def print_linear(symbol, interval):
"""Plot line chart selected crypto and time interval from Binance API
:param str symbol: crypto pair (e.g. BTCUSDT)
:param str interval: time interval (e.g 1d, 1h, 1m.. more in README file)
:return: html file with line chart created by plotly library
"""
df = prepare_data(symbol, interval)
trace = go.Ohlc(
x=df.index[:],
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
name=symbol,
increasing=dict(line=dict(color='blue')),
decreasing=dict(line=dict(color='red')),
)
data = [trace]
layout = {
'title': symbol,
'yaxis': {'title': 'Price'}
}
fig = dict(data=data, layout=layout)
plot = py.plot(fig, filename=f'templates/{symbol}_{interval}.html', auto_open=False)
return plot
def _main_plot(self):
data = None
if self._chart_type == "Candlestick":
data = go.Candlestick(x=self._time,
open=self._open,
high=self._high,
low=self._low,
close=self._close,
name="Candlestick",
showlegend=self._showlegend)
elif self._chart_type == "Line":
data = go.Scatter(x=self._time,
y=self._close,
name="Close",
showlegend=self._showlegend)
elif self._chart_type == "OHLC":
data = go.Ohlc(x=self._time,
open=self._open,
high=self._high,
low=self._low,
close=self._close,
name="OHLC",
showlegend=self._showlegend)
elif self._chart_type == 'Area':
data = go.Scatter(x=self._time,
y=self._close,
name="Close",
showlegend=self._showlegend,
fill='tozeroy')
self._fig.add_trace(data, row=1, col=1)
def test_create_chart(data):
date_y = datetime.date.today() - timedelta(days=60)
df = nsepy.get_history(symbol=data, start=date_y, end=datetime.date.today())
buy = []
df.reset_index('Date', inplace=True)
df['Date'] = df['Date'].astype(str)
df['Date'] = df['Date'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d'))
df.set_index('Date', inplace=True)
df = df['Close'].resample('1M').ohlc()
df.reset_index('Date', inplace=True)
fig = go.Figure(data=[go.Ohlc(x=df['Date'], open=df['open'], high=df['high'], low=df['low'], close=df['close'])])
df['HA_Close'] = round((df['open'] + df['high'] + df['low'] + df['close']) / 4, 2)
df['HA_Open'] = (df['open'].shift(1) + df['open'].shift(1)) / 2
df.iloc[0, df.columns.get_loc("HA_Open")] = (df.iloc[0]['open'] + df.iloc[0]['close']) / 2
df['HA_High'] = df[['high', 'low', 'HA_Open', 'HA_Close']].max(axis=1)
df['HA_Low'] = df[['high', 'low', 'HA_Open', 'HA_Close']].min(axis=1)
fig = go.Figure(data=[
go.Candlestick(x=df['Date'], open=df['HA_Open'], high=df['HA_High'], low=df['HA_Low'], close=df['HA_Close'])])
if (float(df.iloc[[-2]]['HA_High']) <= float(df.iloc[[-2]]['HA_Open'])):
if (float(df.iloc[[-2]]['HA_Low']) <= float(df.iloc[[-2]]['HA_Close'])):
print('Sell Recommanded Price for ' + data + ' :', round(float(df.iloc[[-2]]['HA_Low']) * 98 / 100, 2))
elif (float(df.iloc[[-2]]['HA_Low']) >= float(df.iloc[[-2]]['HA_Open'])):
if ((float(df.iloc[[-2]]['HA_High']) >= float(df.iloc[[-2]]['HA_Close']))):
buy.append(data)
buy.append(round(float(df.iloc[[-2]]['HA_High']) * 102 / 100, 2))
print('Buy Recommanded Price for ' + data + ':', round(float(df.iloc[[-2]]['HA_High']) * 102 / 100, 2))
os.chdir('/storage/emulated/0/recom')
df[['Date', 'HA_Open', 'HA_High', 'HA_Low', 'HA_Close']].to_excel(data + '.xlsx')
fig.write_html(data + '.html')
buy_list.append(buy)
def candles(yourTitle, xTitle, yTitle, dataFrame):
"""
Function for building market figures, returning figure object
:param yourTitle: Title of graph
:param xTitle: Title of X axis
:param yTitle: Title of Y axis
:param dataFrame: source DataFrame with columns Date, Open, High, Low, Close
:return: figure object
