How to add lines with annotations to candlestick charts when some values are missing?

Problem seems the missing values, plotly has difficulty with. With this trick you can only plot the point;

has_value = ~df["Pivot Price"].isna()

import plotly.graph_objects as go
import pandas as pd
from datetime import datetime

df=pd.read_csv("notebooks/for_so.csv")
fig = go.Figure(data=[go.Candlestick(x=df['Date'],
                open=df['Open'],
                high=df['High'],
                low=df['Low'],
                close=df['Close'])])

fig.add_trace(
    go.Scatter(mode = 'lines',
               x=df[has_value]['Date'],
        y=df[has_value]["Pivot Price"], line={'color':'black', 'width':1}
    ))

fig.add_trace(
    go.Scatter(mode = "markers",
        x=df['Date'],
        y=df["Pivot Price"]
    ))

fig.update_layout(
    autosize=False,
    width=1000,
    height=800,)

fig.show()

This did it for me.

The problem seems to be the missing values. So just use pandas.Series.interpolate in combination with fig.add_annotation to get:

I've included annotations for differences as well. There are surely more elegant ways to do it than with for loops, but it does the job. Let me know if anything is unclear!

import pandas as pd
import numpy as np
import plotly.graph_objects as go

df = pd.DataFrame(
{'index': {0: 0,
  1: 1,
  2: 2,
  3: 3,
  4: 4,
  5: 5,
  6: 6,
  7: 7,
  8: 8,
  9: 9,
  10: 10,
  11: 11,
  12: 12,
  13: 13,
  14: 14,
  15: 15,
  16: 16,
  17: 17,
  18: 18,
  19: 19,
  20: 20,
  21: 21,
  22: 22,
  23: 23,
  24: 24},
 'Date': {0: '2018-09-03',
  1: '2018-09-04',
  2: '2018-09-05',
  3: '2018-09-06',
  4: '2018-09-07',
  5: '2018-09-10',
  6: '2018-09-11',
  7: '2018-09-12',
  8: '2018-09-13',
  9: '2018-09-14',
  10: '2018-09-17',
  11: '2018-09-18',
  12: '2018-09-19',
  13: '2018-09-20',
  14: '2018-09-21',
  15: '2018-09-24',
  16: '2018-09-25',
  17: '2018-09-26',
  18: '2018-09-27',
  19: '2018-09-28',
  20: '2018-10-01',
  21: '2018-10-02',
  22: '2018-10-03',
  23: '2018-10-04',
  24: '2018-10-05'},
 'Open': {0: 1.2922067642211914,
  1: 1.2867859601974487,
  2: 1.2859420776367188,
  3: 1.2914056777954102,
  4: 1.2928247451782229,
  5: 1.292808175086975,
  6: 1.3027958869934082,
  7: 1.3017443418502808,
  8: 1.30451238155365,
  9: 1.3110626935958862,
  10: 1.3071041107177734,
  11: 1.3146650791168213,
  12: 1.3166556358337402,
  13: 1.3140604496002195,
  14: 1.3271400928497314,
  15: 1.3080958127975464,
  16: 1.3117163181304932,
  17: 1.3180439472198486,
  18: 1.3169677257537842,
  19: 1.3077707290649414,
  20: 1.3039510250091553,
  21: 1.3043931722640991,
  22: 1.2979763746261597,
  23: 1.2941633462905884,
  24: 1.3022021055221558},
 'High': {0: 1.2934937477111816,
  1: 1.2870012521743774,
  2: 1.2979259490966797,
  3: 1.2959914207458496,
  4: 1.3024225234985352,
  5: 1.3052103519439695,
  6: 1.30804443359375,
  7: 1.3044441938400269,
  8: 1.3120088577270508,
  9: 1.3143367767333984,
  10: 1.3156682252883911,
  11: 1.3171066045761108,
  12: 1.3211784362792969,
  13: 1.3296104669570925,
  14: 1.3278449773788452,
  15: 1.3166556358337402,
  16: 1.3175750970840454,
  17: 1.3196094036102295,
  18: 1.3180439472198486,
  19: 1.3090718984603882,
  20: 1.3097577095031738,
  21: 1.3049719333648682,
  22: 1.3020155429840088,
  23: 1.3036959171295166,
  24: 1.310753345489502},
 'Low': {0: 1.2856279611587524,
  1: 1.2813942432403564,
  2: 1.2793285846710205,
  3: 1.289723515510559,
  4: 1.2918561697006226,
  5: 1.289823293685913,
  6: 1.2976733446121216,
  7: 1.298414707183838,
  8: 1.3027619123458862,
  9: 1.3073604106903076,
  10: 1.3070186376571655,
  11: 1.3120776414871216,
  12: 1.3120431900024414,
  13: 1.3140085935592651,
  14: 1.305841088294983,
  15: 1.3064552545547483,
  16: 1.3097233772277832,
  17: 1.3141123056411743,
  18: 1.309706211090088,
  19: 1.3002548217773438,
  20: 1.3014055490493774,
  21: 1.2944146394729614,
  22: 1.2964619398117063,
  23: 1.2924572229385376,
  24: 1.3005592823028564},
 'Close': {0: 1.292306900024414,
  1: 1.2869019508361816,
  2: 1.2858428955078125,
  3: 1.2914891242980957,
  4: 1.2925406694412231,
  5: 1.2930254936218262,
  6: 1.302643060684204,
  7: 1.3015578985214231,
  8: 1.304546356201172,
  9: 1.311131477355957,
  10: 1.307326316833496,
  11: 1.3146305084228516,
  12: 1.3168463706970217,
  13: 1.3141123056411743,
  14: 1.327087163925171,
  15: 1.30804443359375,
  16: 1.3117333650588991,
  17: 1.3179919719696045,
  18: 1.3172800540924072,
  19: 1.3078734874725342,
  20: 1.3039000034332275,
  21: 1.3043591976165771,
  22: 1.2981956005096436,
  23: 1.294062852859497,
  24: 1.3024225234985352},
 'Pivot Price': {0: 1.2934937477111816,
  1: np.nan,
  2: 1.2793285846710205,
  3: np.nan,
  4: np.nan,
  5: np.nan,
  6: np.nan,
  7: np.nan,
  8: np.nan,
  9: np.nan,
  10: np.nan,
  11: np.nan,
  12: np.nan,
  13: 1.3296104669570925,
  14: np.nan,
  15: np.nan,
  16: np.nan,
  17: np.nan,
  18: np.nan,
  19: np.nan,
  20: np.nan,
  21: np.nan,
  22: np.nan,
  23: 1.2924572229385376,
  24: np.nan}})

