def setup_plot(self, plot_title, kbars):
import finplot as fplt
kbars = kbars.reset_index().rename(columns={'ts': 'time'})
# adopt TWSE style
fplt.display_timezone = gettz(self._exchange.brokerage.TIMEZONE)
fplt.candle_bull_color = '#ef5350'
fplt.candle_bull_body_color = fplt.candle_bull_color
fplt.candle_bear_color = '#26a69a'
fplt.volume_bull_color = '#f7a9a7'
fplt.volume_bull_body_color = fplt.volume_bull_color
fplt.volume_bear_color = '#92d2cc'
main_ax, rsi_ax, macd_ax = fplt.create_plot(plot_title, rows=3)
fplt.candlestick_ochl(kbars[['time', 'open', 'close', 'high', 'low']],
ax=main_ax)
fplt.volume_ocv(kbars[['time', 'open', 'close', 'volume']],
ax=main_ax.overlay())
fplt.plot(kbars['time'], kbars['rsi'], ax=rsi_ax, legend='RSI')
fplt.set_y_range(0, 100, ax=rsi_ax)
fplt.add_band(self.MIN_RSI, self.MAX_RSI, ax=rsi_ax)
fplt.volume_ocv(kbars[['time', 'open', 'close', 'macd_hist']],
ax=macd_ax,
colorfunc=fplt.strength_colorfilter)
fplt.plot(kbars['time'], kbars['macd'], ax=macd_ax, legend='MACD')
fplt.plot(kbars['time'],
kbars['macd_signal'],
ax=macd_ax,
legend='Signal')
return fplt
def show_day_plot():
#self.fecha = instance.text
#print (self.fecha)
temp = lol
#temp = pd.date_range(start=temp['time'][self.fechas[0]], end=temp['time'][self.fechas[1]], freq='D')
#print (type(self.fechas[0]))
#print (self.fechas[0],self.fechas[1])
datex = fechas[3]
desde = fechas[1] + timedelta(hours=5)
hasta = desde + timedelta(hours=24)
temp = lol[(lol.time > desde) & (lol.time <= hasta)]
print(temp)
print('mierdaaa')
#print (emp['time'][self.fecha])
#temp = self.get_frac_df(temp,)
ax, ax2, ax3 = fplt.create_plot('NASDAQ', rows=3)
candle_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
fplt.plot(lol['time'],
lol['close'].rolling(25).mean(),
ax=ax,
color='#0000ff',
legend='ma-25')
#subprocess.Popen(['python','ploter.py'])
#df['rnd'] = np.random.normal(size=len(df))
#fplt.plot(df['time'], df['rnd'], ax=ax2, color='#992277', legend='stuff')
#fplt.set_y_range(ax2, -4.4, +4.4) # fix y-axis range
# finally a volume bar chart in our third plot
volume_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'volume']])
fplt.volume_ocv(volume_src, ax=ax3)
fplt.show()
def update():
# if delta_bar.df[-1]['open'] != 0:
df = delta_bar.df
# Меняем индекс и делаем его типом datetime
df = df.set_index(
pd.to_datetime(df['date_time'], format='%Y-%m-%d %H:%M:%S'))
df = df.drop(
'date_time', 1
) # Удаляем колонку с датой и временем, т.к. дата у нас теперь в индексе
print(df)
# pick columns for our three data sources: candlesticks and TD
candlesticks = df['open close high low'.split()]
deltabarsec = df['open close delta_time_sec'.split()]
delta = df['open close delta'.split()]
maxvolcluster = df['max_vol_cluster']
if not plots:
# first time we create the plots
global ax
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(deltabarsec, ax=ax2))
plots.append(fplt.volume_ocv(delta, ax=ax3))
plots.append(fplt.plot(maxvolcluster, style='o', color='#00f'))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(deltabarsec)
plots[2].update_data(delta)
plots[3].update_data(maxvolcluster)
def __init__(self, platform, symbol, time_frame):
self.__platform = platform
self.__symbol = symbol
self.__time_frame = time_frame
self.__market = MARKET(self.__platform, self.__symbol,
self.__time_frame)
# pull some data
self.__indicators = INDICATORS(self.__platform, self.__symbol,
self.__time_frame)
self.__kline = platform.get_kline(self.__time_frame)
self.__kline.reverse()
# format it in pandas
try: # dataframe有7列的情况
self.__df = pd.DataFrame(self.__kline,
columns=[
'time', 'open', 'high', 'low',
'close', 'volume', 'currency_volume'
])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except: # dataframe只有6列的情况，如okex的现货k线数据
self.__df = pd.DataFrame(
self.__kline,
columns=['time', 'open', 'high', 'low', 'close', 'volume'])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64'
})
# create three plot 创建三层图纸，第一层画k线，第二层画成交量，第三层画一些适宜于副图显示的指标
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
