def plot_candle_daily(ax, ts_code, name, last_date, misc):
daily = main_session.query(models.DailyPro).filter(models.DailyPro.ts_code == ts_code,
models.DailyPro.trade_date <= last_date).order_by(
models.DailyPro.trade_date.desc()).limit(sampling_count).all()
df = DataFrame()
for i, item in enumerate(daily[300::-1]):
df.loc[i, 'date'] = str(item.trade_date)
df.loc[i, 'open'] = item.open
df.loc[i, 'close'] = item.close
df.loc[i, 'high'] = item.high
df.loc[i, 'low'] = item.low
sma_5 = talib.SMA(np.array(df['close']), 5)
sma_10 = talib.SMA(np.array(df['close']), 10)
sma_20 = talib.SMA(np.array(df['close']), 20)
ax.set_xticks(range(0, len(df['date']), 20))
ax.set_xticklabels(df['date'][::20])
ax.plot(sma_5, linewidth=1, label='ma5')
ax.plot(sma_10, linewidth=1, label='ma10')
ax.plot(sma_20, linewidth=1, label='ma20')
mpf.candlestick2_ochl(ax, df['open'], df['close'], df['high'], df['low'],
width=0.5, colorup='red', colordown='green',
alpha=0.5)
# plt.grid()
print('plot {ts_code} {name}'.format(ts_code=ts_code, name=name))
def plot_OHLC(df, name):
df_m = df['volprice'] / df['vol']
sma_5 = df_m.rolling(window=5).mean()
sma_10 = df_m.rolling(window=10).mean()
sma_20 = df_m.rolling(window=20).mean()
x = round(len(df['date']) / 14)
fig = plt.figure(figsize=(24, 12))
ax = fig.add_subplot(1, 1, 1)
ax.set_xticks(range(0, len(df['date']), x))
ax.set_xticklabels(df['date'][::x])
ax.plot(sma_5, label='5MA', color='yellow')
ax.plot(sma_10, label='10MA', color='blue')
ax.plot(sma_20, label='20MA', color='purple')
ax.legend(loc='upper left')
mpf.candlestick2_ochl(ax,
df['open'],
df['close'],
df['high'],
df['low'],
width=0.5,
colorup='r',
colordown='green',
alpha=0.6)
ax.set_title(name)
plt.grid()
plt.show()
def save_candlestick_with_volume_img(df, save_filepath):
fig, ax = plt.subplots(nrows=2,
ncols=1,
figsize=(10, 10),
sharex=True,
gridspec_kw={'height_ratios': [4, 1]})
mpl_finance.candlestick2_ochl(ax[0],
df['open'],
df['close'],
df['high'],
df['low'],
width=1,
colorup='r',
colordown='g')
ax[0].grid(False)
ax[0].set_xticklabels([])
ax[0].set_yticklabels([])
ax[0].xaxis.set_visible(False)
ax[0].yaxis.set_visible(False)
ax[0].axis('off')
mpl_finance.volume_overlay(ax[1],
df['open'],
df['close'],
df['volume'],
colorup='r',
colordown='g',
width=1)
ax[1].grid(False)
ax[1].set_xticklabels([])
ax[1].set_yticklabels([])
ax[1].xaxis.set_visible(False)
ax[1].yaxis.set_visible(False)
ax[1].axis('off')
plt.savefig(save_filepath)
plt.close("all")
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(df1,ticker):
df1.set_index(df1['Date'],inplace=True)
#first plot is Heikin-Ashi candlestick
#use candlestick function and set Heikin-Ashi O,C,H,L
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')
#the second plot is the actual price with long/short positions as up/down arrows
ax2=plt.subplot2grid((200,1), (120,0), rowspan=80,ylabel='price',xlabel='')
df1['Close'].plot(ax=ax2,label=ticker)
#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
ax2.plot(df1.loc[df1['signals']==1].index,df1['Close'][df1['signals']==1],marker='^',lw=0,c='g',label='long')
ax2.plot(df1.loc[df1['signals']<0].index,df1['Close'][df1['signals']<0],marker='v',lw=0,c='r',label='short')
plt.grid(True)
plt.legend(loc='best')
plt.show()
def plot(klines, symbol, fig=None):
style.use('fivethirtyeight')
_, opens, highs, lows, closes, volumes = klines.columns
# Create figure
if fig is None:
fig = plt.figure()
else:
fig.clear()
ax1 = fig.add_subplot(111)
candlestick2_ochl(ax1, opens, closes, highs, lows, width=1, colorup='g')
ax1.set_ylim(lows.min() - lows.min() / 50)
# Add a seconds axis for the volume overlay
ax2 = ax1.twinx()
ax2.set_ylim(0, 5 * volumes.max())
ax2.grid(False)
add_volume(ax2, opens, closes, volumes)
#add_BBands(ax1, closes)
plt.title(symbol)
fig.savefig(r'C:\graphs\{}.png'.format(symbol), format='png',
dpi=400)
plt.close()
def smooth_demo():
data2 = QA.QA_fetch_crypto_asset_day_adv(['huobi'],
symbol=['btcusdt'],
start='2017-10-01',
end='2020-06-30 23:59:59')
xn = data2.close.values
ma5 = talib.MA(data2.close.values, 10)
hma5 = TA_HMA(data2.close.values, 10)