"""
fig = go.Figure(data=go.Ohlc(x=dataFrame['Date'],
open=dataFrame['Open'],
high=dataFrame['High'],
low=dataFrame['Low'],
close=dataFrame['Close']))
fig.update_layout(title_text=yourTitle,
title={
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
xaxis_rangeslider_visible=True,
xaxis_title=xTitle,
yaxis_title=yTitle)
return fig
def ohlcv(df, date, open, high, low, close, volume):
rDf = df.reset_index()
# Create subplots and mention plot grid size
fig = make_subplots(rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.03,
subplot_titles=('OHLC', 'Volume'),
row_width=[0.2, 0.7])
fig.add_trace(go.Ohlc(x=rDf[date],
open=rDf[open],
high=rDf[high],
low=rDf[low],
close=rDf[close],
name="OHLC"),
row=1,
col=1)
# Bar trace for volumes on 2nd row without legend
fig.add_trace(go.Bar(x=rDf[date], y=rDf[volume], showlegend=False),
row=2,
col=1)
# Do not show OHLC's rangeslider plot
fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()
def plot_news(hourly_ohlc, all_sentis):
fig = make_subplots(rows=3,
cols=1,
shared_xaxes=True,
vertical_spacing=0,
row_heights=[3, 1, 1])
fig.add_trace(go.Ohlc(x=hourly_ohlc.index,
open=hourly_ohlc.open,
high=hourly_ohlc.high,
low=hourly_ohlc.low,
close=hourly_ohlc.close,
name="Stock OHLC"),
row=1,
col=1)
fig.add_trace(go.Bar(x=all_sentis.index,
y=all_sentis.values,
name=f"Key Word Count",
marker_color="lightslategray"),
row=2,
col=1)
fig.add_trace(go.Bar(x=hourly_ohlc.index,
y=hourly_ohlc.volume,
name=f"Stock Volume",
marker_color="green"),
row=3,
col=1)
fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(
height=600,
width=1200,
title_text=f"News intraday twitter sentiment from mainstream media")
fig.show()
def plotGraph(self, stock_data: str):
graph_image_base64 = None
try:
df = pd.read_csv(stock_data, sep=";")
fig = go.Figure(data=go.Ohlc(x=df['date'],
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close']))
graphFileName = os.path.join(os.getcwd(), 'libs', 'algonia', 'py',
'graphs',
str(uuid.uuid1()) + '.jpeg')
fig.write_image(graphFileName)
with open(graphFileName, "rb") as img_file:
graph_image_base64 = base64.b64encode(img_file.read())
return graph_image_base64
except Exception as inst:
print('error')
print(type(inst))
print(inst.args)
print(inst)
return 'error'
def build_graphs(chosen_volume): # represents that which is assigned to the component property of the Input
dff = df[df.Volume > chosen_volume]
print(dff.head())
fig_candle = go.Figure(
go.Candlestick(x=dff['Date'],
open=dff['Open'],
high=dff['High'],
low=dff['Low'],
close=dff['Close'],
text=dff['Volume'])
)
fig_candle.update_layout(margin=dict(t=30, b=30)) # xaxis_rangeslider_visible=False,
fig_ohlc = go.Figure(
go.Ohlc(x=dff['Date'],
open=dff['Open'],
high=dff['High'],
low=dff['Low'],
close=dff['Close'],
text=dff['Volume'])
)
fig_ohlc.update_layout(margin=dict(t=30, b=30))
return fig_candle, fig_ohlc
def plot_senti3(self,hourly_ohlc,all_sentis):
# plot it with plotly
fig = make_subplots(rows=4, cols=1,
shared_xaxes=True,
vertical_spacing=0,row_heights=[1.5, 1, 1, 1])
fig.add_trace(go.Ohlc(x=hourly_ohlc.index,
open=hourly_ohlc.open, high=hourly_ohlc.high,
low=hourly_ohlc.low, close=hourly_ohlc.close,name="Intraday stock price"),
row=1, col=1)
fig.add_trace(go.Bar(x=hourly_ohlc.index, y=hourly_ohlc.volume,name="Intraday volume",marker_color="lightslategray"),
row=2, col=1)
fig.add_trace(go.Bar(x=all_sentis.index, y=all_sentis.All_counts,name="Publication count",marker_color="orange"),
row=3, col=1)
fig.add_trace(go.Bar(x=all_sentis.index, y=all_sentis.Positive,name="Positive score",marker_color="green"),
