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()
#!/usr/bin/env python3
import finplot as fplt
import numpy as np
import pandas as pd
dates = pd.date_range('01:00', '01:00:01.200', freq='1ms')
prices = pd.Series(np.random.random(len(dates))).rolling(30).mean() + 4
fplt.plot(dates, prices, width=3)
line = fplt.add_line((dates[100], 4.4), (dates[1100], 4.6), color='#9900ff', interactive=True)
## fplt.remove_primitive(line)
text = fplt.add_text((dates[500], 4.6), "I'm here alright!", color='#bb7700')
## fplt.remove_primitive(text)
rect = fplt.add_rect((dates[700], 4.5), (dates[850], 4.4), color='#8c8', interactive=True)
## fplt.remove_primitive(rect)
def save():
fplt.screenshot(open('screenshot.png', 'wb'))
fplt.timer_callback(save, 0.5, single_shot=True) # wait some until we're rendered
fplt.show()
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()
mnames = mnames[mnames.index('January'):][:12]
mrets = [months.loc[mname].mean()[0] for mname in mnames]
hmap = pd.DataFrame(columns=[2, 1, 0], data=np.array(mrets).reshape((3, 4)).T)
hmap = hmap.reset_index(
) # use the range index as X-coordinates (if no DateTimeIndex is found, the first column is used as X)
colmap = fplt.ColorMap(
[0.3, 0.5, 0.7],
[[255, 110, 90], [255, 247, 0], [60, 255, 50]]) # traffic light
fplt.heatmap(hmap, rect_size=1, colmap=colmap, colcurve=lambda x: x, ax=ax5)
for j, mrow in enumerate(np.array(mnames).reshape((3, 4))):
for i, month in enumerate(mrow):
s = month + ' %+.2f%%' % hmap.loc[i, 2 - j]
fplt.add_text((i, 2.5 - j), s, anchor=(0.5, 0.5), ax=ax5)
ax5.set_visible(crosshair=False, xaxis=False,
yaxis_labels=False) # hide junk for a more pleasing look
fplt.show()
import finplot as fplt
import numpy as np
import pandas as pd
# Creating a randon Datetime index for x-axes.
dates = pd.date_range('01:00', '01:00:01.200', freq='1ms')
# Creating a random price Series for y-axes
prices = pd.Series(np.random.random(len(dates))).rolling(30).mean() + 4
# plotting a line
fplt.plot(dates, prices, legend='Nonscence')
# Drawing a interactive line
line = fplt.add_line((dates[100], 4.4), (dates[1100], 4.6), color='#9900ff', interactive=True)
# removing the interactive line
# fplt.remove_line(line)
# adding an immovable text.
text = fplt.add_text(pos=(dates[50], 4.4), s="I'm here alright!", color='#bb7700')
# removing the immovable text.
# fplt.remove_text(text)
# displaying the plot
fplt.show()
daily_plot = fplt.candlestick_ochl(dfd.dropna(), candle_width=5)
daily_plot.colors.update(
dict(bull_body='#bfb',
bull_shadow='#ada',
bear_body='#fbc',
bear_shadow='#dab'))
daily_plot.x_offset = 3.1 # resample() gets us start of day, offset +1.1 (gap+off center wick)
# plot high resolution on top
fplt.candlestick_ochl(df[['Open', 'Close', 'High', 'Low']])
# scatter plot correlation between Google and Microsoft stock
df['ret_alphabet'] = df.Close.pct_change()
df['ret_microsoft'] = dfms.Close.pct_change()
dfc = df.dropna().reset_index(drop=True)[['ret_alphabet', 'ret_microsoft']]
fplt.plot(dfc, style='o', color=1, ax=ax2)
# draw least-square line
errfun = lambda arr: [
y - arr[0] * x + arr[1]
for x, y in zip(dfc.ret_alphabet, dfc.ret_microsoft)
]
line = scipy.optimize.least_squares(errfun, [0.01, 0.01]).x
linex = [dfc.ret_alphabet.min(), dfc.ret_alphabet.max()]
liney = [linex[0] * line[0] + line[1], linex[1] * line[0] + line[1]]
fplt.add_line((linex[0], liney[0]), (linex[1], liney[1]), color='#993', ax=ax2)
fplt.add_text((linex[1], liney[1]), 'k=%.2f' % line[0], color='#993', ax=ax2)
fplt.add_legend('Alphabet vs. Microsft 90m correlation', ax=ax2)
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()
if (e > 40):
promedio_anterior = df['close'].iloc[e - 30:e]
#print (promedio_anterior)
accu = 0
for h in promedio_anterior:
accu += h
promedio = accu / len(promedio_anterior)
print(str(promedio) + " << " + str(df['close'][e]))
#print ( ema_14[e] > ema_32[e] , ema_14[e-1] < ema_32[e-1])
#detecta subida
if (ema_14[e] > ema_32[e] and ema_14[e - 1] < ema_32[e - 1]
and promedio > df['close'][e]):
point.append(df['close'][e])
inicio = (df['time'][e], df['close'][e])
text = fplt.add_text((inicio[0], inicio[1]),
str(portfolio),
color='#bbff00')
debe_salir = True
#detecta bajada
if (debe_salir and ema_14[e] < ema_32[e]
and ema_14[e - 1] > ema_32[e - 1]):
crossdown.append(df['close'][e])
debe_salir = False
fin = (df['time'][e], df['close'][e])
profit = (fin[1] - inicio[1]) / tick_size
print("===================================")
#print (df['close'][e-4:e] )
print("inicio > " + str(inicio[1]))
print("fin > " + str(fin[1]))
print("diference >> " + str(fin[1] - inicio[1]))
print("ticks >> " + str((fin[1] - inicio[1]) / tick_size))
import pandas as pd
mt5.initialize()
# symbol = input('Enter the symbol').upper()
rates = pd.DataFrame(
mt5.copy_rates_from_pos('EURUSD', mt5.TIMEFRAME_H1, 1,
240)).drop(columns=['spread', 'real_volume'])
rates['time'] = pd.to_datetime(rates['time'], unit='s')
#______Plotting_______#
ax, ax2 = fplt.create_plot(title='EURUSD H1', rows=2)
axo = ax.overlay()
fplt.candlestick_ochl(rates[['time', 'open', 'close', 'high', 'low']], ax=ax)
fplt.volume_ocv(rates[['time', 'open', 'close', 'tick_volume']], ax=axo)
fplt.plot(rates['close'].ewm(8).mean(), legend='8 EMA', ax=ax)
fplt.plot(rates['close'].ewm(24).mean(), legend='24 EMA', ax=ax)
roll_std = (rates['close'].rolling(24).std()) * 100
fplt.plot(roll_std, legend='24 std-dev', ax=ax2)
fplt.add_text(ax=ax2,
pos=(rates['time'].iloc[-1], roll_std[roll_std.index[-1]]),
s=str(roll_std[roll_std.index[-1]]))
# print(roll_std[roll_std.index[-1]])
fplt.show()
mt5.shutdown()
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()
