Mplfinance: Date mismatch when using external axes object

[Vittorio94]

I have the following dataframe that contains ohlc data:
candles.head()

I want to plot the data using an external axes object and then add additional stuff on the chart using matplotlib. The problem is that there seems to be a mismatch in the way mplfinance and matplotlib handle dates. For example, the following code:

fig = mpf.figure(style='yahoo')
ax = fig.add_subplot(1,1,1)

mpf.plot(candles, ax=ax)

ax.plot(candles.index, candles.open)

should overlay a line connecting all the opens of the candles. However, the result is this:

Am I doing something wrong? Thank you

[DanielGoldfarb]

@Vittorio94
Vittorio,
What you are trying to do can be done easily without external axes mode. As a general rule you should avoid external axes mode, unless what you are trying to accomplish cannot be done any other way. This is because some features of mplfinance must be disabled in external axes mode and, as a user of mplfinance, you will have to do a lot more work to accomplish the same result. Even returnfig=True is preferable to external axes mode.

Here is how you should be doing what you are trying to accomplish above:

ap = mpf.make_addplot(candles.open)
mpf.plot(candles, addplot=ap, style='yahoo')

This accomplishes the same thing with half the amount of code!

---

The reason you are seeing the apparent x-axis mismatch is because you are allowing show_nontrading to default to False. When this value is False, then the x-axis values are actually the row numbers of your DataFrame ( range(0,len(candles) ) even though they are formatted as dates. In your external axes example, if you set show_nontrading=True (in the call to mpf.plot()) it will work.

Again, however, I discourage the use of external axes mode unless absolutely necessary since it requires much more code and it necessarily disables some features of mplfinance. If you explain what else you are trying to accomplish, I can let you know if there is a simple way to do it with mplfinance or if external axes are appropriate. If you have not already read it, I suggest "Adding Your Own Technical Studies to Plots."

[Vittorio94]

@DanielGoldfarb
Daniel, Thank you for the exhaustive answer. The reason why I want to use the external axes mode is that I need to add trade markers to the chart. I have tried using addplot, which works perfectly as long as there is no more than one marker per candle. However, say I want to plot an hourly chart, and I have a position that has been opened and closed within the same hour. I would have to create two separate addplots to represent the entry and exit markers. The same problem arises when a position is closed in the same candle where another position has been opened. I would have to create a different addplot for every position, which is very inconvenient!

[DanielGoldfarb]

@Vittorio94
Vittorio, Thanks for the explanation. I understand that mplfinance will not allow you to plot two markers at the same point in time unless they are in separate arrays. Perhaps that is an enhancement we can consider if there is enough demand for it.

If you don't find it inconvenient to put the markers in separate arrays, you can reduce the number of make_addplot() calls by passing a dataframe into make_addplot(), where any markers that are intended to be plotted at the same point in time are in separate columns of the data frame. If that is still inconvenient for the organization of your code, then by all means use external axes mode, but if so, I would encourage you to first try returnfig=True :

fig, axlist = mpf.plot(data,...,returnfig=True)
axlist[0].scatter(...)  # or whatever else you choose to do here to plot your markers
mpf.show()

Of course, if you find it simpler to just use external axes mode directly, then of course do that.

Thanks for you interest in mplfinance. Please, if you don't mind, post an image of your final result. I very much enjoy seeing the creative things that people are doing with mplfinance. All the best. --Daniel

[Vittorio94]

@DanielGoldfarb
Daniel, I did not know that I could return an axes list from mpf.plot(), that's great.
Using external axes mode, I was able to achieve the result I wanted:

If anyone is trying to do something similar to this, here is how I did it. I have a candles dataframe that contains ohlc data, and a positions dataframe that contains the entry and exit information of each position. Here is the output of positions.head():

After creating the figure with mplfinance, I used standard matplotlib to add scatter plots for the trade markers and line plots for the trade lines. There might be a better way to do this, but this has worked nicely for me. Here is the code:

fig, axlist = mpf.plot(candles, show_nontrading=True, returnfig=True, style="classic", type="candle")

for i in range(len(positions)):
    if positions.iloc[i].closedPL > 0:
        lineColor = "limegreen"
    else:
        lineColor = "crimson"

    axlist[0].plot(
    [positions.iloc[i].entryDateTime, positions.iloc[i].exitDateTime],
    [positions.iloc[i].entryPrice, positions.iloc[i].exitPrice],
    color=lineColor,
    linewidth=0.5,
    linestyle="dashed",
    )

    if positions.iloc[i].direction == "long":
        entryColor = "royalblue"
        exitColor = "crimson"
        axlist[0].scatter(
        [positions.iloc[i].entryDateTime, positions.iloc[i].exitDateTime],
        [positions.iloc[i].entryPrice, positions.iloc[i].exitPrice],
        c=[entryColor, exitColor],
        marker="x"
        )

    else:
        entryColor = "crimson"
        exitColor = "royalblue"
        axlist[0].scatter(
        [positions.iloc[i].entryDateTime, positions.iloc[i].exitDateTime],
        [positions.iloc[i].entryPrice, positions.iloc[i].exitPrice],
        c=[entryColor, exitColor],
        marker="x"
        )

mpf.show()

[DanielGoldfarb]

@Vittorio94
Vittorio- Great plot! And thank you for sharing the code. Nice work. All the best. --Daniel

[fxhuhn]

If you don't find it inconvenient to put the markers in separate arrays, you _can_ reduce the number of make_addplot() calls by passing a dataframe into make_addplot(), where any markers that are intended to be plotted at the same point in time are in separate columns of the data frame. If that is still inconvenient for the organization of your code, then by all means use external axes mode, but if so, I would encourage you to first try returnfig=True :

Here as a small example how it can look like if you use multiple scatter plots. Also directly on a point if you take the circles, my "donuts".