row=4, col=1)
fig.add_trace(go.Bar(x=all_sentis.index, y=all_sentis.Negative,name="Negative score",marker_color="red"),
row=4, col=1)
fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(height=600, width=1200,
title_text="{0} intraday twitter sentiment and earnings info".format(self.key_word))
fig.show()
if not os.path.exists(self.saveaddr):os.mkdir(self.saveaddr)
fig.write_image(f'{self.saveaddr}\\{self.key_word}.png')
def plot(ohlc):
fig = go.Figure(data=go.Ohlc(x=ohlc['Date'],
open=ohlc['Open'],
high=ohlc['High'],
low=ohlc['Low'],
close=ohlc['Close']))
fig.show()
def create_chart():
# dfx=pd.read_excel('c.xlsx',index_col=0,parse_dates=True)
# print(dfx)
# print(dfx.index)
df = nsepy.get_history(symbol='CIPLA',
start=datetime.date(2006, 6, 1),
end=datetime.date.today())
# print(df.index)
df.reset_index('Date', inplace=True)
# print(df.index)
# print(df)
df['Date'] = df['Date'].astype(str)
import datetime as dt
df['Date'] = df['Date'].apply(
lambda x: dt.datetime.strptime(x, '%Y-%m-%d'))
df.set_index('Date', inplace=True)
# print(df.index)
df = df['Close'].resample('1M').ohlc()
df.reset_index('Date', inplace=True)
print('After resampling')
print(df)
df.to_excel('monthly.xlsx')
fig = go.Figure(data=[
go.Ohlc(x=df['Date'],
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'])
])
df['HA_Close'] = (df['open'] + df['high'] + df['low'] + df['close']) / 4
df['HA_Open'] = (df['open'].shift(1) + df['open'].shift(1)) / 2
df.iloc[0, df.columns.get_loc("HA_Open")] = (df.iloc[0]['open'] +
df.iloc[0]['close']) / 2
df['HA_High'] = df[['high', 'low', 'HA_Open', 'HA_Close']].max(axis=1)
df['HA_Low'] = df[['high', 'low', 'HA_Open', 'HA_Close']].min(axis=1)
print(df[['Date', 'HA_Open', 'HA_High', 'HA_Low', 'HA_Close']])
fig = go.Figure(data=[
go.Candlestick(x=df['Date'],
open=df['HA_Open'],
high=df['HA_High'],
low=df['HA_Low'],
close=df['HA_Close'])
])
if (float(df.iloc[[-2]]['HA_High']) <= float(df.iloc[[-2]]['HA_Open'])):
if (float(df.iloc[[-2]]['HA_Low']) <= float(df.iloc[[-2
]]['HA_Close'])):
print('Sell Recommanded Price:',
float(df.iloc[[-2]]['HA_Low']) * 98 / 100)
if (float(df.iloc[[-2]]['HA_Low']) >= float(df.iloc[[-2]]['HA_Open'])):
if ((float(df.iloc[[-2]]['HA_High']) >= float(
df.iloc[[-2]]['HA_Close']))):
print('Buy Recommanded Price:',
float(df.iloc[[-2]]['HA_High']) * 102 / 100)
fig.show()
return df
def plot_ohlc_binance(df, title=''):
fig1 = go.Figure(data=go.Ohlc(x=df.index,
open=df['Open_binance'],
high=df['Hight_binance'],
low=df['Low_binance'],
close=df['Close_binance']))
fig1.update_layout(title=title)
fig1.show()
return
def create_chart():
df = pd.read_csv('c.csv', names=['date', 'Close'], index_col=0, parse_dates=True)
df = df['Close'].resample('1M').ohlc()
df.reset_index('date', inplace=True)
# print(df)
fig = go.Figure(data=[go.Ohlc(x=df['date'], open=df['open'], high=df['high'], low=df['low'], close=df['close'])])
# fig.show()
return df
def create_chart(df):
df = df.head(30)
fig = go.Figure(data=[
go.Ohlc(x=df['Date'],
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'])
])
fig.show()
def plot_crypto(symbol):
'Plot the graph given a symbol name'
df_symbol = df[df['crypto_id'] == f'{symbol}']
fig = go.Figure(data=go.Ohlc(x=df_symbol['date'],
open=df_symbol['open'],
high=df_symbol['high'],
low=df_symbol['low'],
close=df_symbol['close']))
fig.update_layout(title=f'{symbol} currency')
return fig.show()
def simple_stock_chart(daily, date, ticker):
"""
Main stock chart with PSAR: Returns a plotly Dash figure object for the OHLC chart
including PSAR.
