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 __plt_volume(self, ax):
ax2 = ax.twinx()
ax2.set_ylim([0, max(self.ohcl["volume"])*4])
ax2.set_ylabel("volume")
finance.volume_overlay(ax2, self.ohcl["open"],
self.ohcl["close"],
self.ohcl["volume"],
colorup='r', colordown='g',
width=0.5, alpha=0.5)
def plot_stock_price(self):
df = self.__data_center.query('TradeData.Stock.Daily', '600000.SSE')
df = df[df['trade_date'] > years_ago(1)]
# df['trade_date'] = df['trade_date'].apply(lambda d: mdates.date2num(d.to_pydatetime()))
adjust_ratio = df['adj_factor']
price_open = df['open'] * adjust_ratio
price_close = df['close'] * adjust_ratio
price_high = df['high'] * adjust_ratio
price_low = df['low'] * adjust_ratio
self.__figure.clear()
ax1 = self.__figure.add_subplot(2, 1, 1)
ax2 = self.__figure.add_subplot(2, 1, 2)
time_serial = pd.to_datetime(df['trade_date'], format="%Y/%m/%d")
# ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2, colspan=1) # 佔全圖2/3的子圖一
ax1.set_xticks(range(0, len(time_serial), 10)) # 設定X軸座標
ax1.set_xticklabels(time_serial) # 設定X軸標籤
mpf.candlestick2_ohlc(ax1, df['open'], df['high'], df['low'], df['close'], width=0.6, colorup='r',
colordown='k', alpha=1) # 畫出K線圖
ax1.tick_params('x', bottom=False, labelbottom=False) # 子圖一不顯示X軸標籤
ax1.set_axisbelow(True) # 設定格線在最底圖層
ax1.grid(True) # 畫格線
# ax2 = plt.subplot2grid((3, 1), (2, 0), rowspan=1, colspan=1, sharex=ax1) # 佔全圖1/3的子圖二，設定X軸座標與子圖一相同
mpf.volume_overlay(ax2, df['open'], df['close'], df['amount'] / 1000, colorup='b', colordown='b',
width=0.6, alpha=1) # 畫出成交量
ax2.set_axisbelow(True) # 設定格線在最底圖層
ax2.grid(True) # 畫格線
plt.gcf().autofmt_xdate() # 斜放X軸標籤
self.__canvas.draw()
# mpf.candlestick2_ochl(axes, price_open, price_close, price_high, price_low, colorup='r', colordown='g')
#
# axes.set_xlabel('日期')
# axes.set_ylabel('价格')
# axes.set_xlim(0, len(df['trade_date']))
# axes.set_xticks(range(0, len(df['trade_date']), 15))
#
# time_serial = pd.to_datetime(df['trade_date'], format="%Y/%m/%d")
# axes.set_xticklabels([time_serial[x] for x in axes.get_xticks()]) # 标签设置为日期
#
# # X-轴每个ticker标签都向右倾斜45度
# for label in axes.xaxis.get_ticklabels():
# label.set_rotation(45)
# label.set_fontsize(10) # 设置标签字体
# plt.show()
self.__canvas.draw()
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 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 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 cal_plot_bband(ticker):
print("Calculating B-Bands...")
stock_data = stock.stock_preprocess_candlestick(ticker)
upper, middle, lower = talib.BBANDS(stock_data[2],
matype=talib.MA_Type.SMA)
##### plotting
fig, ax_list = plt.subplots(2, 1, figsize=(15, 15))
plt.suptitle('Bollinger Bands of {}({})'.format(
stock.check_all_ticker(ticker)[0], ticker),
fontsize=20,
fontweight='bold')
candlestick_ochl(ax_list[0],
stock_data[0],
width=0.8,
colorup='#53B987',
colordown='#EB4D5C')
ax_list[0].xaxis.set_major_locator(mdates.MonthLocator())
ax_list[0].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%b-%d'))
ax_list[0].get_xaxis().set_visible(False)
volume_overlay(ax_list[1],
*stock_data[1:4],
width=0.8,
colorup='#53B987',
colordown='#EB4D5C')
#ax_list[1].xaxis.set_major_locator(mdates.MonthLocator())
#ax_list[1].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%b-%d'))
ax_list[1].set_xticks(range(0, len(stock_data[4]), 30))
ax_list[1].set_xticklabels(stock_data[4][::30])
ax_list[0].plot(stock_data[4], upper, color='#85C0C0')
ax_list[0].plot(stock_data[4], middle, label='20-SMA', color='#AC7878')
ax_list[0].plot(stock_data[4], lower, color='#85C0C0')
ax_list[0].fill_between(stock_data[4], upper, lower, color='#F3F9F9')
for i in range(2):
ax_list[i].minorticks_on()
ax_list[i].tick_params(axis='x', which='minor', bottom='off')
ax_list[0].legend()
plt.setp(plt.gca().get_xticklabels())
fig.tight_layout()
fig.subplots_adjust(hspace=0, top=0.95)
#plt.show()
stock.save_plot('BBANDS', ticker)
