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():
# 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())
df['time'] //= 1000 # convert ms to seconds
# 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 the gree
datasrc0 = fplt.PandasDataSource(df['time open close high low'.split()])
datasrc1 = fplt.PandasDataSource(df['time high tdup'.split()])
datasrc2 = fplt.PandasDataSource(df['time low tddn'.split()])
if not plots:
# first time we create the plots
plots.append(fplt.candlestick_ochl(datasrc0, ax=ax))
plots.append(fplt.labels_datasrc(datasrc1, color='#009900', ax=ax))
plots.append(
fplt.labels_datasrc(datasrc2,
color='#990000',
ax=ax,
anchor=(0.5, 0)))
else:
# every time after we just update the data sources on each plot
plots[0].update_datasrc(datasrc0)
plots[1].update_datasrc(datasrc1)
plots[2].update_datasrc(datasrc2)
def plot_heikin_achi(df, ax):
df['h_close'] = (df.open + df.close + df.high + df.low) * 0.25
df['h_open'] = (df.open.shift() + df.close.shift()) * 0.5
df['h_high'] = df[['high', 'h_open', 'h_close']].max(axis=1)
df['h_low'] = df[['low', 'h_open', 'h_close']].min(axis=1)
candle_datasrc = fplt.PandasDataSource(
df['time h_open h_close h_high h_low'.split()])
fplt.candlestick_ochl(candle_datasrc, ax=ax)
def plotter():
# getting the selections
selection = assetListBox.curselection()[0]
asset = asset_dict[assetListBox.get(selection)]
currency = denomination_dict[opMenuVar.get()]
periods = periodEntryVar.get()
if currency is None: # No currency is selected.
synthetic_asset = pd.DataFrame(
mt5.copy_rates_from_pos(
asset, mt5.TIMEFRAME_H1, 1,
periods)).drop(columns=['spread', 'real_volume'])
synthetic_asset['time'] = pd.to_datetime(synthetic_asset['time'],
unit='s')
synthetic_asset.set_index(keys=['time'], inplace=True)
else: # Cross-currency is selected
synthetic_asset = synthesize(asset, currency, periods)
synthetic_asset.dropna(inplace=True)
# Calculaating Simple Moving averages
synthetic_asset['SMA 1'] = synthetic_asset['close'].rolling(
sma1boxVar.get()).mean()
synthetic_asset['SMA 2'] = synthetic_asset['close'].rolling(
sma2boxVar.get()).mean()
# Calculating Standard-deviation.
synthetic_asset['pct_change'] = synthetic_asset['close'].pct_change() * 100
synthetic_asset['std-dev'] = synthetic_asset['pct_change'].ewm(
stddevVar.get()).std()
candle_stick_data = fplt.PandasDataSource(
synthetic_asset[['open', 'close', 'high', 'low']])
ax, ax2 = fplt.create_plot(title=f'{asset}/{currency}', rows=2)
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
# Plotting SMAs
sma1 = fplt.plot(synthetic_asset['SMA 1'],
legend=f'{sma1boxVar.get()} SMA',
ax=ax)
sma2 = fplt.plot(synthetic_asset['SMA 2'],
legend=f'{sma2boxVar.get()} SMA',
ax=ax)
# Plotting Std-dev
stdDevPlot = fplt.plot(synthetic_asset['std-dev'],
legend=f'Std Dev {stddevVar.get()}',
ax=ax2)
fplt.add_text(pos=(synthetic_asset.index[-1],
synthetic_asset['std-dev'].iloc[-1]),
s=f"{synthetic_asset['std-dev'].iloc[-1].round(3)}",
ax=ax2)
fplt.show()
def update_plot(df):
calc_bollinger_bands(df)
datasrc0 = fplt.PandasDataSource(df['t o c h l'.split()])
datasrc1 = fplt.PandasDataSource(df['t bbh'.split()])
datasrc2 = fplt.PandasDataSource(df['t bbl'.split()])
if not plots:
candlestick_plot = fplt.candlestick_ochl(datasrc0, ax=ax)
plots.append(candlestick_plot)
plots.append(fplt.plot_datasrc(datasrc1, color='#4e4ef1', ax=ax))
plots.append(fplt.plot_datasrc(datasrc2, color='#4e4ef1', ax=ax))
# redraw using bitmex colors
candlestick_plot.bull_color = '#388d53'
candlestick_plot.bull_frame_color = '#205536'