[DanielGoldfarb]

@fxhuhn
Markus- Thank you. If you don't mind, would you also be willing to share the code that generated that plot? --Daniel

[fxhuhn]

The source table contains the OHLC data and some signals. I have separated the steps so that I can still understand the code later as well.

First step is to assign the signals with the price. In my case, it is the "Low" or "High", and create a new column with them (prefix 'signal_').

        for bull_signal in ['bull_rot', 'bull_lolly', 'bull_or', 'day_croc_long', 'bull_1_solo', 'bull_blau', 'bull_grau','bull_t',  'bull_braun', 'bull_gruen', 'bull_kreuz']:
            df['signal_' + bull_signal] = df.loc[(df[bull_signal] == 1), 'Low']

        for bear_signal in ['bear_rot', 'bear_lolly','bear_or', 'day_croc_short', 'bear_1_solo', 'bear_blau', 'bear_grau','bear_t',  'bear_braun', 'bear_gruen','bear_kreuz']:
            df['signal_' + bear_signal] = df.loc[(df[bear_signal] == 1), 'High']

Second step is my dict of scatter signals. It contains my signals that i want to see in the chart ('signal' is the name of the colum and the other key/values are scatter plot related):

        day_signals = [
            {'signal': 'signal_day_croc_long', 'size': 40, 'marker': '2', 'color': 'black', 'position': -1},
            {'signal': 'signal_day_croc_short', 'size': 40, 'marker': '1', 'color': 'black', 'position': 1},

            {'signal': 'signal_bull_rot', 'size': 40, 'color': 'red', 'position': -2},
            {'signal': 'signal_bear_rot', 'size': 40, 'color': 'red', 'position': 2},

            {'signal': 'signal_bull_1_solo', 'size': 5, 'color': 'black', 'position': -2},
            {'signal': 'signal_bear_1_solo', 'size': 5, 'color': 'black', 'position': 2},

            {'signal': 'signal_bull_lolly', 'size': 5, 'color': 'white', 'position': -2},
            {'signal': 'signal_bear_lolly', 'size': 5, 'color': 'white', 'position': 2},

            {'signal': 'signal_bull_or', 'size': 40, 'color': 'orange', 'position': -3},
            {'signal': 'signal_bear_or', 'size': 40, 'color': 'orange', 'position': 3},

            {'signal': 'signal_bull_grau', 'size': 40, 'color': 'grey', 'position': -4},
            {'signal': 'signal_bear_grau', 'size': 40, 'color': 'grey', 'position': 4},

            {'signal': 'signal_bull_t', 'size': 10, 'color': 'grey', 'position': -4},
            {'signal': 'signal_bear_t', 'size': 10, 'color': 'grey', 'position': 4},

            {'signal': 'signal_bull_braun', 'size': 5, 'color': 'brown', 'position': -4},
            {'signal': 'signal_bear_braun', 'size': 5, 'color': 'brown', 'position': 4},

            {'signal': 'signal_bull_blau', 'size': 20, 'color': 'blue', 'position': -6},
            {'signal': 'signal_bear_blau', 'size': 20, 'color': 'blue', 'position': 6},

            {'signal': 'signal_bull_gruen', 'size': 20, 'color': 'green', 'position': -7},
            {'signal': 'signal_bear_gruen', 'size': 20, 'color': 'green', 'position': 7},

            {'signal': 'signal_bull_kreuz', 'size': 20, 'marker': 'P', 'color': 'green', 'position': -7},
            {'signal': 'signal_bear_kreuz', 'size': 20, 'marker': 'P', 'color': 'red', 'position': 7},
        ]

_Annotation. Calling it signal was not my best idea for readable code._

Now we come to the last step. Build the scatter plot.
Be sure, that signal is part of the chart, if it contains only NaN go to the next signal.

        for day_signal in day_signals:
            if df[day_signal['signal']].notna().sum() > 0:
                new_signal = mpf.make_addplot( df[day_signal['signal']] + day_signal['position'] * offset,
                                            scatter = True,
                                            markersize = day_signal['size'],
                                            marker = day_signal.get('marker','o'),
                                            color=day_signal['color'])
                add_plots.append(new_signal)

With the following calculation, I can ensure the appropriate spacing between signals in each chart so that there is no unnecessary overlap.
df[day_signal['signal']] + day_signal['position'] * offset

Small hint for the calculation of the offset:
offset = 0.013 *(df.High.max() - df.Low.min())

I hope this is all understandable so far. Otherwise just ask ;-)