self.__ax, self.__ax2, self.__ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candles = self.__df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=self.__ax)
# overlay volume on the plot
volumes = self.__df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=self.__ax2)
fplt.add_legend("VOLUME", self.__ax2) # 增加"VOLUME"图例
def plot_kline(kline):
"""
回测结束时绘制k线图
:param kline: 回测时传入指定的k线数据
:return:
"""
kline = kline
# kline.reverse()
# format it in pandas
try: # dataframe有7列的情况
df = pd.DataFrame(kline,
columns=[
'time', 'open', 'high', 'low', 'close', 'volume',
'currency_volume'
])
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except: # dataframe只有6列的情况，如okex的现货k线数据
df = pd.DataFrame(
kline, columns=['time', 'open', 'high', 'low', 'close', 'volume'])
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64'
})
# create three plot 创建三层图纸，第一层画k线，第二层画成交量，第三层画一些适宜于副图显示的指标
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
ax, ax2 = fplt.create_plot("KLINE", rows=2)
# plot candle sticks
candles = df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=ax)
# overlay volume on the plot
volumes = df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=ax2)
fplt.add_legend("VOLUME", ax2) # 增加"VOLUME"图例
fplt.show()
def __init__(self, pipe, init_data, init_sym, tech_indicators, fps=10):
super().__init__()
self.plots = []
self.tech_indicators = tech_indicators
self.data, self.watched_sym = init_data, init_sym
self.ax, self.ax2 = fplt.create_plot('Live Stock Trader',
init_zoom_periods=500,
maximize=False,
rows=2)
self.live_data = None
self.update_data()
self.fps = fps
self.plots = [
fplt.candlestick_ochl(self.live_data[0], ax=self.ax),
fplt.volume_ocv(self.live_data[1], ax=self.ax.overlay()),
fplt.plot(self.live_data[2], ax=self.ax, legend='VWAP')
]
self.plots.extend([
fplt.plot(ti_d, ax=self.ax2, legend=t)
for t, ti_d in zip(tech_indicators, self.live_data[3:])
])
fplt.timer_callback(
self.step,
1 / fps) # update (using synchronous rest call) every N seconds
self.pipe = pipe
def update():
# load data
limit = 500
start = int(time.time()*1000) - (500-2)*60*1000
url = 'https://api.bitfinex.com/v2/candles/trade:1m:tBTCUSD/hist?limit=%i&sort=1&start=%i' % (limit, start)
table = requests.get(url).json()
df = pd.DataFrame(table, columns='time open close high low volume'.split())
# calculate indicator
tdup,tddn = td_sequential(df['close'])
df['tdup'] = [('%i'%i if 0<i<10 else '') for i in tdup]
df['tddn'] = [('%i'%i if 0<i<10 else '') for i in tddn]
# pick columns for our three data sources: candlesticks and TD sequencial labels for up/down
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
td_up_labels = df['time high tdup'.split()]
td_dn_labels = df['time low tddn'.split()]
if not plots:
# first time we create the plots
global ax
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(volumes, ax=ax.overlay()))
plots.append(fplt.labels(td_up_labels, color='#009900'))
plots.append(fplt.labels(td_dn_labels, color='#990000', anchor=(0.5,0)))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(td_up_labels)
plots[3].update_data(td_dn_labels)
def drawplot():
url = 'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=%s&interval=%s&apikey=%s&datatype=csv&outputsize=%s'%(sym, inter, APIKEY, ops)
res = rq.get(url)
with open(sym+'temp.csv', 'wb') as f:
for chunk in res.iter_content():
f.write(chunk)
try:
data = pd.read_csv(sym+'temp.csv', parse_dates=['timestamp'], index_col='timestamp').sort_index()
except:
return # if API does not respond with valid data.
update_csv(sym, data)
candles = data[['open','close','high','low']]
volumes = data[['open','close','volume']]
if len(plots) == 0:
plots.append(fplt.volume_ocv(volumes, ax=ax2))
plots.append(fplt.candlestick_ochl(candles, ax=ax))
else:
plots[0].update_data(volumes)
plots[1].update_data(candles)
def update(txt):
df = download(txt)
if len(df) < 20: # symbol does not exist
return
info.setText('Loading symbol name...')