def analysis_stock(twc):
year=2020
month=1
day=1
start = datetime.datetime(year, month, day)
df_2330 = pdr.DataReader('%d.TW' % twc, 'yahoo', start=start)
df_2330.index = df_2330.index.format(formatter=lambda x: x.strftime('%Y-%m-%d'))
sma_10 = talib.SMA(np.array(df_2330['Close']), 10)
sma_30 = talib.SMA(np.array(df_2330['Close']), 30)
df_2330['k'], df_2330['d'] = talib.STOCH(df_2330['High'], df_2330['Low'], df_2330['Close'])
df_2330['k'].fillna(value=0, inplace=True)
df_2330['d'].fillna(value=0, inplace=True)
fig = Figure(figsize=(24, 20))
ax = fig.add_axes([0,0.3,1,0.4])
ax2 = fig.add_axes([0,0.2,1,0.1])
ax3 = fig.add_axes([0,0,1,0.2])
ax.set_xticks(range(0, len(df_2330.index), 10))
ax.set_xticklabels(df_2330.index[::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)
ax.plot(sma_10, label='10d avg')
ax.plot(sma_30, label='30d avg')
ax2.plot(df_2330['k'], label='K value')
ax2.plot(df_2330['d'], label='D value')
ax2.set_xticks(range(0, len(df_2330.index), 10))
ax2.set_xticklabels(df_2330.index[::10])
mpf.volume_overlay(ax3, df_2330['Open'], df_2330['Close'], df_2330['Volume'], colorup='r', colordown='g', width=0.5, alpha=0.8)
ax3.set_xticks(range(0, len(df_2330.index), 10))
ax3.set_xticklabels(df_2330.index[::10])
ax.legend()
ax2.legend()
# Save it to a temporary buffer.
buf = BytesIO()
fig.savefig(buf, format="png")
# Embed the result in the html output.
data = base64.b64encode(buf.getbuffer()).decode("ascii")
return f"<img src='data:image/png;base64,{data}'/>"
def draw_volume(df_Stock):
fig = plt.figure(figsize=(24, 20))
ax3 = fig.add_axes([0, 0, 1, 0.2])
mpf.volume_overlay(ax3,
df_Stock['open'],
df_Stock['close'],
df_Stock['volume'],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax3.set_xticks(range(0, len(df_Stock.index), 1))
ax3.set_xticklabels(df_Stock.index[::])
plt.xticks(rotation=90)
plt.grid()
plt.savefig('static/media/volume.png')
def buttonClicked(self):
sid = self.ui.comboBox.currentText().split("-")[0].strip()
self.ui.figure.clf()
plt.cla()
plt.clf()
sma_20, sma_60, sma_120, sma_240, df = DataProcessing.stock_sma(sid)
#用add_axes創建副圖框
ax = self.ui.figure.add_axes([0.03, 0.4, 0.95,
0.5]) #左下角座標 (0.03,0.4)，右上角座標 (0.95,0.5)
plt.title(sid, fontsize='x-large')
ax2 = self.ui.figure.add_axes([0.03, 0.15, 0.95, 0.2
]) #左下角座標 (0.03,0.15)，右上角座標 (0.95,0.2)
mpf.candlestick2_ochl(ax,
df['Open'],
df['Close'],
df['High'],
df['Low'],
width=0.6,
colorup='r',
colordown='g',
alpha=0.75)
ax.plot(sma_20, label='20MA')
ax.plot(sma_60, label='60MA')
ax.plot(sma_120, label='120MA')
ax.plot(sma_240, label='240MA')
mpf.volume_overlay(ax2,
df['Open'],
df['Close'],
df['Volume'],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax2.set_xticks(range(0, len(df.index), 10))
ax2.set_xticklabels(df.index[::10], rotation=45)
ax.legend(loc='best', shadow=True, fontsize='x-large')
self.ui.canvas.draw()
def ohlc2cs(fname, seq_len, dataset_type, dimension):
# 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
useVolume = False
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 useVolume:
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/{}_{}/{}/{}/{}-{}_combination.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 candleplot(df, name, freq='5min'):
df = df.resample(freq).last().dropna(axis=0)
sma_5 = talib.SMA(df.close.values.astype('float'), 5)
sma_20 = talib.SMA(df.close.values.astype('float'), 20)
sma_60 = talib.SMA(df.close.values.astype('float'), 60)
fig = plt.figure(figsize=(16, 12))
ax = fig.add_axes([0, 0.2, 1, 0.5])
ax2 = fig.add_axes([0, 0, 1, 0.2])
candlestick2_ochl(ax,
df['open'].astype('float'),
df['close'].astype('float'),
df['high'].astype('float'),
df['low'].astype('float'),
width=0.5,
colorup='r',
colordown='g',
alpha=0.6)
ax.set_xticks(range(0, len(df['day']), 48))
ax.plot(sma_5, linewidth=0.5, label='MA 5')
ax.plot(sma_20, linewidth=0.5, label='MA 20')
ax.plot(sma_60, linewidth=0.5, label='MA 60')