kama5 = TA_KAMA(data2.close.values, 10)
window_size, poly_order = 5, 1
yy_sg = savgol_filter(xn, window_size, poly_order)
plt.figure(figsize = (22,9))
ax1 = plt.subplot(111)
mpf.candlestick2_ochl(ax1, data2.data.open.values, data2.data.close.values, data2.data.high.values, data2.data.low.values, width=0.6, colorup='r', colordown='green', alpha=0.5)
#ax1.title("The smoothing windows")
#plt.plot(xn, lw=1, alpha=0.8)
ax1.plot(hma5, lw=2, linestyle="--", color='darkcyan', alpha=0.6)
ax1.plot(yy_sg, lw=1, color='darkcyan', alpha=0.8)
ax1.plot(ma5, lw=1, color='orange', alpha=0.8)
ax1.plot(kama5, lw=1, color='lightskyblue', alpha=0.8)
l=['Hull Moving Average', 'savgol_filter', 'talib.MA10', 'KAMA10']
ax1.legend(l)
plt.title("Smoothing a MA10 line")
plt.show()
def draw():
# quotes = raw_data_helper.get_data("tx5_data/202002/20200212.txt")
start = datetime.datetime(2018, 4, 1)
# df_2330 = pdr.DataReader('2330.TW', 'yahoo', start=start)
# print(df_2330['Close'])
# df_2330.index = df_2330.index.format(formatter=lambda x: x.strftime('%Y-%m-%d'))
fig = plt.figure(figsize=(24, 8))
# print(df_2330)
# print(df_2330['Open'])
ax = fig.add_subplot(1, 1, 1)
# ax.set_xticks(range(0, len(df_2330.index), 10))
# ax.set_xticklabels(df_2330.index[::10])
ax.set_xticks(range(0, len(quotes["time"]), 10))
ax.set_xticklabels(quotes["time"][::10])
# mpf.candlestick2_ochl(ax, df_2330['Open'], df_2330['Close'], df_2330['High'],
# df_2330['Low'], width=0.6, colorup='r', colordown='g', alpha=0.75)
mpf.candlestick2_ochl(ax,
quotes['Open'],
quotes['Close'],
quotes['High'],
quotes['Low'],
width=K_BAR_WIDTH,
colorup='r',
colordown='g',
alpha=0.75)
cursor = Cursor(ax, useblit=True, color='blue', linewidth=0.6)
# cursor = Cursor(ax)
# fig.canvas.mpl_connect('motion_notify_event', mouse_move)
fig.align_labels()
plt.xticks(rotation=0)
ax.format_coord = format_coord
plt.show()
def draw_k_line(df_Stock):
sma_5 = talib.SMA(np.array(df_Stock['close']), 5)
sma_10 = talib.SMA(np.array(df_Stock['close']), 10)
sma_30 = talib.SMA(np.array(df_Stock['close']), 30)
fig = plt.figure(figsize=(24, 20))
ax = fig.add_axes([0, 0.3, 1, 0.4])
ax.set_xticks(range(0, len(df_Stock.index), 1))
ax.set_xticklabels(df_Stock.index[::])
mpf.candlestick2_ochl(ax,
df_Stock['open'],
df_Stock['close'],
df_Stock['high'],
df_Stock['low'],
width=0.6,
colorup='r',
colordown='g',
alpha=0.75)
plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']
ax.plot(sma_5, label='5日均線')
ax.plot(sma_10, label='10日均線')
ax.plot(sma_30, label='30日均線')
plt.xticks(rotation=90)
plt.grid()
ax.legend()
plt.savefig('static/media/k_line.png')
def plot(data, keys, ilabel, has_subplot=True):
fig = plt.figure(figsize=(9, 5))
ax = fig.add_subplot(2, 1, 1)
ax.set_xticklabels([])
plt.title('close price - %s chart' % ilabel)
plt.grid(True)
candlestick2_ochl(ax,
data['open'],
data['close'],
data['high'],
data['low'],
width=1.0,
colorup='r',
colordown='g')
ax.set_ylabel(ilabel)
ax.set_xticks(range(0, len(data.index), 10))
if has_subplot:
bx = fig.add_subplot(2, 1, 2)
plt.ylabel('%s values' % ilabel)
i = 0
for key in keys:
plt.plot(data[key], COLORS[i], lw=0.75, linestyle='-', label=key)
i += 1
plt.legend(loc=2, prop={'size': 9.5})
plt.grid(True)
fig.autofmt_xdate()
plt.show()
def plot_candle_month(ax, ts_code, name, last_date, misc):
monthly = main_session.query(models.MonthlyPro).filter(models.MonthlyPro.ts_code == ts_code,
models.MonthlyPro.trade_date <= last_date).order_by(
models.MonthlyPro.trade_date.desc()).limit(sampling_count).all()
if monthly:
df = DataFrame()
for i, item in enumerate(monthly[300::-1]):
df.loc[i, 'date'] = str(item.trade_date)
df.loc[i, 'open'] = item.open
df.loc[i, 'close'] = item.close
df.loc[i, 'high'] = item.high
df.loc[i, 'low'] = item.low
sma_5 = talib.SMA(np.array(df['close']), 5)
sma_10 = talib.SMA(np.array(df['close']), 10)
sma_20 = talib.SMA(np.array(df['close']), 20)
ax.set_xticks(range(0, len(df['date']), 20))
ax.set_xticklabels(df['date'][::20])
ax.plot(sma_5, linewidth=1, label='ma5')
ax.plot(sma_10, linewidth=1, label='ma10')
ax.plot(sma_20, linewidth=1, label='ma20')