price = df['Open Close High Low'.split()]
ma20 = df.Close.rolling(20).mean()
ma50 = df.Close.rolling(50).mean()
volume = df['Open Close Volume'.split()]
ax.reset() # remove previous plots
axo.reset() # remove previous plots
fplt.candlestick_ochl(price)
fplt.plot(ma20, legend='MA-20')
fplt.plot(ma50, legend='MA-50')
fplt.volume_ocv(volume, ax=axo)
fplt.refresh() # refresh autoscaling when all plots complete
Thread(target=lambda: info.setText(get_name(txt))).start(
) # slow, so use thread
def plot_price_in_range(df, name, start, end):
new_df = get_time_period(df, start, end)
ax = fplt.create_plot(name, rows=1)
# plot candle stick
candles = new_df[['Open', 'Close', 'High', 'Low']]
fplt.candlestick_ochl(candles, ax=ax)
# moving averages
fplt.plot(new_df.Close.rolling(50).mean(), legend='ma50')
fplt.plot(new_df.Close.rolling(100).mean(), legend='ma100')
fplt.plot(new_df.Close.rolling(150).mean(), legend='ma150')
fplt.plot(new_df.Close.rolling(200).mean(), legend='ma200')
# overlay volume on the top plot
volumes = new_df[['Open', 'Close', 'Volume']]
fplt.volume_ocv(volumes, ax=ax.overlay())
fplt.show()
def drawCandleStickChart(timestamp, openVal, high, low, closeVal, volume,
exchange, symbol):
# create two plots
ax = fplt.create_plot(exchange + '-' + symbol, rows=1)
# plot candle sticks
candles = [timestamp, openVal, closeVal, high, low]
fplt.candlestick_ochl(candles, ax=ax)
# # put an MA on the close price
# fplt.plot(timestamp, closeVal.rolling(25).mean(), ax=ax, legend='ma-25')
# overlay volume on the top plot
volumes = [timestamp, openVal, closeVal, volume]
fplt.volume_ocv(volumes, ax=ax.overlay())
# restore view (X-position and zoom) if we ever run this example again
fplt.autoviewrestore()
# we're done
fplt.show()
def change_asset(*args, **kwargs):
'''Resets and recalculates everything, and plots for the first time.'''
# save window zoom position before resetting
fplt._savewindata(fplt.windows[0])
symbol = ctrl_panel.symbol.currentText()
interval = ctrl_panel.interval.currentText()
ws.df = None
df = load_price_history(symbol, interval=interval)
ws.reconnect(symbol, interval, df)
# remove any previous plots
ax.reset()
axo.reset()
ax_rsi.reset()
# calculate plot data
indicators = ctrl_panel.indicators.currentText().lower()
data,price_data = calc_plot_data(df, indicators)
# some space for legend
ctrl_panel.move(100 if 'clean' in indicators else 200, 0)
# plot data
global plots
plots = {}
plots['price'] = fplt.candlestick_ochl(data['price'], ax=ax)
plots['volume'] = fplt.volume_ocv(data['volume'], ax=axo)
if data['ma50'] is not None:
plots['ma50'] = fplt.plot(data['ma50'], legend='MA-50', ax=ax)
plots['ma200'] = fplt.plot(data['ma200'], legend='MA-200', ax=ax)
plots['vema24'] = fplt.plot(data['vema24'], color=4, legend='V-EMA-24', ax=axo)
if data['rsi'] is not None:
ax.set_visible(xaxis=False)
ax_rsi.show()
fplt.set_y_range(0, 100, ax=ax_rsi)
fplt.add_band(30, 70, color='#6335', ax=ax_rsi)
plots['sar'] = fplt.plot(data['sar'], color='#55a', style='+', width=0.6, legend='SAR', ax=ax)
plots['rsi'] = fplt.plot(data['rsi'], legend='RSI', ax=ax_rsi)
plots['stoch'] = fplt.plot(data['stoch'], color='#880', legend='Stoch', ax=ax_rsi)
plots['stoch_s'] = fplt.plot(data['stoch_s'], color='#650', ax=ax_rsi)
else:
ax.set_visible(xaxis=True)
ax_rsi.hide()
# price line
ax.price_line = pg.InfiniteLine(angle=0, movable=False, pen=fplt._makepen(fplt.candle_bull_body_color, style='.'))