ax.legend(loc='upper left')
plt.title(name)
ax.grid(True)
volume_overlay(ax2,
df['open'].astype('float'),
df['close'].astype('float'),
df['volume'].astype('float'),
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax2.set_xticks(range(0, len(df['day']), 48))
ax2.set_xticklabels(df['day'][::48], rotation=45)
ax2.grid(True)
plt.savefig('pics/' + name + '.png', bbox_inches='tight')
plt.show(bbox_inches='tight')
def main(offset=0, gap_size=140):
daily001 = main_session.query(models.DailyPro).filter(
models.DailyPro.ts_code == '000001.SZ').order_by(
models.DailyPro.trade_date.desc()).all()
LAST_MARKET_DATE = daily001[offset].trade_date
FIRST_MARKET_DATE = daily001[offset + gap_size].trade_date
pro = ts.pro_api()
res = pro.moneyflow_hsgt(start_date=FIRST_MARKET_DATE,
end_date=LAST_MARKET_DATE)
df = DataFrame()
for i in range(len(res))[::-1]:
df.loc[i, 'trade_date'] = res.loc[i, 'trade_date']
df.loc[i, 'north_money'] = res.loc[i, 'north_money']
df.loc[i, 'open'] = 0.5
df.loc[i, 'close'] = 1 if res.loc[i, 'north_money'] > 0 else 0.1
fig = plt.figure(figsize=(17, 10))
ax = fig.add_subplot(1, 1, 1)
step = int(gap_size / 15)
ax.set_xticks(range(0, len(df['trade_date']), step))
ax.set_xticklabels(df['trade_date'][::step])
plt.title('north_money')
mpf.volume_overlay(ax,
df['open'],
df['close'],
df['north_money'],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
plt.savefig('../../buffer/north_money/{date}_north_money.png'.format(
date=LAST_MARKET_DATE))
pass
def add_volume(ax, open, close, vol):
volume_params = dict(colorup='g', alpha=0.5, width=1)
bc = volume_overlay(ax, open, close, vol, **volume_params)
ax.add_collection(bc)
idx = np.arange(vol.size)
sma = tl.SMA(vol, 20)
sma *= 2
sma_params = dict(c='#0f07f9', ls='-', linewidth=1)
ax.plot(idx, sma, **sma_params)
ax.fill_between(idx, 0, sma, facecolor='#07f2f9', alpha=0.3)
def draw_candle(data):
#get data as a pandas type
#data = ts.get_k_data('399300', index=True, start='2013-01-01', end='2017-06-31')
print (data,type(data))
sma_10 = talib.SMA(np.array(data['close']), 10)
sma_30 = talib.SMA(np.array(data['close']), 30)
sma_60 = talib.SMA(np.array(data['close']), 60)
fig = plt.figure(figsize=(17, 10))
plt.rcParams['axes.facecolor'] = 'azure' #background color
ax = fig.add_axes([0,0.2,1,0.5])
ax2 = fig.add_axes([0,0,1,0.2])
mpf.candlestick2_ochl(ax, data['open'], data['close'], data['high'], data['low'],
width=0.5, colorup='r', colordown='g', alpha=0.6)
ax.set_xticks(range(0, len(data['date']), 10))
ax.plot(sma_10, label='10 day')
ax.plot(sma_30, label='30 day')
ax.plot(sma_60, label='60 day')
ax.legend(loc='upper left')
ax.grid(True)
mpf.volume_overlay(ax2, data['open'], data['close'], data['volume'], colorup='r', colordown='g', width=0.5, alpha=0.8)
ax2.set_xticks(range(0, len(data['date']), 10))
ax2.set_xticklabels(data['date'][::10], rotation=30)
ax2.grid(True)
plt.show()
def df_test2(number):
plt.rcParams['font.sans-serif']=['Microsoft JhengHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
plt.style.use('ggplot')
start = datetime.datetime(2020,1,1)
df_2330 = pdr.DataReader('%s.TW'%number, 'yahoo', start=start)
df_2330.index = df_2330.index.format(formatter=lambda x: x.strftime('%Y-%m-%d'))
#plt.style.use('ggplot')
fig = plt.figure(figsize=(24, 15))
#ax = fig.add_subplot(1, 1, 1)
ax = fig.add_axes([0,0.2,1,0.5])
ax2 = fig.add_axes([0,0,1,0.2])
ax.set_xticks(range(0, len(df_2330.index), 100))
ax.set_xticklabels(df_2330.index[::100])#10唯一循環
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);
sma_10 = talib.SMA(np.array(df_2330['Close']), 10)
sma_30 = talib.SMA(np.array(df_2330['Close']), 30)
plt.rcParams['font.sans-serif']=['Microsoft JhengHei']
ax.plot(sma_10, label='10日均線')
ax.plot(sma_30, label='30日均線')
mpf.volume_overlay(ax2, df_2330['Open'], df_2330['Close'], df_2330['Volume'], colorup='r', colordown='g', width=0.5, alpha=0.8)