mpf.candlestick2_ochl(ax, df['open'], df['close'], df['high'], df['low'],
width=0.5, colorup='red', colordown='green',
alpha=0.5)
plt.title('{index} {ts_code} {name} circ_mv:{circ_mv}亿 holders:{holders_count} last_chg:{last_chg} min_max:{min_max_gap}'.format(index=int(misc['index']), ts_code=ts_code, name=name,
circ_mv=int(misc[COL_CIRC_MV]), holders_count=int(misc[COL_HOLDERS_COUNT]),
last_chg=misc[COL_LAST_CHG], min_max_gap=misc[COL_MIN_MAX_GAP]),
fontproperties='Heiti TC')
# plt.grid()
print('plot {ts_code} {name}'.format(ts_code=ts_code, name=name))
def buildImage(directory, dataset, filename, label, csvfile):
# Read each CSV window file
print("Building image for {}".format(filename))
#print("directory = {}, dataset = {}, filename = {} , label = {}, csvfile = {} ".format(directory, dataset, filename, label, csvfile))
fname = filename.split('/')[4]
df = pd.read_csv(filename, parse_dates=True, index_col=0)
df.fillna(0)
df.reset_index(inplace=True)
# Plot Chart
plt.style.use('dark_background')
fig = plt.figure(figsize = (dimension / dpi, dimension / dpi ), dpi = dpi)
chart = fig.add_subplot(1,1,1)
candlestick2_ochl(chart, df['Open'], df['Close'], df['High'], df['Low'],
width=1, colorup='#77d879', colordown='#db3f3f')
chart.grid(False)
chart.set_xticklabels([])
chart.set_yticklabels([])
chart.yaxis.set_visible(False)
chart.xaxis.set_visible(False)
chart.axis('off')
# Save chart image
imgfile = '{}/{}/{}/{}.png'.format(directory, dataset, label, fname)
print("Image File: {}".format(imgfile))
fig.savefig(imgfile, pad_inches=0, transparent=False)
plt.close(fig)
def create_EMAplot(data, ticker):
formatter = mdates.DateFormatter("%Y-%m")
ax.xaxis.set_major_formatter(formatter)
locator = mdates.YearLocator()
ax.xaxis.set_major_locator(locator)
#ax.xaxis.set_minor_locator(mdates.MonthLocator())
font={'weight':'normal',
'size':12}
ax4.set_title('Candlestick', fontdict=font)
#To make the chart more readable, get one month of data
x_vals=data.index
mpf.candlestick2_ochl(ax4, data["open"], data["close"],data["high"], data["low"], width=.5, colorup='k', colordown='r', alpha=0.75)
ax.xaxis.set_major_formatter(formatter)
ax4.set_ylabel('Price (USD)')
ax4.legend()
ax5.bar(x_vals, data['volume'])
ax5.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
ax5_locator = mdates.MonthLocator()
ax5.xaxis.set_major_locator(ax5_locator)
ax5.set_ylabel('Price (USD)')
ax5.set_xlabel('Date')
font={'weight':'normal',
'size':12}
ax5.set_title("Volume (x10,000,000)", fontdict=font)
ax5.legend()
def plot(df1, ticker):
df1.set_index(df1['Date'], inplace=True)
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)
ax2.plot(df1.loc[df1['signals'] == 1].index,
df1['Close'][df1['signals'] == 1],
marker='^',
lw=0,
c='g',
label='long')
ax2.plot(df1.loc[df1['signals'] < 0].index,
df1['Close'][df1['signals'] < 0],
marker='v',
lw=0,
c='r',
label='short')
plt.grid(True)
plt.legend(loc='best')
plt.show()
def draw_cci3(self,code,ktype):
kk=gu_save('')
hh=gu_zb(0)
name=kk.get_name(code)
print(name+code)
#取4个类型的df
df=kk.get_k_from_api(code,ktype)
#取4个类型的CCi
if df.empty:
return 0
cci=hh.cci(df)[-self.total:]
df=df[-self.total:]
up,dw=hh.cci_ana_dd2(cci)
cciii=[]
for i in range(0,self.total):
if i in dw:
cciii.append(cci[i])
else:
cciii.append(0)
#画出最后3条线
fig, ax = plt.subplots(2, 1, figsize=(16,8))
ax[0].set_title(name+code+'--'+ktype,fontproperties = 'SimHei',fontsize = 20)
mpf.candlestick2_ochl(ax=ax[0],opens=df["open"].values.tolist(), closes=df["close"].values, highs=df["high"].values, lows=df["low"].values,width=0.7,colorup='r',colordown='g',alpha=0.7)
ax[1].axhline(y=100, color='b', linestyle=':')
ax[1].axhline(y=-100, color='b', linestyle=':')
ax[1].plot(cci,'r')
ax[1].plot(cciii,'b')
plt.show()
return 0
def test3(self):
df1, name = self._getk()
#cci处理
cci_ht, cci_qrfj = self.cci_cl(df1)
df = df1[-self.total:]
#顶点坐标
up_li2, dw_li2 = self.cci_draw_line(cci_ht, cci_qrfj)
print(up_li2)
#return 0
fig = plt.figure()
X = 2
Y = 1
ax5 = fig.add_subplot(X, Y, 1)
ax2 = fig.add_subplot(X, Y, 2)
#画K线
mpf.candlestick2_ochl(ax=ax5,
opens=df["open"].values.tolist(),
closes=df["close"].values,