ax.price_line.setPos(price_data['last_close'])
ax.price_line.pen.setColor(pg.mkColor(price_data['last_col']))
ax.addItem(ax.price_line, ignoreBounds=True)
# restores saved zoom position, if in range
fplt.refresh()
def update():
df = delta_bars.df
# Меняем индекс и делаем его типом datetime
df = df.set_index(
pd.to_datetime(df['date_time'], format='%Y-%m-%d %H:%M:%S'))
df = df.drop(
'date_time', 1
) # Удаляем колонку с датой и временем, т.к. дата у нас теперь в индексе
play_sound(df.tail(2)) # Вызов функции звукового сигнала
print(df)
# pick columns for our three data sources: candlesticks and TD
candlesticks = df['open close high low'.split()]
deltabarsec = df['open close sec'.split()]
delta = df['open close delta'.split()]
maxvolcluster = df['max_vol']
if not plots:
# first time we create the plots
global ax
plots.append(fplt.candlestick_ochl(candlesticks, candle_width=0.8))
plots.append(fplt.volume_ocv(deltabarsec, ax=ax2))
fplt.add_legend('Время формирования бара', ax=ax2)
plots.append(
fplt.volume_ocv(delta, colorfunc=fplt.strength_colorfilter,
ax=ax3))
fplt.add_legend('Дельта', ax=ax3)
plots.append(
fplt.plot(maxvolcluster,
legend='Max volume',
style='o',
color='#00f'))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(deltabarsec)
plots[2].update_data(delta)
plots[3].update_data(maxvolcluster)
def show_day():
#lol = df
temp = lol
#temp = self.get_frac_df(temp,)
ax, ax2, ax3 = fplt.create_plot('NASDAQ', rows=3)
candle_src = fplt.PandasDataSource(
lol[['time', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
fplt.plot(lol['time'],
lol['close'].rolling(25).mean(),
ax=ax,
color='#0000ff',
legend='ma-25')
#df['rnd'] = np.random.normal(size=len(df))
#fplt.plot(df['time'], df['rnd'], ax=ax2, color='#992277', legend='stuff')
#fplt.set_y_range(ax2, -4.4, +4.4) # fix y-axis range
# finally a volume bar chart in our third plot
volume_src = fplt.PandasDataSource(lol[['time', 'open', 'close',
'volume']])
fplt.volume_ocv(volume_src, ax=ax3)
# we're done
fplt.show()
def plot(txt):
# Get data
df = download(txt)
if len(df) < 20: # symbol does not exist
return
info.setText('Loading symbol name...')
# Cleanup
ax.reset() # remove previous plots
axo.reset() # remove previous plots
# Process result
price = df['Open Close High Low'.split()]
volume = df['Open Close Volume'.split()]
# Indicators
emaShort = df.Close.ewm(span=10,
min_periods=0,
adjust=False,
ignore_na=False).mean()
emaMedium = df.Close.ewm(span=20,
min_periods=0,
adjust=False,
ignore_na=False).mean()
emaLong = df.Close.ewm(span=50,
min_periods=0,
adjust=False,
ignore_na=False).mean()
# Plot data
fplt.candlestick_ochl(price)
fplt.plot(emaShort, legend='EMA-10', color='#00FFFF', width=2)
fplt.plot(emaMedium, legend='EMA-20', width=2)
fplt.plot(emaLong, legend='EMA-50', color='#FF0000', width=2)
fplt.volume_ocv(volume, ax=axo)
fplt.refresh() # refresh autoscaling when all plots complete
Thread(target=lambda: info.setText(get_name(txt))).start(
) # slow, so use thread
def update(self):
table = self.exchange.get_kline(self.timeframe)
table.reverse()
try:
df = pd.DataFrame(
table,
columns='time open high low close volume currency_volume'.
split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except:
df = pd.DataFrame(
table, columns='time open high low close volume'.split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64'
})
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
result = self.indicators.MACD(12, 26, 9)
dif = result['DIF']
dea = result['DEA']
macd = result['MACD']
if not plots:
# first time we create the plots
global ax, ax2, ax3
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(volumes, ax=ax2))
plots.append(fplt.plot(dif, ax=ax3, legend="DIF"))
plots.append(fplt.plot(dea, ax=ax3, legend="DEA"))
plots.append(fplt.plot(macd, ax=ax3, legend="MACD"))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(dif)
plots[3].update_data(dea)
plots[4].update_data(macd)
def update(self):
table = self.exchange.get_kline(self.timeframe)
table.reverse()
try:
df = pd.DataFrame(
table,
columns='time open high low close volume currency_volume'.