"""
# Create chart specific dataframe from df_daily
df_stock = daily.loc[
pd.IndexSlice[:date, ticker],
["open", "high", "low", "close", "psar_short", "psar_long"],
].droplevel(1)
# Build chart
fig_stock = {
"data": [
# Create OHLC.
go.Ohlc(
x=df_stock.index,
open=df_stock["open"],
high=df_stock["high"],
low=df_stock["low"],
close=df_stock["close"],
name="OHLC",
line={"width": 1.5},
increasing_line_color=c5,
decreasing_line_color=c1,
),
# Add in PSAR one day.
go.Scatter(
x=df_stock.index,
y=df_stock["psar_short"],
name="PSAR Short",
mode="markers",
marker_size=4,
marker_color=c4,
),
# Add in PSAR weekly.
go.Scatter(
x=df_stock.index,
y=df_stock["psar_long"],
name="PSAR Long",
mode="markers",
marker_size=4,
marker_color=c2,
),
],
# Adjust layout properties.
"layout":
go.Layout(
title="OHLC chart for {}".format(ticker),
yaxis={"title": "Price in AU$"},
xaxis={"rangeslider": dict(visible=False)},
),
}
return fig_stock
def update_grafico_principal(n_clicks, sensor, ejes):
if len(ejes) < 1:
ejes.append('x')
# fecha = datos.fecha_inicial()
#Se inicializan variables
df = pd.DataFrame()
fig_principal = go.Figure()
#Listas que conteneran cada trace generado por cada dataframe creado para poder visualizarlos en una grafica
trace_principal = []
if n_clicks >= 0:
#Dependiendo del tipo de sensor se crean visualizaciones distintas
if tipo_sensor == 'Acelerometro':
if cantidad_sensores == '1-sensor':
# La variable df contiene el dataframe que se utiliza para generar los graficos OHLC e histograma
new_df = pd.DataFrame()
list_df = []
colors = [
'#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231',
'#911eb4'
]
count = 0
new_count_de = 0
new_count_in = 1
for e in ejes:
start_time = time()
df = datos.datos_ace(sensor, e)
elapsed_time = time() - start_time
print("Tiempo Transcurrido crear DF: %0.1f seconds." %
elapsed_time)
# Aqui se crea el grafico OHLC
start_time = time()
trace_principal.append(
go.Ohlc(x=df.index,
open=df['open'],
high=df['max'],
low=df['min'],
close=df['close'],
increasing_line_color=colors[new_count_in],
decreasing_line_color=colors[new_count_de],
name=str(sensor) + ' eje: ' + str(e),
showlegend=True))
count = count + 1
new_count_in = new_count_in + 2
new_count_de = new_count_de + 2
new_df = df
#lista de dataframe para generar los indicadores resumen
list_df.append(df)
df = pd.concat(list_df, axis=0, ignore_index=True)
fig_principal = go.Figure(data=trace_principal)
return fig_principal
def plot_returns(self, start="2019-01-01", end="2020-01-01"):
if self.expected_returns is None:
self.expected_return()
temp = self.historical.reset_index()
fig = go.Figure(data=go.Ohlc(x=temp['Date'],
open=temp['Open'],
high=temp['High'],
low=temp['Low'],
close=temp['Close']))
fig.show()
del temp
def colored_bar_trace(df):
return go.Ohlc(
x=df.index,
open=df["open"],
high=df["high"],
low=df["low"],
close=df["close"],
showlegend=True,
name="Colored bar",
line=dict(width=0.6),
)
def get_ohlc(self, price_data, ticker):
'''return go.Ohlc object '''
return go.Ohlc(
x=price_data.index,
open=price_data.Open,
high=price_data.High,