ax2.set_xticks(range(0, len(df_2330.index), 10))
ax2.set_xticklabels(df_2330.index[::10])
ax.legend()
path_img = ".\static\image\\%s_test.jpg"%number#path_img 為 圖片路竟
print(path_img)
plt.savefig(path_img)
plt.close()
def ohlc2cs(fname, dimension):
# python preprocess.py -m ohlc2cs -l 20 -i stockdatas/EWT_testing.csv -t testing
print("Converting olhc to candlestick")
inout = fname
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)
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, df['Open'], df['Close'], df['High'],
df['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
ax2 = ax1.twinx()
# Plot the volume overlay
bc = volume_overlay(ax2, df['Open'], df['Close'], df['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 = "temp_class/{}.png".format(inout)
fig.savefig(pngfile, pad_inches=0, transparent=False)
plt.close(fig)
# normal length - end
params = []
params += ["-alpha", "off"]
subprocess.check_call(["convert", pngfile] + params + [pngfile])
print("Converting olhc to candlestik finished.")
def draw_chlcv(self, plt):
"""draw_chlcv func
Open, High, Low, Close, Volume の値を取り出し描画する
Args:
plt(module): 描画するためのモジュール
Returns:
module: 描画した情報
matplotlib.figure.Figure: 描画した情報
matplotlib.axes._subplots.AxesSubplot 描画した情報
"""
data_df = copy.deepcopy(self.data_df)
data_df = data_df.iloc[-self.graph_length:]
data_df.columns = [
"Open", "High", "Low", "Close", "Adj_Close", "Volume"
]
data_df = data_df[["Open", "High", "Low", "Close", "Volume"]]
fig = plt.figure(figsize=(18, 9))
fig.subplots_adjust(left=0.045,
bottom=0.075,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.15)
ax = plt.subplot(1, 1, 1)
candlestick2_ohlc(ax,
data_df["Open"],
data_df["High"],
data_df["Low"],
data_df["Close"],
width=0.9,
colorup="grey",
colordown="black")
place = ax.get_xticks()
place[-2] = place[-2] - 1.0
ax.set_xticks(place)
labels_length = len(ax.get_xticks())
def cal_index(x):
"""cal_index func
新たなindexを計算
Args:
x(int): もともとのindex
Returns:
float: 新たなindex
"""
return x / (labels_length - 1) * len(data_df)
def my_round(f):
"""my_round func
四捨五入関数
Args:
f(float): 四捨五入計算後の入力
Returns:
int: 四捨五入計算後の出力
"""
return int(
Decimal(str(f)).quantize(Decimal('0'), rounding=ROUND_HALF_UP))
def get_date_index(i):
"""get_date_index
index として使用する日付を取得
Args:
i(int); index
Returns:
str: 日付のindex
"""
return data_df.index[i]
new_labels = [data_df.index[0]] + \
[get_date_index(my_round(cal_index(x))) for x in range(1, labels_length - 1)] + \
[data_df.index[-1]]
ax.set_xticklabels(new_labels, rotation=30)
ax.set_xlim([-1, data_df.shape[0] + 1])
ax.set_ylabel("Price")
# 出来高を描写する範囲を指定
bottom, top = ax.get_ylim()
ax.set_ylim(bottom - (top - bottom) / 4, top)
# 出来高のチャートをプロット
ax2 = ax.twinx()
volume_overlay(ax2,
data_df["Open"],
data_df["Close"],
data_df["Volume"],
width=1,
colorup="g",
colordown="g")
ax2.set_xlim([0, data_df.shape[0]])
# 出来高チャートは下側25%に収める
ax2.set_ylim([0, float(data_df["Volume"].max()) * 4])
ax2.set_ylabel("Volume")
return plt, fig, ax
ax = fig.add_axes([0, 0.2, 1, 0.5])
ax2 = fig.add_axes([0, 0, 1, 0.2])
mpf.candlestick2_ochl(ax,
data['open'],
data['close'],
data['high'],
data['low'],
width=0.5,
colorup='r',
colordown='g',
alpha=0.6)
ax.set_xticks(range(0, len(data['date']), 10))
ax.plot(sma_10, label='SMA 10')
ax.plot(sma_30, label='SMA 30')
ax.legend(loc='upper left')
ax.grid(True)
mpf.volume_overlay(ax2,
data['open'],
data['close'],
data['volume'],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax2.set_xticks(range(0, len(data['date']), 10))
ax2.set_xticklabels(data['date'][::10], rotation=30)
ax2.grid(True)
plt.show()
def plot(self, widget):
import mpl_finance as finance
self.widget = widget
finance.volume_overlay(widget, self.open, self.close, self.volume,
self.colorup, self.colordown, self.width)
def PlotStockData(Symbol, df_data, PlotType, TechIndicators, size_factor):
fig = plt.figure()
fig.canvas.set_window_title('Wanna join the Axis Cult?')