split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except:
df = pd.DataFrame(
table, columns='time open high low close volume'.split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64'
})
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
boll = self.indicators.BOLL(20)
upperband = boll['upperband']
middleband = boll['middleband']
lowerband = boll['lowerband']
if not plots:
# first time we create the plots
global ax, ax2
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(volumes, ax=ax2))
plots.append(fplt.plot(upperband, legend="UPPERBAND"))
plots.append(fplt.plot(middleband, legend="MIDDLEBAND"))
plots.append(fplt.plot(lowerband, legend="LOWERBAND"))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(upperband)
plots[3].update_data(middleband)
plots[4].update_data(lowerband)
def update(self):
table = self.exchange.get_kline(self.timeframe)
table.reverse()
try:
df = pd.DataFrame(
table,
columns='time open high low close volume currency_volume'.
split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except:
df = pd.DataFrame(
table, columns='time open high low close volume'.split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64'
})
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
ma = self.indicators.MA(20, 30)
ma1 = ma[0]
ma2 = ma[1]
if not plots:
# first time we create the plots
global ax, ax2
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(volumes, ax=ax2))
plots.append(fplt.plot(ma1, legend="MA20"))
plots.append(fplt.plot(ma2, legend="MA30"))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(ma1)
plots[3].update_data(ma2)
def update(txt):
df = download(txt)
if len(df) < 20: # symbol does not exist
return
info.setText('Loading symbol name...')
price = df['Open Close High Low'.split()]
ma20 = df.Close.rolling(20).mean()
ma50 = df.Close.rolling(50).mean()
volume = df['Open Close Volume'.split()]
if not plots:
plots.append(fplt.candlestick_ochl(price))
plots.append(fplt.plot(ma20, legend='MA-20'))
plots.append(fplt.plot(ma50, legend='MA-50'))
plots.append(fplt.volume_ocv(volume, ax=ax.overlay()))
else:
plots[0].update_data(price)
plots[1].update_data(ma20)
plots[2].update_data(ma50)
plots[3].update_data(volume)
Thread(target=lambda: info.setText(get_name(txt))).start(
) # slow, so use thread
def update():
# load data
limit = 500
start = int(time.time() * 1000) - (500 - 2) * 60 * 1000
url = 'https://api.bitfinex.com/v2/candles/trade:1m:tBTCUSD/hist?limit=%i&sort=1&start=%i' % (
limit, start)
table = requests.get(url).json()
df = pd.DataFrame(table, columns='time open close high low volume'.split())
# pick columns for our three data sources: candlesticks and TD sequencial labels for up/down
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
if not plots:
# first time we create the plots
global ax
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(volumes, ax=ax.overlay()))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
def plot(df, x, y, kind, **kwargs):
_x = df.index if y is None else df[x]
try:
_y = df[x].reset_index(drop=True) if y is None else df[y]
except:
_y = df.reset_index(drop=True)
kwargs = dict(kwargs)
if 'by' in kwargs:
del kwargs['by']
if kind in ('candle', 'candle_ochl', 'candlestick', 'candlestick_ochl',
'volume', 'volume_ocv', 'renko'):
if 'candle' in kind:
return finplot.candlestick_ochl(df, **kwargs)
elif 'volume' in kind:
return finplot.volume_ocv(df, **kwargs)
elif 'renko' in kind:
return finplot.renko(df, **kwargs)
elif kind == 'scatter':
if 'style' not in kwargs:
kwargs['style'] = 'o'
return finplot.plot(_x, _y, **kwargs)
elif kind == 'bar':