close=price_data.Close,
low=price_data.Low,
increasing_line_color="Black",
decreasing_line_color="Black",
name=f"{ticker.upper()}",
)
def bar_trace(df):
return go.Ohlc(
x=df.index,
open=df["open"],
high=df["high"],
low=df["low"],
close=df["close"],
increasing=dict(line=dict(color="#888888")),
decreasing=dict(line=dict(color="#888888")),
showlegend=True,
name="Bar",
line=dict(width=0.6),
)
def wrapper(df):
fig1 = go.Figure(data=go.Ohlc(x=df.index,
open=df['Open_okex'],
high=df['Hight_okex'],
low=df['Low_okex'],
close=df['Close_okex']))
fig1.update_layout(title='before')
fig1.show()
df = func(df)
fig1 = go.Figure(data=go.Ohlc(x=df.index,
open=df['Open_okex'],
high=df['Hight_okex'],
low=df['Low_okex'],
close=df['Close_okex']))
fig1.update_layout(title='after')
fig1.show()
fig2 = go.Figure(data=go.Ohlc(x=df.index,
open=df['Open_binance'],
high=df['Hight_binance'],
low=df['Low_binance'],
close=df['Close_binance']))
fig2.update_layout(title='binance')
fig2.show()
def update_timeline_graph(mode, n_clicks, ticker):
TICKER_PERIOD = "2y" # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
GOOGLE_TIME_PERIOD = "today 12-m"
KWD_LIST = [ticker, "bitcoin", "stimulus check"]
TA_TIMEFRAME = "week"
df = my_ticker(ticker, TICKER_PERIOD)
# add moving average
df["ma50"] = df["Close"].rolling(window=50).mean()
df["ma200"] = df["Close"].rolling(window=200).mean()
df_btc = my_ticker("BTC-USD", TICKER_PERIOD)
df_google = google_trend(KWD_LIST, GOOGLE_TIME_PERIOD)
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Ohlc(x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
increasing_line_color= 'green', decreasing_line_color= 'orange', name=ticker)
, secondary_y=True)
fig.add_trace(go.Scatter(x=df.index, y=df["ma200"], name=f"{ticker} ma200",
line = dict(width=1, color="pink")), secondary_y=True)
fig.add_trace(go.Scatter(x=df.index, y=df["ma50"], name=f"{ticker} ma50",
line = dict(width=1, color="lightgreen")), secondary_y=True)
if mode == "ticker_vs_btc":
fig.add_trace(go.Scatter(x=df_btc.index, y=df_btc["Close"], name="Bitcoin",
line = dict(width=1, color="lightblue")), secondary_y=False)
fig.update_yaxes(title_text="Price BTC (USD)", secondary_y=False, color="lightblue")
elif mode == "ticker_vs_google_search":
for i, kwd in enumerate(KWD_LIST):
fig.add_trace(go.Scatter(x=df_google.index, y=df_google[KWD_LIST[i]], name=f"search {str(KWD_LIST[i])}",
line = dict(width=1, dash='dot')), secondary_y=False)
fig.update_yaxes(title_text="Google search index", secondary_y=False, color="grey")
fig.update_yaxes(title_text=f"Price {ticker} (USD)", secondary_y=True)
fig.update_xaxes(title_text="Date")
#fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(template="plotly_white",
title=f"Ticker: {ticker}, Company: {get_company_name(ticker)} <br>TA {TA_TIMEFRAME}: {tradingview_rec(ticker, TA_TIMEFRAME)}")
return fig
def priceHistoryOLHCChart(self, ticker=False, period=8):
"""Function to generate ohlc history price chart
Args:
ticker (str): Name of the ticker. Defaults to False.
period (int): Number of years to get history. Defaults to 8.
