def __init__(self):
super().__init__()
self.setWindowTitle("QGraphicsView")
layout = QGridLayout()
self.setLayout(layout)
self.resize(800, 300)
self.ws = BinanceFutureWebsocket()
plots = {}
fplt.y_pad = 0.07 # pad some extra (for control panel)
fplt.max_zoom_points = 7
fplt.autoviewrestore()
self.ax, self.ax_rsi = fplt.create_plot('Complicated Binance Futures Example', rows=2, init_zoom_periods=3000)
self.axo = self.ax.overlay()
layout.addWidget(self.ax.vb.win, 0, 0)
self.ax_rsi.hide()
self.ax_rsi.vb.setBackgroundColor(None) # don't use odd background color
self.ax.set_visible(xaxis=True)
self.symbol = "BTCUSDT"
self.interval = "1m"
self.change_asset()
fplt.timer_callback(self.realtime_update_plot, 1) # update every second
fplt.show(qt_exec=False)
def save_plot(self, plot_title, kbars):
fplt = self.setup_plot(plot_title, kbars)
with open(f'{plot_title}.png', 'wb') as f:
fplt.timer_callback(lambda: fplt.screenshot(f),
1,
single_shot=True)
fplt.show()
def worker(sym, inter, ops, csvlock, plots):
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)
#==============================================#
ax, ax2 = fplt.create_plot(sym+' Stock Time Series - '+inter+' - '+ops, rows=2)
drawplot()
fplt.timer_callback(drawplot, 3600.0) # draw the graphs every 60 mins (3600 secs)
fplt.show()
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 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 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 __init__(self, data_host):
self.plots = []
self.data_host = data_host
symbol = data_host.request['symbol']
fplt.create_plot(f"{symbol}", init_zoom_periods=75, maximize=True)
self.update_plot()
def upd():
try:
if self.data_host.changed:
self.update_plot()
self.data_host.changed = False
except Exception as ex:
print(ex)
fplt.timer_callback(upd, 0.1) # update in 10 Hz
#fplt.autoviewrestore()
fplt.show()
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 getStockHistory(s):
stock = Stock.Stock(s)
t = stock.history()
# print(t.json())
# plt.plot(t.json()['o'])
# plt.plot(t.json()['h'])
# plt.plot(t.json()['l'])
# plt.plot(t.json()['c'])
# plt.title(stock.ticker)
# plt.show()
# df = t.json()
# df = pd.read_json(t.json())
# df = pd.DataFrame.from_dict(t.json())
# print(df)
# fplt.candlestick_ochl(df[['o', 'c', 'h', 'l']])
# fplt.show()
df = f = yfinance.download('XRP')
fplt.candlestick_ochl(df[['Open', 'Close', 'High', 'Low']])
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 search(self):
"""
This function displays given stock's history for the period of 4 years.
Args:
None
Returns:
None
"""
try:
df = yf.download(
self.search_entry.get(),
start="2017-01-01",
end=datetime.today().strftime("%Y-%m-%d"),
)
fplt.candlestick_ochl(df[["Open", "Close", "High", "Low"]])
fplt.plot(df.Close.rolling(50).mean())
fplt.plot(df.Close.rolling(200).mean())
fplt.show()
except IndexError:
messagebox.showerror("Stock Name Error", "Invalid Stock name")
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 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()
def plot_price(df):
df = df.set_index('time')
fplt.candlestick_ochl(df[['open', 'close', 'high', 'low']])
fplt.show()
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
combo.currentTextChanged.connect(update)
update(combo.currentText())
fplt.show(qt_exec=False) # prepares plots when they're all setup
win.show()
app.exec_()
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 draw_plot(self, plot_title, kbars):
fplt = self.setup_plot(plot_title, kbars)
fplt.autoviewrestore()
fplt.show()
def plot_signal(kline, buy_signal=None, sell_signal=None, *args):