import plotly.graph_objects as go
import pandas as pd
from datetime import datetime

# df=pd.read_csv("for_so.csv")
fig = go.Figure(data=[go.Candlestick(x=df['Date'],
# fig = go.Figure(data=[go.Candlestick(x=df.index,
                open=df['Open'],
                high=df['High'],
                low=df['Low'],
                close=df['Close'])])

# some calculations
df_diff = df['Pivot Price'].dropna().diff().copy()
df2 = df[df.index.isin(df_diff.index)].copy()
df2['Price Diff'] = df['Pivot Price'].dropna().values

fig.add_trace(
    go.Scatter(mode = "lines+markers",
        x=df['Date'],
        y=df["Pivot Price"]
    ))

fig.update_layout(
    autosize=False,
    width=1000,
    height=800,)

fig.add_trace(go.Scatter(x=df['Date'], y=df['Pivot Price'].interpolate(),
# fig.add_trace(go.Scatter(x=df.index, y=df['Pivot Price'].interpolate(),
                         mode = 'lines',
                         line = dict(color='black')))


def annot(value):
#     print(type(value))
    if np.isnan(value):
        return ''
    else:
        return value
    

j = 0
for i, p in enumerate(df['Pivot Price']):
#      print(p)
#     if not np.isnan(p) and not np.isnan(df_diff.iloc[j]):
    if not np.isnan(p):
#         print(not np.isnan(df_diff.iloc[j]))
        
        fig.add_annotation(dict(font=dict(color='rgba(0,0,200,0.8)',size=12),
                                        x=df['Date'].iloc[i],
#                                          x=df.index[i],
#                                         x = xStart
                                        y=p,
                                        showarrow=False,
                                        text=annot(round(abs(df_diff.iloc[j]),3)),
                                        textangle=0,
                                        xanchor='right',
                                        xref="x",
                                        yref="y"))
        j = j + 1
fig.update_xaxes(type='category')
fig.show()