"""
# Check arguments
if not ticker:
return
elif ticker=='IBOV':
# Data Frame with Price Data
Plot_DF = pd.DataFrame({"Open": self.Ibov_Data["Open"]
,"Close": self.Ibov_Data["Close"]
,"High": self.Ibov_Data["High"]
,"Low": self.Ibov_Data["Low"]
,"Volume": self.Ibov_Data["Volume"]
}, self.Ibov_Data.index).dropna()
else:
# Data Frame with Price Data
Plot_DF = pd.DataFrame({"Open": self.Price_Data["Open"][ticker]
,"Close": self.Price_Data["Close"][ticker]
,"High": self.Price_Data["High"][ticker]
,"Low": self.Price_Data["Low"][ticker]
,"Volume": self.Price_Data["Volume"][ticker]
}, self.Price_Data.index).dropna()
Plot_DF = Plot_DF.iloc[-252*period:]
# Plotly object
fig = go.Figure(data=go.Ohlc(
x=Plot_DF.index
,open=Plot_DF['Open']
,high=Plot_DF['High']
,low=Plot_DF['Low']
,close=Plot_DF['Close']
))
fig.update_layout(
title=f"Price History OHLC"
,yaxis_title=f"{ticker}"
)
fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()
def simple_stock_chart(df_stock, fig_stock_title):
"""Simple stock chart"""
fig_stock = {
"data": [
go.Ohlc(
x=df_stock.index,
open=df_stock["Open"],
high=df_stock["High"],
low=df_stock["Low"],
close=df_stock["Close"],
)
],
"layout": go.Layout(
title="OHLC chart for {}".format(fig_stock_title),
yaxis={"title": "Price in US$"},
),
}
return fig_stock
def plot_ohlc(fin_data, user_input):
fig = make_subplots(rows=2,
cols=1,
row_heights=[0.8, 0.2],
shared_xaxes=True,
vertical_spacing=0.02)
fig.add_trace(go.Ohlc(x=fin_data.index,
open=fin_data['Open'],
high=fin_data['High'],
low=fin_data['Low'],
close=fin_data['Close']),
row=1,
col=1)
fig.add_trace(go.Bar(x=fin_data.index,
y=fin_data["Volume"],
marker_color="black"),
row=2,
col=1)
fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(
title=user_input.upper(),
plot_bgcolor="#FFFFFF",
hovermode="x",
hoverdistance=100, # Distance to show hover label of data point
spikedistance=-1, # Distance to show spike
xaxis=dict(
# title="time",
linecolor="#BCCCDC",
# showspikes=True, # Show spike line for X-axis
# Format spike
spikethickness=2,
spikedash="dot",
spikecolor="#999999",
spikemode="toaxis+across",
),
yaxis=dict(title="Price", linecolor="#BCCCDC"),
yaxis2=dict(title="Volume", linecolor="#BCCCDC"))
return fig
def draw_price_and_volume_candle_chart(
df: pandas.DataFrame,
symbol: str,
sma_column: str = None,
export_picture: bool = False,
):
"""
Draw Price & Volume Candle Chart for OHLC data with an optional SMA line using Plotly.
"""
t1 = go.Ohlc(
name="OHLC",
x=df.index,
yaxis="y2",
open=df["Open"],
high=df["High"],
low=df["Low"],
close=df["Close"],
)
t2 = go.Bar(name="Volume", x=df.index, y=df["Volume"], yaxis="y",)
if sma_column:
t3 = go.Scatter(name=sma_column, x=df.index, yaxis="y2", y=df[sma_column],)
fig = Figure([t1, t2, t3])
else:
fig = Figure([t1, t2])
layout_params = {
"title_text": f"{symbol} - Price & Volume",
"height": 800,
"yaxis": {"domain": [0, 0.2], "showticklabels": True},
"yaxis2": {"domain": [0.2, 1]},
}
if export_picture:
layout_params["width"] = 1200
fig.update_layout(layout_params)
fig.show()
def live_quote(ticker="NEA"):
end = datetime.datetime.now()
start = end - datetime.timedelta(days=3)
if ticker == "XJO":
ticker_quote = "^AXJO"
elif ticker == "XVI":
ticker_quote = "^AXVI"
else:
ticker_quote = ticker + ".AX"
df_stock = yf.download(ticker_quote, start=start, end=end, interval="1m")
df_stock = df_stock.loc[df_stock.index[-1].strftime("%Y-%m-%d"):, :]
fig_stock = {
"data": [
# Create OHLC.
go.Ohlc(
x=df_stock.index,
open=df_stock["Open"],
high=df_stock["High"],
low=df_stock["Low"],
close=df_stock["Close"],
name="Live",
line={"width": 1.5},
increasing_line_color=c5,
decreasing_line_color=c1,
)
