Below is the starting code, dataframes and TA indicator. Using plotly to make all my graphs. The dataframes are 'df' and 'd15'. I do have others but will keep it simple for the help.
import yfinance as yf
import plotly.graph_objs as go
#Importing my data
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
d15 = yf.download(tickers='EURUSD=X', period='3d',interval='15m')
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
atr_period = 10
atr_multiplier = 6.0
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
supertrend = Supertrend(df, atr_period, atr_multiplier)
df = df.join(supertrend)
#15 Minute Indicator
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
atr_period = 10
atr_multiplier = 6.0
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
supertrend = Supertrend(df, atr_period, atr_multiplier)
df = df.join(supertrend)
This next part is the plot which I think is where I need the help. I need to add 2 buttons and add these 2 charts to each other?
Button 1: 5m (Shows 05m TF Plot)
Button 2: 15m (Shows 15m TF Plot)
#5 Minute TF plot
fig = go.Figure()
fig.add_trace(go.Candlestick(x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
increasing_line_color= '#04b29b',
decreasing_line_color= '#ff2d5d',
increasing_fillcolor = '#04b29b',
decreasing_fillcolor = '#ff2d5d',
name='EURUSD'
))
fig.add_trace(go.Scatter(x=df.index,
y=df['Final Lowerband'],
mode='lines',
line=dict(color='#04b29b'),
name='Bullish'
))
fig.add_trace(go.Scatter(x=df.index,
y=df['Final Upperband'],
mode='lines',
line=dict(color='#ff2d5d'),
name='Bearish'
))
fig.update_layout(xaxis_rangeslider_visible=False,
plot_bgcolor = 'black', showlegend = False,
margin = dict(l=10, r=10,t=10,b=10),
paper_bgcolor='black',
xaxis=dict(showgrid=False, zerolinecolor = 'white',
color='white'),
yaxis=dict(showticklabels=False, showgrid=False))
fig.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), #hide weekends
dict(values=["2015-12-25", "2016-01-01"]) # hide Christmas
and New Year's
]
)
fig.show()
This is the 15 minute tf
15 Minute TF Plot
fig15 = go.Figure()
fig15.add_trace(go.Candlestick(x=d15.index,
open=d15['Open'],
high=d15['High'],
low=d15['Low'],
close=d15['Close'],
increasing_line_color= '#04b29b',
decreasing_line_color= '#ff2d5d',
increasing_fillcolor = '#04b29b',
decreasing_fillcolor = '#ff2d5d',
name='EURUSD'
))
fig15.add_trace(go.Scatter(x=d15.index,
y=d15['Final Lowerband'],
mode='lines',
line=dict(color='#04b29b'),
name='Bullish'
))
fig15.add_trace(go.Scatter(x=d15.index,
y=d15['Final Upperband'],
mode='lines',
line=dict(color='#ff2d5d'),
name='Bearish'
))
fig15.update_layout(xaxis_rangeslider_visible=False,
plot_bgcolor = 'black', showlegend = False,
margin = dict(l=10, r=10,t=10,b=10),
paper_bgcolor='black',
xaxis=dict(showgrid=False, zerolinecolor = 'white',
color='white'),
yaxis=dict(showticklabels=False, showgrid=False))
fig15.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), #hide weekends
dict(values=["2015-12-25", "2016-01-01"]) # hide Christmas
and New Year's
]
)
fig15.show()
CodePudding user response：
If you want to get an answer quickly, you will get a quicker answer if you put a more simplified code on it. Or, if you create a reproducible situation with sample data and code for graphs, you will have a better chance of getting an answer. To answer the main question of how to make each graph a button, you can use the stock price data from the official reference, draw three different stocks, and set the show/hide control for each to show For example, if you only have AAPL, set the others to False to hide them. That is simply the only setting.
import plotly.graph_objects as go
import plotly.express as px
df = px.data.stocks()
fig = go.Figure()
fig.add_trace(go.Scatter(x=df['date'], y=df['AAPL'], name='AAPL'))
fig.add_trace(go.Scatter(x=df['date'], y=df['GOOG'], name='GOOG'))
fig.add_trace(go.Scatter(x=df['date'], y=df['AMZN'], name='AMZN'))
fig.update_layout(
updatemenus=[
dict(
type="buttons",
direction="right",
active=0,
x=0.25,
y=1.2,
buttons=list([
dict(label="All",
method="update",
args=[{"visible": [True, True, True]},
{"title": "All"}
]),
dict(label="AAPL",
method="update",
args=[{"visible": [True, False, False]},
{"title": "AAPL"}
]),
dict(label="GOOG",
method="update",
args=[{"visible": [False, True, False]},
{"title": "GOOG"}
]),
dict(label="AMZN",
method="update",
args=[{"visible": [False, False, True]},
{"title": "AMZN"}
]),
]),
)
])
fig.show()