"""回测完成后调用此函数绘制k线图与指标"""
fplt.foreground = '#FFFFFF'
fplt.background = '#333333'
fplt.odd_plot_background = '#333333'
fplt.cross_hair_color = "#FFFFFF"
ax, ax2, ax3 = fplt.create_plot('历史K线图',
init_zoom_periods=100,
maximize=True,
rows=3)
fplt.add_legend("K线主图", ax)
fplt.add_legend("成交量", ax2)
fplt.add_legend("指标副图", ax3)
df = pd.DataFrame(kline)
df = df[[0, 1, 2, 3, 4, 5]]
columns = ['time', 'open', 'high', 'low', 'close', 'volume']
df.columns = columns
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()]
fplt.candlestick_ochl(candlesticks)
fplt.volume_ocv(volumes, ax=ax2)
if args:
count = 1
for i in args:
indicators = pd.Series(i)
fplt.plot(df['time'],
indicators,
legend="指标{}".format(count),
ax=ax3)
count += 1
if buy_signal:
for i in buy_signal:
df.loc[df['high'] == buy_signal[buy_signal.index(i)],
"buy_signal"] = df['high']
fplt.plot(df['time'],
df['buy_signal'],
ax=ax,
color="#FF0000",
style='^',
width=2,
legend='买入信号')
if sell_signal:
for i in sell_signal:
df.loc[df['low'] == sell_signal[sell_signal.index(i)],
"sell_signal"] = df['low']
fplt.plot(df['time'],
df['sell_signal'],
ax=ax,
color="#00FF00",
style='v',
width=2,
legend='卖出信号')
fplt.show()
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()
def show(self):
"""最后必须调用此函数以显示图像"""
fplt.show()
def show():
fplt.show()
df_symbol = pd.DataFrame(read_ticker(file_csv), columns=['Ticker'])
df_symbol["HQM Score"] = 0
symbols = df_symbol['Ticker'].tolist()
num_symbols = len(symbols)
fa_dict = {}
fa_results = get_sector_profiles_nodb(symbols)
for row in fa_results:
fa_dict[row[0]] = row
for i in df_symbol.index:
if not i % 5:
finplot = reload(finplot)
print(f"\n========== {i+1} / {num_symbols} =========================\n")
symbol = df_symbol.loc[i, "Ticker"]
fa_info = fa_dict.get(symbol, [symbol,"N/A","N/A","N/A",0,"N/A",0,0])
file_png, log_msg = plt_chart(fa_info, save_chart=True, interactive=False)
if file_png:
print(log_msg)
generate_html(file_csv, file_png, i)
finplot.show()
except KeyboardInterrupt:
sys.exit()
def main():
app = QtWidgets.QApplication(sys.argv)
main = MainWindow()
finplot.show(qt_exec=False)
main.show()
sys.exit(app.exec_())
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
t -= t % (60*interval_mins)
c = trade['price']
if t < df['t'].iloc[-1]:
# ignore already-recorded trades
continue
elif t > df['t'].iloc[-1]:
# add new candle
o = df['c'].iloc[-1]
h = c if c>o else o
l = o if o<c else c
df1 = pd.DataFrame(dict(t=[t], o=[o], c=[c], h=[l], l=[l]))
df = pd.concat([df, df1], ignore_index=True, sort=False)
else:
# update last candle
i = df.index.max()
df.loc[i,'c'] = c
if c > df.loc[i,'h']:
df.loc[i,'h'] = c
if c < df.loc[i,'l']:
df.loc[i,'l'] = c
update_plot(df)
if __name__ == '__main__':
df = pd.DataFrame(price_history())
ws = BitMEXWebsocket(endpoint=baseurl+'/v1', symbol='XBTUSD')
ax = fplt.create_plot('Realtime Bitcoin/Dollar 1m (BitMEX websocket)', init_zoom_periods=100, maximize=False)
update_plot(df)
fplt.timer_callback(update_data, 1.0) # update every second
fplt.show()
import pandas_datareader.data as web
# from pandas_datareader.yahoo import options
import datetime as dt
# import matplotlib.pyplot as plt
import pandas as pd
import finplot as fin
assets = {
'Alphabet': 'GOOGL',
'Verizon': 'VZ',
'Amazon': 'AMZN',
'Coca-cola': 'KO',
'JP-Morgan': 'JPM',
'General Electric': 'GE',
'Goldman': 'GS'
}
start = dt.datetime(2020, 1, 1)
end = dt.datetime.now()
fb = web.DataReader(assets['Goldman'], 'yahoo', start, end)
# print(fb)
candles = fb[['Open', 'Close', 'High', 'Low']]
fin.create_plot(title='Goldman')
fin.candlestick_ochl(candles)
fin.show()