fig.suptitle('Stock: {0} ({1} ~ {2})'.format(
Symbol,
df_data.index.min().strftime('%Y-%m-%d'),
df_data.index.max().strftime('%Y-%m-%d')),
y=1,
fontsize=14)
DefaultSize = fig.get_size_inches()
fig.set_size_inches(
(DefaultSize[0] * size_factor, DefaultSize[1] * size_factor))
row_size = 3
row_index = [0]
for indicator in TechIndicators:
IndicatorName = indicator[strName]
if TechIndicatorPlotMode[IndicatorName][strPlotInAxPrice] == False:
row_size += 1
ax_price = plt.subplot2grid((row_size, 1), (0, 0), rowspan=2)
row_index[0] = 2
ax_volume = plt.subplot2grid((row_size, 1), (row_size - 1, 0),
sharex=ax_price)
# for using mpl_finance
df_data = df_data.reset_index()
df_data.columns = [strDate, strOpen, strHigh, strLow, strClose, strVolume]
df_data[strDate] = df_data[strDate].map(mdates.date2num)
DateSize = len(df_data[strDate])
date_indices = numpy.arange(DateSize)
df_data_Date = df_data[strDate]
df_data[strDate] = date_indices
def format_date(x, pos):
this_index = numpy.clip(int(x + 0.5), 0, DateSize - 1)
return mdates.num2date(df_data_Date[this_index]).strftime('%m/%d/%y')
if PlotType == strCandle:
lines, patches = mpl_finance.candlestick_ohlc(ax_price,
df_data.values,
width=1,
colorup='r',
colordown='g')
for line, patch in zip(lines, patches):
patch.set_edgecolor("k")
patch.set_linewidth(0.72)
patch.set_antialiased(False)
line.set_color("k")
line.set_zorder(0) # make lines appear behind the patches
line.set_visible(True) # make them invisible
elif PlotType == strBasic:
df_data[strClose].plot(ax=ax_price, kind='line', color='g')
mpl_finance.volume_overlay(ax_volume,
df_data[strOpen],
df_data[strClose],
df_data[strVolume],
colorup='r',
colordown='g',
width=1)
def GetMondayList(df_data_Date):
MondayList = []
for index, value in enumerate(df_data_Date):
if mdates.num2date(value).weekday() == 0:
MondayList.append(index)
return MondayList
ax_volume.xaxis.set_major_locator(
ticker.FixedLocator(GetMondayList(df_data_Date)))
ax_volume.xaxis.set_minor_locator(ticker.MultipleLocator(1))
ax_volume.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
plt.xlabel("Date")
ax_price.set_ylabel("Price")
ax_volume.set_ylabel('Volume')
for label in ax_volume.xaxis.get_ticklabels():
label.set_rotation(45)
for indicator in TechIndicators:
TechIndicatorName = indicator[strName]
TechIndicatorFuncDict[TechIndicatorName][strFuncName](
indicator[strParam], df_data, ax_price, row_size, row_index)
plt.setp(ax_price.get_xticklabels(), visible=False)
ax_price.grid(color='grey', linestyle='--', linewidth=0.5)
ax_volume.grid(color='grey', linestyle='--', linewidth=0.5)
plt.tight_layout()
return fig
def plotCandlestickChart(StockID, ShowDays):
l = crawlerStockServes_TW.get_data(StockID)
col = ["Date", "Name", "Open", "High", "Low", "Close", "Volume", "PER"]
df = pd.DataFrame(l, columns=col)
df.set_index("Date", inplace=True)
print(df[-20:])
inputs = {
'open': df['Open'].values,
'high': df['High'].values,
'low': df['Low'].values,
'close': df['Close'].values,
'volume': df['Volume'].values
}
df = df[ShowDays:]
#df.index = df.index.format(formatter=lambda x: x.strftime('%Y-%m-%d'))
sma_10 = talib.SMA(inputs["close"], timeperiod=10)
sma_20 = talib.SMA(inputs["close"], timeperiod=20)
upper, middle, lower = talib.BBANDS(inputs["close"], 20, 2, 2)
sma_10 = sma_10[ShowDays:]
sma_20 = sma_20[ShowDays:]
upper = upper[ShowDays:]
lower = lower[ShowDays:]
fig = plt.figure(figsize=(16, 10))