return finplot.bar(_x, _y, **kwargs)
elif kind in ('barh', 'horiz_time_volume'):
return finplot.horiz_time_volume(df, **kwargs)
elif kind in ('heatmap'):
return finplot.heatmap(df, **kwargs)
elif kind in ('labels'):
return finplot.labels(df, **kwargs)
elif kind in ('hist', 'histogram'):
return finplot.hist(df, **kwargs)
else:
if x is None:
_x = df
_y = None
if 'style' not in kwargs:
kwargs['style'] = None
return finplot.plot(_x, _y, **kwargs)
volatility.append(vol)
volatility_diff.append(diff)
#volatility = volatility.rolling(60*6).mean()
#open_price_position_relative_to_volatility_box = (df['open']-df['low'])/center
#%% Plot
# create two plots
ax, ax2, ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candles = df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=ax)
# overlay volume on the top plot
volumes = df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=ax.overlay())
# put an MA on the close price
#fplt.plot(df['time'], df['close'].rolling(window).mean(), ax=ax, legend='ma-25')
#fplt.plot(df['time'], maxim, ax=ax, legend='maxim')
#fplt.plot(df['time'], minim, ax=ax, legend='minim')
# draw second plot
for i, window in enumerate(windows):
if (i < 5):
fplt.plot(df['time'],
volatility[i],
ax=ax2,
legend='volatility ' + str(window))
if (i >= 5):
fplt.plot(df['time'],
btc = yf.download('BTC-USD', '2014-09-01')
ax1, ax2, ax3, ax4, ax5 = fplt.create_plot('Bitcoin/Dollar long term analysis',
rows=5,
maximize=False)
fplt.set_y_scale(ax=ax1, yscale='log')
fplt.plot(btc.Close, color='#000', legend='Log price', ax=ax1)
btc['ma200'] = btc.Close.rolling(200).mean()
btc['ma50'] = btc.Close.rolling(50).mean()
fplt.plot(btc.ma200, legend='MA200', ax=ax1)
fplt.plot(btc.ma50, legend='MA50', ax=ax1)
btc['one'] = 1
fplt.volume_ocv(btc[['ma200', 'ma50', 'one']],
candle_width=1,
ax=ax1.overlay(scale=0.02))
daily_ret = btc.Close.pct_change() * 100
fplt.plot(daily_ret, width=3, color='#000', legend='Daily returns %', ax=ax2)
fplt.add_legend('Daily % returns histogram', ax=ax3)
fplt.hist(daily_ret, bins=60, ax=ax3)
fplt.add_legend('Yearly returns in %', ax=ax4)
fplt.bar(btc.Close.resample('Y').last().pct_change().dropna() * 100, ax=ax4)
# calculate monthly returns, display as a 4x3 heatmap
months = btc['Adj Close'].resample(
'M').last().pct_change().dropna().to_frame() * 100
months.index = mnames = months.index.month_name().to_list()
interval = '1d'
url = 'https://query1.finance.yahoo.com/v7/finance/download/%s?period1=%s&period2=%s&interval=%s&events=history' % (
symbol, start_t, end_t, interval)
r = requests.get(url)
df = pd.read_csv(StringIO(r.text))
df['Date'] = pd.to_datetime(df['Date']).astype(
'int64') // 1_000_000 # use finplot's internal representation, which is ms
ax, ax2 = fplt.create_plot('S&P 500 MACD', rows=2)
# plot macd with standard colors first
macd = df.Close.ewm(span=12).mean() - df.Close.ewm(span=26).mean()
signal = macd.ewm(span=9).mean()
df['macd_diff'] = macd - signal
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'macd_diff']],
ax=ax2,
colorfunc=fplt.strength_colorfilter)
fplt.plot(macd, ax=ax2, legend='MACD')
fplt.plot(signal, ax=ax2, legend='Signal')
# change to b/w coloring templates for next plots
fplt.candle_bull_color = fplt.candle_bear_color = '#000'
fplt.volume_bull_color = fplt.volume_bear_color = '#333'
fplt.candle_bull_body_color = fplt.volume_bull_body_color = '#fff'
# plot price and volume
fplt.candlestick_ochl(df[['Date', 'Open', 'Close', 'High', 'Low']], ax=ax)
hover_label = fplt.add_legend('', ax=ax)
axo = ax.overlay()
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'Volume']], ax=axo)
