You can do so in three steps:
- set up a figure using
go.Figure()
,
- add a trace for your bid_prices using
fig.update(go.Scatter)
- and do the same thing for your flags.
The snippet below does exactly what you're describing in your question. I've set up two dataframes df1
and df2
, and then I've merged them together to make things a bit easier to reference later on.
I'm also showing flags for an accumulated series where each increment in the series > 0.9
is flagged in flags = [True if elem > 0.9 else False for elem in bid_price]
. You should be able to easily adjust this to whatever your real world dataset looks like.
Plot:

Complete code with random data:
# imports
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import random
# settings
observations = 100
np.random.seed(5); cols = list('a')
bid_price = np.random.uniform(low=-1, high=1, size=observations).tolist()
flags = [True if elem > 0.9 else False for elem in bid_price]
time = [t for t in pd.date_range('2020', freq='D', periods=observations).format()]
# bid price
df1=pd.DataFrame({'time': time,
'bid_price':bid_price})
df1.set_index('time',inplace = True)
df1.iloc[0]=0; d1f=df1.cumsum()
# flags
df2=pd.DataFrame({'time': time,
'flags':flags})
df2.set_index('time',inplace = True)
df = df1.merge(df2, left_index=True, right_index=True)
df.bid_price = df.bid_price.cumsum()
df['flagged'] = np.where(df['flags']==True, df['bid_price'], np.nan)
# plotly setup
fig = go.Figure()
# trace for bid_prices
fig.add_traces(go.Scatter(x=df.index, y=df['bid_price'], mode = 'lines',
name='bid_price'))
# trace for flags
fig.add_traces(go.Scatter(x=df.index, y=df['flagged'], mode = 'markers',
marker =dict(symbol='triangle-down', size = 16),
name='Flag'))
fig.update_layout(template = 'plotly_dark')
fig.show()