highs=df["high"].values,
lows=df["low"].values,
width=0.7,
colorup='r',
colordown='g',
alpha=0.7)
#画cci指标
ax2.plot(cci_ht, 'r')
#叠加文字
#c_text=[]
'''for i in range(3,self.total):
if cci_ht[i]<cci_ht[i-1] and cci_ht[i-1]>cci_ht[i-2] and cci_ht[i]>90:
c_text.append([i,cci_ht[i],'p'])
for x in c_text:
plt.text(x[0],x[1],x[2],size = 5,bbox = dict(facecolor = "r", alpha = 0.2))
'''
iii = self.total - 1
while cci_qrfj[iii] == 100:
iii -= 1
x = np.linspace(0, self.total, 10)
print(up_li2)
for u in up_li2[-4:]:
y1 = u[1]
y2 = u[3]
x1 = u[0]
x2 = u[2]
k = (y2 - y1) / (x2 - x1)
if k > 0: continue
b = y2 - k * x2
y = k * x + b
plt.plot(x, y, '-.y')
#画两点线
plt.axhline(y=100, color='b', linestyle=':')
plt.axhline(y=-100, color='b', linestyle=':')
plt.show()
return 0
def ohlc2cs(fname, seq_len, dataset_type, dimension, use_volume):
# python preprocess.py -m ohlc2cs -l 20 -i stockdatas/EWT_testing.csv -t testing
print("Converting olhc to candlestick")
symbol = fname.split('_')[0]
symbol = symbol.split('/')[1]
print(symbol)
path = "{}".format(os.getcwd())
# print(path)
if not os.path.exists("{}/dataset/{}_{}/{}/{}".format(path, seq_len, dimension, symbol, dataset_type)):
os.makedirs("{}/dataset/{}_{}/{}/{}".format(path,
seq_len, dimension, symbol, dataset_type))
df = pd.read_csv(fname, parse_dates=True, index_col=0)
df.fillna(0)
plt.style.use('dark_background')
df.reset_index(inplace=True)
df['Date'] = df['Date'].map(mdates.date2num)
for i in range(0, len(df)):
# ohlc+volume
c = df.ix[i:i + int(seq_len) - 1, :]
if len(c) == int(seq_len):
my_dpi = 96
fig = plt.figure(figsize=(dimension / my_dpi,
dimension / my_dpi), dpi=my_dpi)
ax1 = fig.add_subplot(1, 1, 1)
candlestick2_ochl(ax1, c['Open'], c['Close'], c['High'],
c['Low'], width=1,
colorup='#77d879', colordown='#db3f3f')
ax1.grid(False)
ax1.set_xticklabels([])
ax1.set_yticklabels([])
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)
ax1.axis('off')
# create the second axis for the volume bar-plot
# Add a seconds axis for the volume overlay
# if use_volume:
# ax2 = ax1.twinx()
# # Plot the volume overlay
# bc = volume_overlay(ax2, c['Open'], c['Close'], c['Volume'],
# colorup='#77d879', colordown='#db3f3f', alpha=0.5, width=1)
# ax2.add_collection(bc)
# ax2.grid(False)
# ax2.set_xticklabels([])
# ax2.set_yticklabels([])
# ax2.xaxis.set_visible(False)
# ax2.yaxis.set_visible(False)
# ax2.axis('off')
pngfile = 'dataset/{}_{}/{}/{}/{}-{}.png'.format(
seq_len, dimension, symbol, dataset_type, fname[11:-4], i)
fig.savefig(pngfile, pad_inches=0, transparent=False)
plt.close(fig)
# normal length - end
print("Converting olhc to candlestik finished.")
def plotStock(data, ax):
#fig = plt.figure(figsize=(15, 10))
data.sort_index()
data.reset_index()
data.dropna()
data.index.name = 'date'
data = data.loc[len(data.index) - 200:, :]
#data['date'] = mdates.date2num(pd.to_datetime(data['date']))
#data['date'] = mdates.date2num(data['date'].astype(dt.date))
data = data.reindex(
columns=['date', 'open', 'high', 'low', 'close', 'EMA_5', 'EMA_10'])
#fig = plt.figure()
#fig = plt.figure(facecolor='#07000d', figsize=(15, 10))
ax = plt.subplot2grid((6, 4), (1, 0), rowspan=4, colspan=4, facecolor='k')
candlestick2_ochl(ax,
data['open'],
data['close'],
data['high'],
data['low'],
width=0.5,
colorup='r',
colordown='green',
alpha=0.6)
ax.set_xticklabels(data['date'][::10])
ax.plot(data['EMA_5'].values, label='5 日均线')
ax.plot(data['EMA_10'].values, label='10 日均线')
#ax1.set_xticks(range(0,len(data['date']),10))
#candlestick_ochl(ax1, data.values,width=0.5, colorup='r', colordown='b', alpha=0.6)
#ax.plot(data['date'].values, data['EMA_5'].values, '#e1edf9', label='5 日均线',lw=1.5)
#ax.plot(data['date'].values, data['EMA_10'].values, '#4ee6fd', label='10 日均线',lw=1.5)
ax.grid(True, color='w')
#ax.xaxis.set_major_locator(mticker.MaxNLocator(10))
#ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y%m%d'))
#ax.yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax.spines['bottom'].set_color("#5998ff")
ax.spines['top'].set_color("#5998ff")
ax.spines['left'].set_color("#5998ff")
ax.spines['right'].set_color("#5998ff")
