax2.spines['bottom'].set_color("#5998ff")
ax2.spines['top'].set_color("#5998ff")
ax2.spines['left'].set_color("#5998ff")
ax2.spines['right'].set_color("#5998ff")
ax2.tick_params(axis='x', colors='w')
ax2.tick_params(axis='y', colors='w')
plt.ylabel('MACD', color='w')
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
# 展示图
plt.ylabel('MACD')
plt.show()
def plot_dataframe(df, ticker, name=None, plot=True, starting_date=None):
from mplfinance import candlestick_ohlc
df = dataframe_utilities.add_indicators(df)
if starting_date is not None:
df = df.truncate(before=starting_date)
# ohlc: open high, low close
# df_ohlc = df['5. adjusted close'].resample('10D').ohlc()
# df_ohlc = df['4. close'].resample('10D').ohlc()
# df_volume = df['6. volume'].resample('10D').sum()
df_ohlc = df.resample("D").agg({
'1. open': 'first',
'2. high': 'max',
'2. low': 'min',
'4. close': 'last'
})
# df_ohlc = df_resampled.ohlc()
df_volume = df['6. volume'].resample("D").sum()
df_ohlc.reset_index(inplace=True)
df_ohlc.dropna(inplace=True)
df_volume.dropna(inplace=True)
df_ohlc['date'] = df_ohlc['date'].map(mdates.date2num)
df_ohlc.head()
fig, (ax1, ax2, ax3,
ax4) = plt.subplots(nrows=4,
ncols=1,
figsize=(19.2, 10.8),
dpi=96,
sharex=True,
gridspec_kw={'height_ratios': [2, 1, 1, 0.5]})
ax1.xaxis_date()
candlestick_ohlc(ax1,
df_ohlc.values,
width=1,
colorup='#77d879',
colordown='#db3f3f')
ax1.set_xlabel('Time')
ax1.set_ylabel('Price per Share (USD)')
titleAX1 = 'Historic Share Price of ' + ticker
ax1.set_title(titleAX1,
horizontalalignment='center',
verticalalignment='top')
ax1.plot(df.index,
df['volatility_bbh'],
color="yellow",
alpha=0.5,
linewidth=1,
label="Bollinger High")
ax1.plot(df.index,
df['volatility_bbl'],
color="red",
alpha=0.5,
linewidth=1,
label="Bollinger Low")
ax1.fill_between(df.index,
y1=df['volatility_bbh'],
y2=df['volatility_bbl'],
color='orange',
alpha='0.3')
ax1.legend(loc="lower left")
ax2.plot(df.index,
df['trend_macd'],
color='purple',
linewidth=0.5,
label="MACD")
ax2.plot(df.index,
df['trend_macd_signal'],
color='orange',
alpha=0.5,
linewidth=1,
markersize=1,
label="MACD Signal")
ax2.set_xlabel('Time')
ax2.set_ylabel('Value')
ax2.set_ylim([-1, 1])
titleAX2 = 'MACD of ' + ticker
ax2.set_title(titleAX2,
horizontalalignment='center',
verticalalignment='top')
ax2.legend(loc="lower left")
ax3.set_xlabel('Time')
ax3.set_ylabel('Value')
titleAX3 = 'Other Indicators of ' + ticker
ax3.set_title(titleAX3,
horizontalalignment='center',
verticalalignment='top')
ax3.plot(df.index,
df['momentum_rsi'],
color='purple',
alpha=1,
linewidth=1,
markersize=1,
label="RSI")
ax3.plot(df.index,
df['momentum_stoch'],
color='black',
alpha=1,
linewidth=1,
markersize=1,
label="Stochastic %K")
ax3.plot(df.index,
df['momentum_stoch_signal'],
color='red',
alpha=1,
linewidth=1,
markersize=1,
label="Stochastic %D")
horiz_line_data = np.array([30 for i in range(len(df.index))])
ax3.plot(df.index,
horiz_line_data,
'--',
alpha=0.5,
linewidth=0.5,
label="RSI Low")
horiz_line_data = np.array([70 for i in range(len(df.index))])
ax3.plot(df.index,
horiz_line_data,
'--',
alpha=0.5,
linewidth=0.5,
label="RSI High")
horiz_line_data = np.array([20 for i in range(len(df.index))])
ax3.plot(df.index,
horiz_line_data,
'--',
alpha=0.5,
linewidth=0.5,
label="Stochastic Low")
horiz_line_data = np.array([80 for i in range(len(df.index))])
ax3.plot(df.index,
horiz_line_data,
'--',
alpha=0.5,
linewidth=0.5,
label="Stochastic High")
ax3.legend(loc="lower left")
ax4.set_xlabel('Time')
ax4.set_ylabel('Number of Shares')
titleAX4 = 'Market Share Volume of ' + ticker
ax4.set_title(titleAX4,
horizontalalignment='center',
verticalalignment='top')
ax4.fill_between(df_volume.index.map(mdates.date2num),
df_volume.values,
0,
color="blue")
plt.tight_layout()
my_dpi = 96
if name is None:
name = ticker
fig.savefig('./figures/{}.png'.format(name),
dpi=my_dpi * 10,
bbox_inches='tight')
# if starting_date == None:
# fig.savefig('./figures/fullhistory/ETF/{}.png'.format(ticker), dpi=my_dpi*10,bbox_inches='tight')
if plot:
plt.show()
def graph_data(self, istock, MA1, MA2):
"""
Use this to dynamically pull a stock.