fplt.plot(df.Volume.ewm(span=24).mean(), ax=axo, color=1)
def plt_chart(fa_info, save_chart=False, interactive=False):
global df
global symbol
global file_png
# load data and convert date
symbol, sector, industry, website, num_emp, profile = fa_info
return_result = (None, None)
end_t = int(time())
start_t = end_t - NUM_OF_MONTHS * 30 * 24 * 60 * 60 # twelve months
interval = '1d'
url = 'https://query1.finance.yahoo.com/v7/finance/download/%s?period1=%s&period2=%s&interval=%s&events=history' % (
symbol, start_t, end_t, interval)
try:
r = requests.get(url)
df = pd.read_csv(StringIO(r.text))
if df.empty:
print(f"[Warn] symbol {symbol} has no data")
return return_result
if df.shape[0] < MA_SLOW:
print(
f"[Warn] symbol {symbol} has fewer than {MA_SLOW} data points")
return return_result
except Exception as ex:
print(
f"[Error] failed to download quote from Yahoo finance for {symbol}"
)
return return_result
last_date = str(df.tail(1)['Date'].values[0]).split("T")[0]
df['Date'] = pd.to_datetime(df['Date']).astype(
'int64') # use finplot's internal representation, which is ns
log_msg = ""
if sector:
title = f"[{symbol}] {website} - {sector} - {industry} {last_date}"
log_msg += title + "\n" + profile
else:
title = f"[{symbol}] {last_date}"
log_msg += title
log_msg += "\n======================================================="
print(log_msg)
# with open("_finplot-watchlist.log", "a") as f:
# f.write(log_msg)
# print(__file__)
pardir = os.path.dirname(__file__)
chart_dir = os.path.join(pardir, "charts", today_str)
if not os.path.exists(chart_dir):
os.mkdir(chart_dir)
file_png = os.path.join(chart_dir, f"{symbol}_{today_str}.png")
# print(file_png)
ax, ax1, ax2 = fplt.create_plot(title, rows=3)
# plot price
fplt.candlestick_ochl(df[['Date', 'Open', 'Close', 'High', 'Low']], ax=ax)
hover_label = fplt.add_legend(symbol, ax=ax)
ma15 = df.Close.ewm(span=MA_FAST).mean()
ma50 = df.Close.ewm(span=MA_SLOW).mean()
ma200 = df.Close.ewm(span=MA_LONG).mean()
fplt.plot(ma15, ax=ax, color=COLOR_IDX["blue"], width=1)
fplt.plot(ma50, ax=ax, color=COLOR_IDX["red"], width=2)
fplt.plot(ma200, ax=ax, color=COLOR_IDX["green"], width=3)
# plot macd with standard colors first
macd = ma15 - ma50
signal = macd.ewm(span=MACD_AVG).mean()
trend = TREND_FACTOR * (ma50 - ma200)
df['macd_diff'] = MACD_FACTOR * (macd - signal)
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'macd_diff']],
ax=ax1,
colorfunc=fplt.strength_colorfilter)
fplt.plot(macd, ax=ax1)
# fplt.plot(signal, ax=ax1, legend='Signal')
fplt.plot(macd, ax=ax1, width=COLOR_IDX["red"])
fplt.plot(signal, ax=ax1, color=COLOR_IDX["black"])
fplt.plot(trend, ax=ax1, width=2, color=COLOR_IDX["blue"])
# # change to b/w coloring templates for next plots
# fplt.candle_bull_color = fplt.candle_bear_color = '#000'
# fplt.volume_bull_color = fplt.volume_bear_color = '#333'
# fplt.candle_bull_body_color = fplt.volume_bull_body_color = '#fff'
# plot volume
# axo = ax.overlay()
vol_factor = 100000
df['Volume'] = df['Volume'] / vol_factor
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'Volume']], ax=ax2)
fplt.plot(df.Volume.ewm(span=20).mean(),
ax=ax2,
color=COLOR_IDX["black"],
width=2)
# if interactive:
# fplt.set_time_inspector(update_legend_text, ax=ax, when='hover')
# fplt.add_crosshair_info(update_crosshair_text, ax=ax)
if save_chart:
fplt.timer_callback(save, 0.5,
single_shot=True) # wait some until we're rendered
# print(chart_info)
return_result = (fplt, file_png)
return return_result
'''Plot quote table (in millions). We're using lables on top of a heatmap to create sort of a table.'''