ax = fig.add_axes([0.05, 0.4, 0.9, 0.5])
ax2 = fig.add_axes([0.05, 0.2, 0.9, 0.2])
ax.set_xticks(range(0, len(df.index), 10))
ax.set_xticklabels(df.index[::10])
mpf.candlestick2_ochl(ax,
df['Open'],
df['Close'],
df['High'],
df['Low'],
width=0.6,
colorup='r',
colordown='g',
alpha=0.75)
#plt.rcParams['font.sans-serif']=['Microsoft JhengHei']
ax.plot(sma_10, label='10MA')
ax.plot(sma_20, label='20MA')
ax.plot(upper, label='BBU')
ax.plot(lower, label='BBL')
ax.grid(True, which='both')
ax.legend()
mpf.volume_overlay(ax2,
df['Open'],
df['Close'],
df['Volume'],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax2.set_xticks(range(0, len(df.index), 10))
ax2.set_xticklabels(df.index[::10])
ax2.grid(True, which='both')
return plt
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()
ax1.plot(Select['sma_short'], label='MA short')
ax1.plot(Select['sma_long'], label='MA long')
ax1.legend()
ax1.autoscale()
ax1.set_ylim((Select['Close'].min() * 0.9, Select['Close'].max() * 1.1))
# b.) KD line chart.
ax2.plot(Select['K'], label='K')
ax2.plot(Select['D'], label='D')
ax2.legend()
# c.) Volume bar chart.
mpf.volume_overlay(ax3,
Select['Open'],
Select['Close'],
Select['Volume %s' % GD.SYMBOL],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax3.set_xticks(range(0, len(Select.index), 20))
ax3.set_xticklabels(Select.index[::20])
fig.savefig("./fig/%s_charts.png" % GD.SYMBOL)
plt.clf()
plt.close()
print(data_train.shape)
print(data_test.shape)
print(train_norm.shape)
print(test_norm.shape)
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()
ax = fig.add_axes([0, 0.2, 1, 0.5])
ax2 = fig.add_axes([0, 0, 1, 0.2])
ax.set_xticks(range(0, len(df_1109.index), 10))
ax.set_xticklabels(df_1109.index[::10])
mpf.candlestick2_ochl(ax,
df_1109['Open'],
df_1109['Close'],
df_1109['High'],
df_1109['Low'],
width=0.6,
colorup='r',
colordown='g',
alpha=0.75)
plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']
ax.plot(sma_10, label='10日均線')
ax.plot(sma_30, label='30日均線')
mpf.volume_overlay(ax2,
df_1109['Open'],
df_1109['Close'],
df_1109['Volume'],
colorup='r',
colordown='g',
width=0.5,
alpha=0.8)
ax2.set_xticks(range(0, len(df_1109.index), 10))
ax2.set_xticklabels(df_1109.index[::10])
ax.legend()
def draw_klines(df: pd.DataFrame,
main_chart_lines=None,
sub_chart_lines=None,
*,
annotate=False,
to_file=None):
"""
基础画图方法
:param df: dataframe->基础需包含['datetime', 'open', 'close', 'high', 'low', 'volume']，
可选trades: List[Dict]，若有则会添加交易标识Dict中包含字段['price', 'size', 'direction']
:param extra_lines: 添加想要的线
:param to_file: 生成图片，默认None为不生成
:return:
"""
import matplotlib.pyplot as plt
import mpl_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.values.T
xdate = data['datetime'].tolist()
def mydate(x, pos):
try:
return xdate[int(x)]
except IndexError:
return ''
data_len = len(data_mat[2])
fig, [ax1, ax2, ax3] = plt.subplots(3, 1, sharex=True)
fig.set_figheight(300 / 72)
fig.set_figwidth(1200 / 72)
ax1.set_position([0.1, 0.4, 0.8, 0.55])
ax2.set_position([0.1, 0.2, 0.8, 0.15])
ax3.set_position([0.1, 0.05, 0.8, 0.1])
ax1.set_title('KLine', fontsize='large', fontweight='bold')
ax2.set_title('MACD')
ax3.set_title('Volume')
mpf.candlestick2_ochl(ax1,
data_mat[1],
data_mat[2],
data_mat[3],
data_mat[4],
colordown=DEFALUT_CHART_COLOR['barDown'],
colorup=DEFALUT_CHART_COLOR['barUp'],
width=min(120 / data_len, 1),
alpha=1)
mpf.volume_overlay(ax3,
data_mat[1],
data_mat[2],
data_mat[5],