"""
try:
print('Currently Pulling', stock)
urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
stockFile =[]
try:
sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
splitSource = sourceCode.split('\n')
for eachLine in splitSource:
splitLine = eachLine.split(',')
if len(splitLine)==6:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep, highp, lowp, openp, volume = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={ 0: self.bytes_per_date_to_num('%Y%m%d')}
)
x = 0
y = len(date)
newAr = []
while x < y:
appendLine = date[x],openp[x],highp[x],lowp[x],closep[x],volume[x]
newAr.append(appendLine)
x+=1
Av1 = self.moving_average(closep, MA1)
Av2 = self.moving_average(closep, MA2)
SP = len(date[MA2-1:])
fig = plt.figure(facecolor='#07000d')
ax1 = plt.subplot2grid((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
candlestick_ohlc(ax1, newAr[-SP:], width=.6, colorup='#53c156', colordown='#ff1717')
Label1 = str(MA1)+' SMA'
Label2 = str(MA2)+' SMA'
ax1.plot(date[-SP:],Av1[-SP:],'#e1edf9',label=Label1, linewidth=1.5)
ax1.plot(date[-SP:],Av2[-SP:],'#4ee6fd',label=Label2, linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
maLeg = plt.legend(
loc=9,
ncol=2,
prop={'size':7},
fancybox=True,
borderaxespad=0.0
)
maLeg.get_frame().set_alpha(0.4)
textEd = pylab.gca().get_legend().get_texts()
pylab.setp(textEd[0:5], color = 'w')
volumeMin = 0
ax0 = plt.subplot2grid((6,4), (0,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
rsi = self.rsi(closep)
rsiCol = '#c1f9f7'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(date[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
ax0.axhline(70, color=negCol)
ax0.axhline(30, color=posCol)
ax0.fill_between(date[-SP:], rsi[-SP:], 70, where=(rsi[-SP:]>=70), facecolor=negCol, edgecolor=negCol, alpha=0.5)
ax0.fill_between(date[-SP:], rsi[-SP:], 30, where=(rsi[-SP:]<=30), facecolor=posCol, edgecolor=posCol, alpha=0.5)
ax0.set_yticks([30,70])
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#5998ff")
ax0.spines['top'].set_color("#5998ff")
ax0.spines['left'].set_color("#5998ff")
ax0.spines['right'].set_color("#5998ff")
ax0.tick_params(axis='y', colors='w')
ax0.tick_params(axis='x', colors='w')
plt.ylabel('RSI')
ax1v = ax1.twinx()
ax1v.fill_between(date[-SP:],volumeMin, volume[-SP:], facecolor='#00ffe8', alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
ax1v.set_ylim(0, 3*volume.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
ax2 = plt.subplot2grid(
(6, 4),
(5, 0),
sharex=ax1,
rowspan=1,
colspan=4,
axisbg='#07000d'
)
fillcolor = '#00ffe8'
# nslow = 26
# nfast = 12
nema = 9
_, _, macd = self.compute_macd(closep)
ema9 = self.exp_moving_average(macd, nema)
ax2.plot(date[-SP:], macd[-SP:], color='#4ee6fd', lw=2)
ax2.plot(date[-SP:], ema9[-SP:], color='#e1edf9', lw=1)
ax2.fill_between(
date[-SP:],
macd[-SP:]-ema9[-SP:],
0,
alpha=0.5,
facecolor=fillcolor,
edgecolor=fillcolor
)
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax2.spines['bottom'].set_color("#5998ff")
ax2.spines['top'].set_color("#5998ff")
ax2.spines['left'].set_color("#5998ff")
ax2.spines['right'].set_color("#5998ff")
ax2.tick_params(axis='x', colors='w')
ax2.tick_params(axis='y', colors='w')
plt.ylabel('MACD', color='w')
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
plt.suptitle(stock.upper(),color='w')
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.annotate(
'Big news!',
(date[510], Av1[510]),
xytext=(0.8, 0.9),
textcoords='axes fraction',
arrowprops=dict(facecolor='white', shrink=0.05),
fontsize=14,
color = 'w',
horizontalalignment='right',
verticalalignment='bottom'
)
plt.subplots_adjust(
left=.09,
bottom=.14,
right=.94,
top=.95,
wspace=.20,
hspace=0
)
plt.show()
fig.savefig('example.png', facecolor=fig.get_facecolor())
except Exception as e:
print('main loop', str(e))