ax.tick_params(axis='y', colors='w')
ax.tick_params(axis='x', colors='w')
#ax.set_xlabel("Date")
#ax.xaxis.label.set_color("r")
ax.set_ylabel("stock price and volume")
ax.yaxis.label.set_color("r")
#plt.setp(ax0.get_xticklabels(), visible=False)
#plt.setp(ax1.get_xticklabels(), visible=False)
plt.show()
def gen_plot_1(dataframe, startidx, endidx):
#####
# plot just price
#####
endidx = endidx + 1
#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['Adj Vol'][startidx:endidx].tolist()
time = dataframe['Time'][startidx:endidx].tolist()
# date=dataframe['Date'][startidx:endidx].tolist()
open.reverse()
high.reverse()
low.reverse()
close.reverse()
volume.reverse()
time.reverse()
# date.reverse()
num_ticks = 6
def mydate(x, pos):
try:
return time[int(x)]
except IndexError:
return ''
#####
# plot just price
#####
fig = plt.figure()
ax = plt.subplot()
candlestick2_ochl(ax,
open,
close,
high,
low,
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/Time')
ax.set_xlim(-1.0, len(open) - 1.0)
ax.xaxis.set_major_locator(ticker.MaxNLocator(num_ticks))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
ax.grid()
plt.show()
def show_candle_stick(self, xstep=None, ystep=None):
# 创建图像和子图
fig = plt.figure(figsize=(20, 13))
ax = fig.add_axes([0.02, 0.4, 1, 0.6])
ax2 = fig.add_axes([0.02, 0.05, 1, 0.35])
# k 线
mpf.candlestick2_ochl(ax,
self.data['open'],
self.data['close'],
self.data['high'],
self.data['low'],
width=0.5,
colorup='r',
colordown='g',
alpha=0.8)
# 设置横纵轴坐标
# ax.set_xticks(range(0, len(self.data.index), 10))
ax.set_yticks(
np.arange(int(self.min_price * 0.9), int(self.max_price * 1.1), 2))
# ax.set_ylabel('price')
ax2.set_yticks(np.arange(0, int(self.max_vol * 1.1), 50000))
# ax2.set_ylabel('vol')
ax.plot(self.sma_10, label='10 日均线')
ax.plot(self.sma_30, label='30 日均线')
# 创建图例
ax.legend(loc='upper center', fontsize=14)
# 网格
ax.grid(True)
# 成交量
mpf.volume_overlay(ax2,
self.data['open'],
self.data['close'],
self.data['volume'],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
# ax2.set_xticks(range(0, len(self.data.index), 10))
# ax2.set_xticklabels(self.data.index.strftime('%Y-%m-%d'),
# fontdict = {'fontsize':14},
# rotation=30)
ax2.set_xticks(np.arange(0, len(self.data.index), xstep))
xlabels = list(
self.data.index.strftime('%Y-%m-%d'))[0:len(self.data.index):xstep]
ax2.set_xticklabels(xlabels, fontdict={'fontsize': 15}, rotation=45)
print(xlabels)
ax2.grid(True)
plt.show()
def volumn():
fig = plt.figure(figsize=(8, 6), dpi=100, facecolor="white") # 创建fig对象
gs = gridspec.GridSpec(2, 1, left=0.05, bottom=0.15, right=0.96, top=0.96, wspace=None, hspace=0,
height_ratios=[3.5, 1])
numt = np.arange(0, len(df_stockload.index))
graph_KAV = fig.add_subplot(gs[0, :])
graph_KAV.set_title(u"600797 浙大网新-日K线")
graph_KAV.set_xlabel(u"日期")
graph_KAV.set_ylabel(u"价格")
graph_KAV.set_xlim(0, len(df_stockload.index)) # 设置一下x轴的范围
graph_KAV.set_xticks(range(0, len(df_stockload.index), 15)) # X轴刻度设定，每15天标一个日期
graph_KAV.grid(True, color='k')
graph_KAV.set_xticklabels(
[df_stockload.index.strftime('%Y-%m-%d')[index] for index in graph_KAV.get_xticks()]) # 标签设置为日期
# 绘制K线走势
mpf.candlestick2_ochl(graph_KAV, df_stockload.Open, df_stockload.Close, df_stockload.High, df_stockload.Low,
width=0.5, colorup='r', colordown='g')
graph_VOL = fig.add_subplot(gs[1, :])
graph_VOL.bar(numt, df_stockload.Volume,
color=['g' if df_stockload.Open[x] > df_stockload.Close[x] else 'r' for x in
range(0, len(df_stockload.index))])
graph_VOL.set_ylabel(u"成交量")
graph_VOL.set_xlabel(u"日期")
graph_VOL.set_xlim(0, len(df_stockload.index)) # 设置一下X轴的范围
graph_VOL.set_xticks(range(0, len(df_stockload.index), 15)) # X轴刻度设定，每15天标一个日期
graph_VOL.set_xticklabels(
[df_stockload.index.strftime('%Y-%m-%d')[index] for index in graph_VOL.get_xticks()]) # 标签设置为日期
# 将日K线X轴labels隐藏
for label in graph_KAV.xaxis.get_ticklabels():
label.set_visible(False)
# X轴每个ticker标签都向右倾斜45度
for label in graph_VOL.xaxis.get_ticklabels():
label.set_rotation(45)
label.set_fontsize(10) #设置标签字号
plt.show()
def draw_kchart(stockNumber):
stock_name = get_stock_name(stockNumber)
if stock_name == "no": return "股票代碼錯誤!"