colordown=DEFALUT_CHART_COLOR['barDown'],
colorup=DEFALUT_CHART_COLOR['barUp'],
width=min(120 / data_len, 1),
alpha=1)
ax1.grid(True)
ax2.grid(True)
ax3.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))
if main_chart_lines:
for l in main_chart_lines:
ax1.add_line(l)
else:
import talib
l = range(data_len)
for p in [5, 10, 30, 60]:
ma_line = mpf.Line2D(l,
talib.MA(data_mat[2].astype(float), p),
color=DEFALUT_CHART_COLOR[f'ma{p}'],
linewidth=120 / data_len)
ax1.add_line(ma_line)
if sub_chart_lines:
for l in main_chart_lines:
ax2.add_line(l)
else:
import talib
dif, dea, macd = talib.MACDEXT(data_mat[2].astype(float),
fastperiod=12,
fastmatype=1,
slowperiod=26,
slowmatype=1,
signalperiod=9,
signalmatype=1)
macd = macd * 2
l = range(data_len)
dif_line = mpf.Line2D(l,
dif,
color=DEFALUT_CHART_COLOR['dif'],
linewidth=120 / data_len)
dea_line = mpf.Line2D(l,
dea,
color=DEFALUT_CHART_COLOR['dea'],
linewidth=120 / data_len)
ax2.add_line(dif_line)
ax2.add_line(dea_line)
mpf.candlestick2_ochl(ax2, [0] * len(macd),
macd,
np.where(macd >= 0, macd, 0),
np.where(macd < 0, macd, 0),
colordown=DEFALUT_CHART_COLOR['barDown'],
colorup=DEFALUT_CHART_COLOR['barUp'],
width=120 / data_len,
alpha=0.7)
if 'trades' in df.columns:
trades_long_x = []
trades_long_y = []
trades_long_s = []
trades_short_x = []
trades_short_y = []
trades_short_s = []
size_ratio = 100 * 120 / data_len
for i, (_, tl) in enumerate(df.trades.iteritems()):
if isinstance(tl, Iterable):
long_n = 0
short_n = 0
total_long = 0
total_short = 0
for t in tl:
if t['direction'] == 'long':
trades_long_x.append(i)
trades_long_y.append(t['price'])
trades_long_s.append(t['size'] * size_ratio)
long_n += t['size']
total_long += t['price']
elif t['direction'] == 'short':
trades_short_x.append(i)
trades_short_y.append(t['price'])
trades_short_s.append(t['size'] * size_ratio)
short_n += t['size']
total_short += t['price']
else:
if annotate and long_n:
avg_long_price = total_long / long_n
ax1.annotate(f'+{long_n}@{avg_long_price:.1f}',
xy=(i, avg_long_price),
xytext=(i + 0.1, avg_long_price - 0.1))
if annotate and short_n:
avg_short_price = total_short / short_n
ax1.annotate(f'-{short_n}@{avg_short_price:.1f}',
xy=(i, avg_short_price),
xytext=(i + 0.1, avg_short_price + 0.1))
else:
ax1.scatter(trades_long_x,
trades_long_y,
s=trades_long_s,
color=DEFALUT_CHART_COLOR['longMark'],
marker='^',
alpha=0.8,
linewidths=0,
zorder=2)
ax1.scatter(trades_short_x,
trades_short_y,
s=trades_short_s,
color='',
edgecolors=DEFALUT_CHART_COLOR['shortMark'],
marker='v',
alpha=0.8,
linewidths=1,
zorder=2)
if to_file:
try:
fig.savefig(to_file,
dpi=DEFALUT_CHART_PARAMS['dpi'],
figsize=(120, 30))
except Exception as e:
print('errSaveFig:', e)
return fig
df['Low'],
width=0.6,
alpha=0.75,
colorup='r',
colordown='g')
ax.set_xticks(range(0, len(df.index), 100))
ax.set_xticklabels(df.index[::100])
ax.set_xlim(0, len(df.index))
ax.axes.xaxis.set_ticks([])
ax.plot(sma20.index, sma20, label='ma20')
ax.set_title('stock')
ax.set_ylabel('price')
ax.legend(loc=2)
# 量圖---------------------------------------------------------------------
mpf.volume_overlay(ax2,
df['Open'],
df['Close'],
df['Volume'],
width=0.6,
colorup='r',
colordown='g')
ax2.set_xticks(range(0, len(df.index), 100))
ax2.set_xticklabels(df.index[::100])
ax2.set_xlim(0, len(df.index))
#ax2.set_yticks(range(0, max(df['Volume']), max(df['Volume'])/4))
