Plotting Multiple Candlestick charts on different pane

[guanm326]

Not sure where to ask questions. I have 2 dataframes with separate indices (intraday OHLC and daily OHLC), how do I get the second pane to plot the daily OHLC aligned correct to the first panes intraday OHLC?

thanks

[highfestiva]

Good one, I did think about it but never got around to fixing that. I'll have something in a couple of weeks. In the mean while you can disconnect the two axes by severing the x-link: ax2.setXLink(None).

[highfestiva]

c0f7c1e fixes that. Perfect alignment might sometimes be difficult to achieve, as different months, days and time zones play a role in stock markets (that close).

Note that you should remove all margins in order to get perfect alignment.

import finplot as fplt
import yfinance as yf

df = yf.download('ETH-USD', '2020-07-10', '2020-09-02', interval='90m') # market that doesn't close

# no margins
fplt.right_margin_candles = 0
fplt.side_margin = 0
ax1,ax2 = fplt.create_plot(rows=2)

# first plot shows 90m candles
fplt.candlestick_ochl(df[['Open','Close','High','Low']], ax=ax1)

# second plot shows daily
dfd = df.Open.resample('D').first().to_frame()
dfd['Close'] = df.Close.resample('D').last()
dfd['High'] = df.High.resample('D').max()
dfd['Low'] = df.Low.resample('D').min()
fplt.candlestick_ochl(dfd.dropna(), ax=ax2)

fplt.show()

[highfestiva]

One other options I should mention, which I kinda like myself is plotting them both on the same axis, like so:

It's easy to do: just download, resample and plot the background first, in this case five 90-minute per business day. We offset the base candle, since resample puts it at start of day by default. Plot the high-res version on top. Done.

#!/usr/bin/env python3

import finplot as fplt
import yfinance as yf

df = yf.download('GOOG', '2020-07-10', '2020-09-03', interval='90m')

dfd = df.Open.resample('D').first().to_frame()
dfd['Close'] = df.Close.resample('D').last()
dfd['High'] = df.High.resample('D').max()
dfd['Low'] = df.Low.resample('D').min()

daily_plot = fplt.candlestick_ochl(dfd.dropna(), candle_width=5)
daily_plot.colors.update(dict(bull_body='#bfb', bull_shadow='#ada', bear_body='#fbc', bear_shadow='#dab'))
daily_plot.x_offset = 3.1 # resample() gets us start of day

fplt.candlestick_ochl(df[['Open','Close','High','Low']])

fplt.show()

[guanm326]

Hello somehow I didn't get any notification of you reply, apologies. I tried your first solution but I am getting back green candles without any wicks at all on my dataset....

                        Open     High      Low    Close    Volume   OpenUS   HighUS    LowUS  CloseUS   VolumeUS
Datetime                                                                                                        
2020-09-18 13:45:00  3301.50  3304.75  3297.00  3298.75   28211.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 14:00:00  3298.75  3306.50  3298.00  3306.25   35531.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 14:15:00  3306.25  3311.75  3305.00  3307.50   38635.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 14:30:00  3307.75  3313.75  3304.25  3313.50   36680.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 14:45:00  3313.25  3316.75  3309.25  3312.75   51666.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 15:00:00  3313.00  3316.25  3306.25  3307.50  147701.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 15:15:00  3307.75  3317.50  3305.25  3317.25   78692.0  3354.75  3357.25  3280.75  3317.25  1976121.0
2020-09-18 15:30:00  3317.25  3317.25  3317.25  3317.25       6.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 15:45:00  3317.00  3317.25  3314.25  3316.50    5766.0      NaN      NaN      NaN      NaN        NaN
2020-09-18 16:00:00  3316.50  3318.00  3314.50  3317.25    3714.0      NaN      NaN      NaN      NaN        NaN

I use the following code with added 0 margins to test out:

    # Visualization
    import finplot as fplt

    # no margins
    fplt.right_margin_candles = 0
    fplt.side_margin = 0

    mat = feat.data_frame.tail(1000)
    ax, ax2 = fplt.create_plot("Custom", rows=2)
    fplt.candlestick_ochl(mat[['Open', 'High', 'Low', 'Close']].dropna())
    fplt.candlestick_ochl(mat[['OpenUS', 'HighUS', 'LowUS', 'CloseUS']], ax=ax2)
    fplt.show()

But once I plot it, I get

I am totally confused to what happened to the wicks...but when I run your example, it works...

[highfestiva]

Ah, you've flipped order of the columns! It's called candlestick_ochl, as in Open, Close, High, Low. But you've provided them in the order Open, High, Low, Close. Right order:

fplt.candlestick_ochl(mat[['OpenUS', 'CloseUS', 'HighUS', 'LowUS']].dropna(), candle_width=10)
fplt.candlestick_ochl(mat[['Open', 'Close', 'High', 'Low']])
fplt.show()

gets me closer to what you're expecting:

Set some colors and you're good to go! Notice that you should plot your low-res version first to get it underneath, then the higher-resolution one on top. (Also .dropna() was meant on the low-res data, but you can probably leave that out.)

[guanm326]

LOL nice! totally didn't see that! thanks so much for quick response, finplot is by far the best interactive plot out there....no joke

[highfestiva]

Thanks for the nice feedback! Feels good to develop stuff for the py community when the response goes like this. ;D Skickat från min iPhone

…

19 sep. 2020 kl. 18:33 skrev Mike ***@***.***>: LOL nice! totally didn't see that! thanks so much for quick response, finplot is by far the best interactive plot out there....no joke — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub, or unsubscribe.