end = datetime.datetime.now()
date = end.strftime("%Y%m%d")
year = str(int(date[0:4]) - 1)
month = date[4:6]
stock = pdr.DataReader(stockNumber + '.TW', 'yahoo', start= year+"-"+month,end=end)
stock.index = stock.index.format(formatter=lambda x: x.strftime('%Y-%m-%d'))
#KD
sma_10 = talib.SMA(np.array(stock['Close']), 10)
sma_30 = talib.SMA(np.array(stock['Close']), 30)
stock['k'], stock['d'] = talib.STOCH(stock['High'], stock['Low'], stock['Close'])
stock['k'].fillna(value=0, inplace=True)
stock['d'].fillna(value=0, inplace=True)
sma_5 = talib.SMA(np.array(stock['Close']), 5)
sma_20 = talib.SMA(np.array(stock['Close']), 20)
sma_60 = talib.SMA(np.array(stock['Close']), 60)
fig = plt.figure(figsize=(20, 10))#,facecolor='black')
fig.suptitle(stock_name.strip('加到投資組合'),fontsize="x-large", FontProperties=chinese_title)
ax = fig.add_axes([0.1,0.5,0.75,0.4])
plt.title("開盤價:"+str(round(stock['Open'][-1], 2))+" 收盤價:"+str(round(stock['Close'][-1], 2))+"\n最高價:"+str(round(stock['High'][-1] ,2))+" 最低價:"+str(round(stock['Low'][-1], 2)),fontsize="25",fontweight='bold',bbox=dict(facecolor='yellow',edgecolor='red',alpha=0.65),loc='left', FontProperties=chinese_subtitle)
plt.title("更新日期:"+stock.index[-1],fontsize="20",fontweight='bold',loc="right", FontProperties=chinese_subtitle)
plt.grid(True,linestyle="--",color='gray',linewidth='0.5',axis='both')
ax2 = fig.add_axes([0.1,0.3,0.75,0.20])
plt.grid(True,linestyle="--",color='gray',linewidth='0.5',axis='both')
ax3 = fig.add_axes([0.1,0.03,0.75,0.20])
mpf.candlestick2_ochl(ax, stock['Open'], stock['Close'], stock['High'],
stock['Low'], width=0.6, colorup='r', colordown='g', alpha=0.75)
ax.plot(sma_5, label='5日均線')
ax.plot(sma_10, label='10日均線')
ax.plot(sma_20, label='20日均線')
ax.plot(sma_60, label='60日均線')
ax2.plot(stock['k'], label='K值')
ax2.plot(stock['d'], label='D值')
ax2.set_xticks(range(0, len(stock.index),10))
ax2.set_xticklabels(stock.index[::10],fontsize="10", rotation=25)
mpf.volume_overlay(ax3, stock['Open'], stock['Close'], stock['Volume'], colorup='r', colordown='g', width=0.5, alpha=0.8)
ax3.set_xticks(range(0, len(stock.index),10))
ax3.set_xticklabels(stock.index[::5],fontsize="10", rotation=45)
ax.legend(prop=chinese_font, fontsize=20);
ax2.legend(prop=chinese_font);
plt.grid(True,linestyle="--",color='gray',linewidth='0.5',axis='both')
plt.gcf()
plt.savefig("Kchrat.png",bbox_inches='tight',dpi=300,pad_inches=0.0)
plt.show()
plt.close()
return Imgur.showImgur("Kchrat")
def show_K_xian_tu(data_list):
ax1 = plt.subplot2grid((9, 10), (0, 0), rowspan=7, colspan=10)
ax2 = plt.subplot2grid((9, 10), (7, 0), rowspan=2, colspan=10, sharex=ax1)
data_time = data_list.index
print(data_time)
data_list = data_list.reset_index()
data_list['date'] = data_list['date'].map(mdate.date2num) # 把日期形式改变成数字形式方便matplotlib读取
mpf.candlestick2_ochl(ax=ax1, opens=data_list['open'], closes=data_list['close'], highs=data_list['high'], lows=data_list['low'], width=1,colorup='red',colordown='green')
ax2.bar(data_list.index, data_list['volume'])
plt.xticks(data_list.index[::200],data_time[::200])
plt.show()
def dr_cci2(szb):
#详细分析
co=cciorder(szb)
#取4个类型的df
df=co.df
if df.empty:return 0
cci=co.cci
ln=len(cci)
total=250
if ln<total:
total=ln
PLUS_DI,MINUS_DI,ADX,ADXR=co.stockzb.dmi()
bixl_li,bicz_li=co.bias_1()
vok=co.vol1()
vo=vok[-total:]
bixl=bixl_li[-total:]
bicz=bicz_li[-total:]
df=df[-total:]
cci=cci[-total:]
MINUS_DI=MINUS_DI[-total:]
PLUS_DI=PLUS_DI[-total:]
ADX=ADX[-total:]
ADXR=ADXR[-total:]
#df=df[-self.total:]
#4个类型的顶点
#画出最后3条线
name=co.getname()
code=co.getcode()
fig, ax = plt.subplots(4, 1, figsize=(16,8))
ax[0].set_title(name+code+'--',fontproperties = 'SimHei',fontsize = 20)
ax[1].plot(cci,'r')
ax[2].plot(ADX,'r')
ax[2].plot(PLUS_DI,'y')
ax[2].plot(MINUS_DI,'b')
ax[2].plot(ADXR,'g')
ax[2].axhline(y=80, color='b', linestyle=':')
ax[2].axhline(y=50, color='b', linestyle=':')
ax[2].axhline(y=20, color='b', linestyle=':')
ax[3].plot(vo,'r')
#ax[3].plot(bicz,'b')
ax[3].axhline(y=0, color='b', linestyle=':')
mpf.candlestick2_ochl(ax=ax[0],opens=df["open"].values.tolist(), closes=df["close"].values, highs=df["high"].values, lows=df["low"].values,width=0.7,colorup='r',colordown='g',alpha=0.7)
ax[1].axhline(y=100, color='b', linestyle=':')
ax[1].axhline(y=-100, color='b', linestyle=':')
plt.style.use('ggplot')
plt.show()
return 1
def test2(self):
df1, name = self._getk()
index = 619
N = 14
df = df1
#图表格式
X = 3
Y = 1
fig = plt.figure()
ax5 = fig.add_subplot(X, Y, 1)
ax2 = fig.add_subplot(X, Y, 2)
ax3 = fig.add_subplot(X, Y, 3)