def test_ohlc2cs(df, seq_len, dimension, add_v, stock_name):
# python preprocess.py -m ohlc2cs -l 20 -i stockdatas/EWT_testing.csv -t testing
print("Converting test olhc to candlestick")
df = df
plt.style.use('dark_background')
df.reset_index(inplace=True)
figs = np.zeros((1, dimension, dimension, 3))
labels = []
# ohlc+volume
c = df.loc[len(df) - int(seq_len):len(df) - 1, :]
if len(c) == int(seq_len):
my_dpi = 100
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=0.5,
colorup='#db3f3f',
colordown='#77d879')
ax1.grid(False)
ax1.set_xticklabels([])
ax1.set_yticklabels([])
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)
ax1.axis('off')
if add_v == 1:
ax2 = fig.add_subplot(2, 1, 1)
volume_overlay(ax2,
opens=c['Open'],
closes=c['Close'],
volumes=c['Volume'],
colorup='#db3f3f',
colordown='#77d879',
width=0.5)
ax2.grid(False)
ax2.set_xticklabels([])
ax2.set_yticklabels([])
ax2.xaxis.set_visible(False)
ax2.yaxis.set_visible(False)
ax2.axis('off')
# create the second axis for the volume bar-plot
# Add a seconds axis for the volume overlay
fig.canvas.draw()
fig_np = np.array(fig.canvas.renderer._renderer)
figs[0] = fig_np[:, :, :3]
fig.savefig(stock_name + '_sample.png')
plt.close(fig)
# normal length - end
print("Converting olhc to candlestik finished.")
return figs
def ohlc2cs(df, seq_len, dimension, add_v):
# python preprocess.py -m ohlc2cs -l 20 -i stockdatas/EWT_testing.csv -t testing
print("Converting olhc to candlestick")
df = df
plt.style.use('dark_background')
df.reset_index(inplace=True)
figs = np.zeros((len(df) - 1, dimension, dimension, 3))
labels = []
for i in range(0, len(df) - 1):
# ohlc+volume
c = df.loc[i:i + int(seq_len) - 1, :]
c_ = df.loc[i:i + int(seq_len), :]
if len(c) == int(seq_len):
my_dpi = 100
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=0.5,
colorup='#db3f3f',
colordown='#77d879')
ax1.grid(False)
ax1.set_xticklabels([])
ax1.set_yticklabels([])
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)
ax1.axis('off')
if add_v == 1:
ax2 = fig.add_subplot(2, 1, 1)
volume_overlay(ax2,
opens=c['Open'],
closes=c['Close'],
volumes=c['Volume'],
colorup='#db3f3f',
colordown='#77d879',
width=0.5)
ax2.grid(False)
ax2.set_xticklabels([])
ax2.set_yticklabels([])
ax2.xaxis.set_visible(False)
ax2.yaxis.set_visible(False)
ax2.axis('off')
# create the second axis for the volume bar-plot
# Add a seconds axis for the volume overlay
starting = c_["Close"].iloc[-2]
endvalue = c_["Close"].iloc[-1]
if endvalue > starting:
label = 1
else:
label = 0
labels.append(label)
fig.canvas.draw()
fig_np = np.array(fig.canvas.renderer._renderer)
figs[i] = fig_np[:, :, :3]
#fig.savefig('./sample/sample_'+str(i)+'.png')
plt.close(fig)
# normal length - end
print("Converting olhc to candlestik finished.")
return figs, labels
def plot(self, widget):
import mpl_finance as finance
self.widget = widget
finance.volume_overlay(widget, self.open, self.close, self.volume,
self.colorup, self.colordown, self.width)
width=0.5,
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=0.5,
alpha=0.8,
colordown='g',
colorup='r')
ax2.set_xticks(range(0, len(df), 1))
# ax.set_xticklabels(df['date'][::5])
ax2.set_xticklabels(df['datetime'][::1])
plt.setp(ax2.get_xticklabels(), rotation=30, horizontalalignment='right')
ax2.grid(True)
# plt.subplots_adjust(hspace=0)
if save:
# path = os.path.join(os.path.dirname(__file__),'data',today)
fig.savefig(title + '.png')
else:
plt.show()
plt.close()