candlestick_plot.bull_body_color = '#52b370'
candlestick_plot.bear_color = '#d56161'
candlestick_plot.bear_frame_color = '#5c1a10'
candlestick_plot.bear_body_color = '#e8704f'
candlestick_plot.repaint()
else:
plots[0].update_datasrc(datasrc0)
plots[1].update_datasrc(datasrc1)
plots[2].update_datasrc(datasrc2)
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 plotter():
# getting the selections
selection = assetListBox.curselection()[0]
asset = asset_dict[assetListBox.get(selection)]
currency = denomination_dict[opMenuVar.get()]
periods = periodEntryVar.get()
synthetic_asset = synthesize(asset, currency, periods)
synthetic_asset.dropna(inplace=True)
candle_stick_data = fplt.PandasDataSource(
synthetic_asset[['open', 'close', 'high', 'low']])
ax = fplt.create_plot(title=f'{asset}/{currency}', rows=1)
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
def plt_finplot_show(df, symbol):
import finplot as fplt
df.reset_index(drop=True, inplace=True)
df = df.astype({'date': 'datetime64[ns]'})
ax, ax2 = fplt.create_plot(symbol, rows=2)
# plot candle sticks
candle_src = fplt.PandasDataSource(df[['date', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
# put an MA in there
fplt.plot(df['date'], df['close'].rolling(25).mean(), ax=ax, color='#0000ff', legend='ma-25')
# place some dumb markers
hi_wicks = df['high'] - df[['open', 'close']].T.max().T
df.loc[(hi_wicks > hi_wicks.quantile(0.99)), 'marker'] = df['close']
fplt.plot(df['date'], df['marker'], ax=ax, color='#000000', style='^', legend='dumb mark')
# draw some random crap on our second plot
df['rnd'] = np.random.normal(size=len(df))
fplt.plot(df['date'], df['rnd'], ax=ax2, color='#992277', legend='stuff')
fplt.set_y_range(ax2, -1.4, +1.7) # fix y-axis range
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()
"""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()
'H': 'high',
'L': 'low',
'V': 'volume'
})
df.to_csv(symbol + '.csv', index=False)
else:
df = pd.read_csv(symbol + '.csv')
df = df.astype({'time': 'datetime64[ns]'})
# create three plots
ax, ax2, ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candle_src = fplt.PandasDataSource(df[['time', 'open', 'close', 'high',
'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
# put an MA in there
fplt.plot(df['time'],
df['close'].rolling(25).mean(),
ax=ax,
color='#0000ff',
legend='ma-25')
# place some dumb markers
hi_wicks = df['high'] - df[['open', 'close']].T.max().T
df.loc[(hi_wicks > hi_wicks.quantile(0.99)), 'marker'] = df['close']
fplt.plot(df['time'],
df['marker'],
ax=ax,
def plot_heikin_achi_volume(df, ax):
volume_datasrc = fplt.PandasDataSource(
df['time h_open h_close volume'.split()])
fplt.volume_ocv(volume_datasrc, ax=ax)
def plotter():
"""Takes the input value from widgets and creates a candlestick plot."""
base = (symbolVar.get()).upper()
quote = quoteCurrency.get()
start_date = startDateCal.get_date()
end_date = endDateCal.get_date()
interval = intervalVar.get()
#______If no symbol entered_______#
if len(base) == 0:
errorBox()
return None
else:
pass
base_ticker = yf.Ticker(base)
#_____If symbol is invalid________#
try:
base_currency = base_ticker.info['currency']
except ImportError:
errorBox2()
return None
else:
pass
if quote == 'Select' or quote == base_currency: # No currency is selected or base denomination = selected currency.
base_ticker = yf.Ticker(base)
base_quote = base_ticker.history(
start=start_date, end=end_date, interval=interval).drop(
columns=['Dividends', 'Stock Splits', 'Volume'])
else: # A currency is selected.