print(name, df.loc[index, 'date'])
'''fig = plt.figure()
ax1=fig.add_subplot(5,1,1)
ax2=fig.add_subplot(5,1,2)
ax3=fig.add_subplot(5,1,3)
ax4=fig.add_subplot(5,1,4)
ax5=fig.add_subplot(5,1,5)
ax1.plot(macd1.values.tolist(),'g',macd2.values.tolist(),'b',macd3.values.tolist(),'y')
ax2.plot(up.values.tolist(),'g',mid.values.tolist(),'b',lo.values.tolist(),'y')
ax3.plot(cci.values.tolist(),'r')'''
#dmi图
MINUS_DI, PLUS_DI, ADX, ADXR = self.dmi(df, index)
l = len(ADX.values.tolist())
print(l)
zz = [
1 if ADX.loc[x] < MINUS_DI.loc[x] and ADX.loc[x] < PLUS_DI.loc[x]
else 0 for x in range(l)
]
print(zz)
ax3.plot(zz)
ax3.plot(PLUS_DI.values.tolist(), 'y', MINUS_DI.values.tolist(), 'k')
#ax2.plot(ADX.values.tolist(),'g',ADXR.values.tolist(),'b',PLUS_DI.values.tolist(),'y',MINUS_DI.values.tolist(),'k')
#股价图
mpf.candlestick2_ochl(ax=ax5,
opens=df["open"].values,
closes=df["close"].values,
highs=df["high"].values,
lows=df["low"].values,
width=0.7,
colorup='r',
colordown='g',
alpha=0.7)
plt.show()
def save_candlestick_img_with_volume(df, save_filepath):
fig, ax = plt.subplots(nrows=2,
ncols=1,
figsize=(2.56, 2.56),
sharex=True,
gridspec_kw={'height_ratios': [2, 1]})
mpl_finance.candlestick2_ochl(ax[0],
df['Open'],
df['Close'],
df['High'],
df['Low'],
width=1,
colorup='r',
colordown='g')
x_list = [i for i in range(len(df))]
ma5_value = numpy.array(df["Open_MA_5"])
ma25_value = numpy.array(df["Open_MA_25"])
ma75_value = numpy.array(df["Open_MA_75"])
ax[0].plot(x_list, ma5_value, markersize=2, color='black')
ax[0].plot(x_list, ma25_value, markersize=2, color='y')
ax[0].plot(x_list, ma75_value, markersize=2, color='b')
ax[0].grid(False)
ax[0].set_xticklabels([])
ax[0].set_yticklabels([])
ax[0].xaxis.set_visible(False)
ax[0].yaxis.set_visible(False)
y_min = min(df["Low"].min(), ma5_value.min(), ma25_value.min(),
ma75_value.min())
y_max = max(df["High"].max(), ma5_value.max(), ma25_value.max(),
ma75_value.max())
ax[0].set_ylim([y_min, y_max])
ax[0].axis('off')
mpl_finance.volume_overlay(ax[1],
df['Open'],
df['Close'],
df['volume'],
colorup='black',
colordown='black',
width=2)
ax[1].grid(False)
ax[1].set_xticklabels([])
ax[1].set_yticklabels([])
ax[1].xaxis.set_visible(False)
ax[1].yaxis.set_visible(False)
ax[1].axis('off')
plt.savefig(save_filepath)
plt.close("all")
gc.collect()
def fig_plot(df, stock_id):
n = -200
closes = np.array(df['Close'].iloc[n:].tolist())
highs = np.array(df['High'].iloc[n:].tolist())
lows = np.array(df['Low'].iloc[n:].tolist())
opens = np.array(df['Open'].iloc[n:].tolist())
Time = np.array(df['Date'].map(lambda x: str(x)).iloc[n:].tolist())
Vol = np.array(df['Volume'].iloc[n:].tolist())
#use talib pakage to calculate SMA series
avg60 = talib.SMA(closes, timeperiod=60)
avg120 = talib.SMA(closes, timeperiod=120)
avg150 = talib.SMA(closes, timeperiod=150)
#create plot figure
fig = plt.figure()
ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2)
#x label range
ax1.set_xticks(range(0, len(Time), 15))
# ax1.set_xticklabels(Time[::10], rotation=20)
mpf.candlestick2_ochl(ax1, opens, closes, highs, lows, width=0.6, colorup='r', colordown='g')
#SMA line
ax1.plot(avg60, 'g', label='MA60')
ax1.plot(avg120, 'k--', label='MA120')
ax1.plot(avg150, 'k.', label='MA150')
#價量結構
for n in [10, 50]:
MaxVolClose = df['Close'].iloc[df['Volume'].iloc[-n:].idxmax()]
MaxVolOpen = df['Open'].iloc[df['Volume'].iloc[-n:].idxmax()]
if MaxVolClose > MaxVolOpen:
ax1.axhline(y=MaxVolOpen, c='r', ls='--', lw=2, label=f'support at {MaxVolOpen}')
else:
ax1.axhline(y=MaxVolClose, c='b', ls='--', lw=2, label=f'pressure at {MaxVolClose}')
#成交量
ax2.bar(Time, Vol ,color='b')
ax2.set_xticks(range(0, len(Time), 15))
# ax2.set_xticklabels(Time[::10], rotation=40)
#圖片網紋
ax1.grid(True, ls=':', color='k', alpha=0.5)
ax2.grid(True, ls=':', color='k', alpha=0.5)
#fig setting
plt.rc('figure', figsize=(10, 10))
plt.title(stock_id)
plt.subplots_adjust(wspace=0, hspace=0)
ax1.legend(loc = 'best')
fig_path = 'C:/Users/Drey/Pictures/uptrend/'+ stock_id.split('.')[0] + '.png'
# fig_path = '/home/drey/stockgallery/media/images/' + stock_id.split('.')[0] + '.png'
plt.savefig(fig_path)
plt.close()
def stock_Candlestick(userstock):
stock=TheConstructor(userstock)
fig = plt.figure(figsize=(24, 8))
ax = fig.add_subplot(1, 1, 1)
ax.set_xticks(range(0, len(stock.index), 5))
ax.set_xticklabels(stock.index[::5])
plt.xticks(fontsize=10,rotation=90)
mpf.candlestick2_ochl(ax, stock['Open'], stock['Close'], stock['High'], stock['Low'],
width=0.5, colorup='r', colordown='green',
alpha=0.6)
plt.title("Candlestick_chart") # 標題設定