ax.set_visible(yaxis=False) # Y axis is useless on our table
def skip_y_crosshair_info(x, y, xt, yt): # we don't want any Y crosshair info on the table
return xt, ''
fplt.add_crosshair_info(skip_y_crosshair_info, ax=ax)
fplt.set_y_range(0, 2, ax) # 0-1 for bid row, 1-2 for ask row
# add two columns for table cell colors
quotes[1] = -quotes['askSize'] * 0.5 / quotes['askSize'].max() + 0.5
quotes[0] = +quotes['bidSize'] * 0.5 / quotes['bidSize'].max() + 0.5
ts = [int(t.timestamp()) for t in quotes.index]
colmap = fplt.ColorMap([0.0, 0.5, 1.0], [[200, 80, 60], [200, 190, 100], [40, 170, 30]]) # traffic light colors
fplt.heatmap(quotes[[1, 0]], colmap=colmap, colcurve=lambda x: x, ax=ax) # linear color mapping
fplt.labels(ts, [1.5]*count, ['%.1f'%(v/1e6) for v in quotes['askSize']], ax=ax2, anchor=(0.5, 0.5))
fplt.labels(ts, [0.5]*count, ['%.1f'%(v/1e6) for v in quotes['bidSize']], ax=ax2, anchor=(0.5, 0.5))
prices, quotes = download_resample()
fplt.max_zoom_points = 5
fplt.right_margin_candles = 0
ax,ax2 = fplt.create_plot(f'BitMEX {downsample}m quote bubble plot + quote table', rows=2, maximize=False)
fplt.windows[0].ci.layout.setRowStretchFactor(0, 10) # make primary plot large, and implicitly table small
candles = fplt.candlestick_ochl(prices[['Open','Close','High','Low']], ax=ax)
candles.colors.update(dict(bear_body='#fa8')) # bright red, to make bubbles visible
fplt.volume_ocv(prices[['Open','Close','Volume']], ax=ax.overlay())
plot_quote_bubbles(quotes, ax=ax)
plot_quote_table(quotes, ax=ax2)
fplt.show()
basequote = base_asset[[
'open', 'high', 'low', 'close'
]] * quote_asset[['open', 'high', 'low', 'close']]
# averaging volume.
basequote['vol'] = (
(base_asset['tick_volume'] + quote_asset['tick_volume']) / 2)
basequote.set_index(keys=[base_asset['time']], inplace=True)
return basequote
symbol1 = input('Enter base symbol: ').upper()
symbol2 = input('Enter quote symbol: ').upper()
period = int(input('Enter period in Hours: '))
synthetic_asset = synthesize(symbol1, symbol2, period)
print(synthetic_asset)
candle_stick_data = fplt.PandasDataSource(
synthetic_asset[['open', 'close', 'high', 'low']])
volume = fplt.PandasDataSource(synthetic_asset[['open', 'close', 'vol']])
ax = fplt.create_plot(title=f'{symbol1}/{symbol2}', rows=1)
axo = ax.overlay()
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
volume_olay = fplt.volume_ocv(volume, ax=axo)
fplt.show()
mt5.shutdown()
def plot_bars(bars: pd.DataFrame):
ax1, ax2 = fplt.create_plot('Prices+Vol', rows=2)
fplt.candlestick_ochl(
bars[['price_open', 'price_close', 'price_high', 'price_low']], ax=ax1)
fplt.volume_ocv(bars[['price_open', 'price_close', 'volume_sum']], ax=ax2)
fplt.show()
df['wpr_2'] = talib.WILLR(df['high'].values,
df['low'].values,
df['close'].values,
timeperiod=96)
df['line'] = -50 # Добавляю колонку с линией для индикаторов
print(df)
# создаем 4 окна
ax, ax2, ax3, ax4 = fplt.create_plot(symbol, rows=4)
# рисуем свечной график в основном окне
candles = df[['open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=ax)
# рисуем график времени дельты свечи
delta_time = df[['open', 'close', 'delta_time']]
fplt.volume_ocv(delta_time, ax=ax2)
# рисуем график количества тиков
ticks = df[['open', 'close', 'ticks']]
fplt.volume_ocv(ticks, ax=ax3)
# WPR
df[['wpr_1']].plot(ax=ax4, legend='WPR14', color='#008800')
df[['wpr_2']].plot(ax=ax4, legend='WPR96', color='#EF5350')
df[['line'
]].plot(ax=ax4,
color='#000000') # Построение центральной линии для индикаторов
fplt.show()
"""A PandasDataSource object can be created to pass as argument in
candlestick_ochl() function."""
import pandas_datareader as pdr
import finplot as fplt
# Getting timeseries
k = pdr.DataReader('AAPL', data_source='yahoo', start='2020-01-01')
# Setting PandasDataSource objects for OCHL & volume
price = fplt.PandasDataSource(k[['Open', 'Adj Close', 'High', 'Low']])
volume = fplt.PandasDataSource(k[['Open', 'Close', 'Volume']])
# Setting Graph window with 2 axes
ax, ax2 = fplt.create_plot(title='Aaple Inc', rows=2)
# Setting overlay on first axis
axo = ax.overlay()
# plotting candlestick chart
fplt.candlestick_ochl(price)
# fplt.volume_ocv(volume, ax=axo) # plotting volume on overlay.
fplt.volume_ocv(volume, ax=ax2) # plotting volume on second axis.
# Adding text on top left corner
fplt.add_text(ax=ax, pos=(k.index[1], k.High[-1]), s='Aaple Inc')
fplt.show()