],
# Adjust layout properties.
"layout":
go.Layout(
title="Live chart for {}".format(ticker),
yaxis={"title": "Price in AU$"},
xaxis={"rangeslider": dict(visible=True)},
),
}
return fig_stock
def ohlc(df: pd.DataFrame,
timestamp=None,
ohlc_key=None,
symbol=None,
increasing_line_color=None,
decreasing_line_color=None,
strftime_str='%Y/%m/%d %H:%M:%S'):
if ohlc_key is None:
ohlc_key = ['open', 'high', 'low', 'close']
if timestamp is None:
timestamp = df.index
timestamp = timestamp.strftime(strftime_str)
if isinstance(timestamp, pd.Series):
timestamp = timestamp.apply(
lambda x: pd.Timestamp.strftime(x, strftime_str))
return go.Ohlc(x=timestamp,
open=df[ohlc_key[0]],
high=df[ohlc_key[1]],
low=df[ohlc_key[2]],
close=df[ohlc_key[3]],
name=symbol,
increasing_line_color=increasing_line_color,
decreasing_line_color=decreasing_line_color)
def price_plot_with_plotly(price, show_hours=True, **kwargs):
"""Use plotly for interactive plot.
Parameters
----------
price : pd.DataFrame
price data frame
"""
has_ohlc, ohlc = get_ohlc(price) # Get ohlc for
config = _process_kwargs(kwargs, _valid_plot_kwargs())
config['type'] = config['type'].lower() # Relax the spelling.
# style = config['style']
if show_hours:
timeline_str = [
p.strftime("%y-%m-%d (%H:%M:%S)") for p in price.index.to_list()
]
else:
timeline_str = [p.strftime("%y-%m-%d") for p in price.index.to_list()]
fig = go.Figure()
if config['type'] == 'line':
fig.add_trace(
go.Scatter(x=timeline_str,
y=price.Close,
line=dict(color='royalblue'),
name='Price'))
elif config['type'] == 'candle' or config['type'] == 'candlestick':
if has_ohlc:
fig.add_trace(
go.Candlestick(x=timeline_str,
open=ohlc[0],
high=ohlc[1],
low=ohlc[2],
close=ohlc[3],
name='Price'))
else:
raise AttributeError(
'Try to plot candlestick but the data has no ohlc columns')
elif config['type'] == 'ohlc' or config['type'] == 'bars':
if has_ohlc:
fig.add_trace(
go.Ohlc(x=timeline_str,
open=ohlc[0],
high=ohlc[1],
low=ohlc[2],
close=ohlc[3],
name='Price'))
else:
raise AttributeError(
'Try to plot ohlc but the data has no ohlc columns')
mavgs = config['mav']
if mavgs is not None:
# Overlay moving average
if isinstance(mavgs, int):
mavgs = mavgs, # convert to tuple
if len(mavgs) > 7:
mavgs = mavgs[0:7] # take at most 7
#
# if style['mavcolors'] is not None:
# mavc = cycle(style['mavcolors'])
# else:
# mavc = None
for mav in mavgs:
mavprices = price.Close.rolling(mav).mean().values
# if mavc:
# ax1.plot(timeline_str, mavprices, color=next(mavc))
# else:
# ax1.plot(timeline_str, mavprices)
fig.add_trace(
go.Scatter(x=timeline_str,
y=mavprices,
line=dict(color='royalblue'),
name="MAvg " + str(mav)))
fig.update_layout(title={
'text': config['title'],
'xanchor': 'auto'
},
yaxis_title='Price',
xaxis=dict(tickangle=-90))
fig.show()