plt.grid()
plt.savefig('Candlestick_chart.png') #存檔
plt. close() # 殺掉記憶體中的圖片
return Imgur.showImgur('Candlestick_chart')#開始利用imgur幫我們存圖片，以便於等等發送到手機
def draw_subgraph(self, stockdat, numt):
""" 绘制K线图 """
# ohlc = list(zip(np.arange(0,len(stockdat.index)),stockdat.Open,stockdat.Close,stockdat.High,stockdat.Low))#使用zip方法生成数据列表
mpf.candlestick2_ochl(self.am,
stockdat.Open,
stockdat.Close,
stockdat.High,
stockdat.Low,
width=0.5,
colorup='r',
colordown='g') # 绘制K线走势
''' 绘制均线 '''
self.am.plot(numt, stockdat['Ma20'], 'black', label='M20', lw=1.0)
self.am.plot(numt, stockdat['Ma60'], 'green', label='M60', lw=1.0)
self.am.plot(numt, stockdat['Ma120'], 'blue', label='M120', lw=1.0)
self.am.legend(loc='best', shadow=True, fontsize='10')
''' 绘制成交量 '''
self.vol.bar(numt,
stockdat.Volume,
color=[
'g' if stockdat.Open[x] > stockdat.Close[x] else 'r'
for x in range(0, len(stockdat.index))
])
''' 绘制MACD '''
# 绘制BAR>0 柱状图
bar_red = np.where(stockdat['macd_bar'] > 0, 2 * stockdat['macd_bar'],
0)
# 绘制BAR<0 柱状图
bar_green = np.where(stockdat['macd_bar'] < 0,
2 * stockdat['macd_bar'], 0)
self.macd.plot(numt, stockdat['macd_dif'], 'red',
label='macd dif') # dif
self.macd.plot(numt, stockdat['macd_dea'], 'blue',
label='macd dea') # dea
self.macd.bar(numt, bar_red, facecolor='red', label='hist bar')
self.macd.bar(numt, bar_green, facecolor='green', label='hist bar')
self.macd.legend(loc='best', shadow=True, fontsize='10')
# legend = self.macd.legend(loc='best',shadow=True, fontsize ='10')
# legend.get_frame().set_facecolor('#00FFCC')# Put a nicer background color on the legend.
# legend.get_title().set_fontsize(fontsize = 20)
''' 绘制KDJ '''
self.devol.plot(numt, stockdat['K'], 'blue', label='K') # K
self.devol.plot(numt, stockdat['D'], 'g--', label='D') # D
self.devol.plot(numt, stockdat['J'], 'r-', label='J') # J
self.devol.legend(loc='best', shadow=True, fontsize='10')
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, 6))
fig, (ax, ax2) = plt.subplots(2,1, gridspec_kw = {'height_ratios':[3, 1]}, figsize=(8, 8))
#ax = fig.add_subplot(111)
ax.set_title('code:' + str(self.code) + "\n[rule]buy in 5 candle from GC at MACD", loc='center', fontsize=20)
ax.set_xlabel('day')
ax.set_ylabel('price')
ax.autoscale_view()
ax.grid(True)
#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)
self.__plt_ichimoku_cloud(ax)
self.__plt_bolinger_band(ax)
self.__plt_envelope(ax)
self.__plt_upper_support_line(ax)
self.__plt_GMMA(ax)
self.__plt_volume(ax)
#self.__plt_RSI(ax2)
#self.__plt_RCI(ax2)
self.__plt_MACD(ax2)
#plt.xlim([30,65])
ax.set_xlim(80, 135)
ax2.set_xlim(80, 135)
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)
fig.tight_layout()
plt.show()
def plot_stock_line(api,code, name, table_name, current, start='2017-10-01', save=False):
title = '{} {} {} {}'.format(current, code, name, table_name)
title = title.replace('*', '_')
if os.path.exists(title + '.png'):
return
if code is None and name is not None:
code = base_info[base_info['name'] == name]['code'].values[0]
df = None
for _ in range(4):
try:
df = ts.bar(code, conn=api, start_date=start)
except Exception as e:
logger.info(e)
ts.close_apis(api)
time.sleep(random.random() * 30)
api = ts.get_apis()
else:
break
if df is None:
return
df = df.sort_index()
if name is None:
name = base_info[base_info['code'] == code]['name'].values[0]
df = df.reset_index()
df['datetime'] = df['datetime'].dt.strftime('%Y-%m-%d')
sma5 = talib.SMA(df['close'].values, 5)
sma20 = talib.SMA(df['close'].values, 10)
# ax.set_xticks(range(0,len(df),20))
# # ax.set_xticklabels(df['date'][::5])
# ax.set_xticklabels(df['datetime'][::20])
fig = plt.figure(figsize=(10, 8))
# fig,(ax,ax2)=plt.subplots(2,1,sharex=True,figsize=(16,10))
ax = fig.add_axes([0, 0.3, 1, 0.50])
ax2 = fig.add_axes([0, 0.1, 1, 0.20])
candlestick2_ochl(ax, df['open'], df['close'], df['high'], df['low'], width=1, colorup='r', colordown='g',
alpha=0.6)
ax.grid(True)
ax.set_title(title)
ax.plot(sma5, label='MA5')
ax.legend()
ax.plot(sma20, label='MA20')
ax.legend(loc=2)
ax.grid(True)
# df['vol'].plot(kind='bar')
volume_overlay(ax2, df['open'], df['close'], df['vol'], width=1, alpha=0.8, colordown='g', colorup='r')
ax2.set_xticks(range(0, len(df), 5))
# ax.set_xticklabels(df['date'][::5])
ax2.set_xticklabels(df['datetime'][::5])
plt.setp(ax2.get_xticklabels(), rotation=30, horizontalalignment='right')
ax2.grid(True)
plt.subplots_adjust(hspace=0.3)
if save:
# path = os.path.join(os.path.dirname(__file__),'data',today)
fig.savefig(title + '.png')
else:
plt.show()
plt.close()