base_quote = synthesize(base, quote, start_date, end_date, interval)
base_quote.dropna(inplace=True)
# print(base_quote)
# Calculating Simple Moving averages
base_quote['SMA 1'] = base_quote['Close'].rolling(sma1Var.get()).mean()
base_quote['SMA 2'] = base_quote['Close'].rolling(sma2Var.get()).mean()
# print(base_quote)
# Calculating Standard-deviation.
base_quote['pct_change'] = base_quote['Close'].pct_change() * 100
base_quote['std-dev'] = base_quote['pct_change'].rolling(
stdDevVar.get()).std()
# print(base_quote)
candle_stick_data = fplt.PandasDataSource(
base_quote[['Open', 'Close', 'High', 'Low']])
ax, ax2 = fplt.create_plot(title=f"{base}/{quote}", rows=2)
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
# Plotting SMAs
sma1 = fplt.plot(base_quote['SMA 1'], legend=f'{sma1Var.get()} SMA', ax=ax)
sma2 = fplt.plot(base_quote['SMA 2'], legend=f'{sma2Var.get()} SMA', ax=ax)
# Ploting StdDev
stdDevPlot = fplt.plot(base_quote['std-dev'],
legend=f'{stdDevVar.get()} period std-dev',
ax=ax2)
fplt.add_text(pos=(base_quote.index[-1], base_quote['std-dev'].iloc[-1]),
s=f"{base_quote['std-dev'].iloc[-1].round(2)}%",
ax=ax2)
fplt.show()
def show_day_plot(self, instance):
aux = int(instance.text.split('-')[0])
#print ('fechaaa')
#print (aux)
'''TERMINA TUS WEADAS ACA'''
#print (self.marketdata)
#temp = pd.date_range(start=temp['time'][self.fechas[0]], end=temp['time'][self.fechas[1]], freq='D')
#print (self.fechas[0])
#print (self.fechas[0],self.fechas[1])
#datex = self.fechas[3]
#print (rt.Singleton.getInstance().result)
try:
self.marcadores = pd.read_pickle('estrategia1.pkl')
self.marketdata['marker'] = self.marcadores['marker']
except:
print('mierda')
desde = self.fechas[aux] + timedelta(hours=5)
hasta = desde + timedelta(hours=24)
temp = self.marketdata[(self.marketdata.time > desde)
& (self.marketdata.time <= hasta)]
#print (temp)
#print (temp['time'][self.fecha])
#temp = self.get_frac_df(temp,)
#print ('actualizado')
#print ('SELECCIONADO')
#print (temp)
#print (temp.dtypes)
'''TODO :MEJORA EL FFT que agrege en la ventana izquierda en matplotlib'''
#self.plot_fft(temp,'filtered')
ax, ax2 = fplt.create_plot('NASDAQ', rows=2) #,maximize=True)
try:
'''CANDLESTICK CHARTS'''
candle_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src,
ax=ax,
draw_body=True,
draw_shadow=True)
fplt.plot(temp['time'],
temp['sma200'],
ax=ax,
color='#0000ff',
legend='ma-200')
fplt.plot(temp['time'],
temp['filtered'],
ax=ax,
color='#ff00ff',
legend='filtrado',
width=2)
try:
'''OBTIENE LAS ORDENES DE COMPRA-VENTA'''
buys = pd.read_pickle("book_buy.pkl")
#sell = pd.read_pickle("book_sell.pkl")
'''PROCEDE A PLOTEAR'''
print('compras')
print(buys)
pass
except:
print("[ ERROR ] Fallo al obtener las ordenes de compra/venta")
pass
'''VOLUMEN CHARTS'''
volume_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'volume']])
fplt.volume_ocv(volume_src, ax=ax2)
# place some dumb markers
#hi_wicks = df['high'] - df[['open','close']].T.max().T
#df.loc[(hi_wicks>hi_wicks.quantile(0.99)), 'marker'] = df['close']
fplt.plot(temp['time'],
temp['marker'],
ax=ax,
color='#000000',
style='^',
legend='dumb mark')
#with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also
# print (self.marcadores)
'''INTERMEDIOS'''
#te = np.random.normal(size=len(temp))
#fplt.plot(temp['time'], te, ax=ax2, color='#001177', legend='vortex pos')
#fplt.plot(temp['time'], temp['vortex_n'], ax=ax2, color='#ff0000', legend='vortex neg')
#fplt.set_y_range(ax2, -4.4, +4.4) # fix y-axis range
# draw some random crap on our second plot
#temp['rnd'] = np.random.normal(size=len(temp))
#print (temp.vortexn)
#fplt.plot(temp['time'], temp['vortexn'], ax=ax3, color='#992277', legend='stuff')
#fplt.set_y_range(ax3, -10.4, +10.7) # fix y-axis range
# finally a volume bar chart in our third plot
fplt.show()
#pass
except Exception as e:
print(e)
pass
