How to show alternative calendar dates in mplfinance? - python-3.x

Question

TL;DR - The issue
I have an mplfinance plot based on a pandas dataframe in which the indices are in Georgian calendar format and I need to have them displayed as Jalali format.
My data and code
My data looks like this:
open high low close
date
2021-03-15 67330.0 69200.0 66870.0 68720.0
2021-03-16 69190.0 71980.0 69000.0 71620.0
2021-03-17 72450.0 73170.0 71700.0 71820.0
2021-03-27 71970.0 73580.0 70000.0 73330.0
2021-03-28 73330.0 73570.0 71300.0 71850.0
... ... ... ... ...
The first column is both a date and the index. This is required by mplfinance plot the data correctly;
Which I can plot with something like this:
import mplfinance as mpf
mpf.plot(chart_data.tail(7), figratio=(16,9), type="candle", style='yahoo', ylabel='', tight_layout=True, xrotation=90)
Where chart_data is the data above and the rest are pretty much formatting stuff.
What I have now
My chart looks like this:
However, the I need the dates to look like this: 1400-01-12. Here's a table of equivalence to further demonstrate my case.
2021-03-15 1399-12-25
2021-03-16 1399-12-26
2021-03-17 1399-12-27
2021-03-27 1400-01-07
2021-03-28 1400-01-08
What I've tried
Setting Jdates as my indices:
chart_data.index = history.jdate
mpf.plot(chart_data_j)
Throws this exception:
TypeError('Expect data.index as DatetimeIndex')
So I tried converting the jdates into datetimes:
chart_data_j.index = pd.to_datetime(history.jdate)
Which threw an out of bounds exception:
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1398-03-18 00:00:00
So I though maybe changing the timezone/locale would be an option, so I tried changing the timezones, following the official docs:
pd.to_datetime(history.date).tz_localize(tz='US/Eastern')
But I got this exception:
raise TypeError(f"{ax_name} is not a valid DatetimeIndex or PeriodIndex")
And finally I tried using libraries such as PersianTools and pandas_jalali to no avail.

Answer 1

You can get this to work by creating your own custom DateFormatter class, and using mpf.plot() kwarg returnfig=True to gain access to the Axes objects to be able to install your own custom DateFormatter.
I have written a custom DateFormatter (see code below) that is aware of the special way that MPLfinance handles the x-axis when show_nontrading=False (i.e. the default value).
import pandas as pd
import mplfinance as mpf
import jdatetime as jd
import matplotlib.dates as mdates
from matplotlib.ticker import Formatter
class JalaliDateTimeFormatter(Formatter):
"""
Formatter for JalaliDate in mplfinance.
Handles both `show_nontrading=False` and `show_nontrading=True`.
When show_nonntrading=False, then the x-axis is indexed by an
integer representing the row number in the dataframe, thus:
Formatter for axis that is indexed by integer, where the integers
represent the index location of the datetime object that should be
formatted at that lcoation. This formatter is used typically when
plotting datetime on an axis but the user does NOT want to see gaps
where days (or times) are missing. To use: plot the data against
a range of integers equal in length to the array of datetimes that
you would otherwise plot on that axis. Construct this formatter
by providing the arrange of datetimes (as matplotlib floats). When
the formatter receives an integer in the range, it will look up the
datetime and format it.
"""
def __init__(self, dates=None, fmt='%b %d, %H:%M', show_nontrading=False):
self.dates = dates
self.len = len(dates) if dates is not None else 0
self.fmt = fmt
self.snt = show_nontrading
def __call__(self, x, pos=0):
'''
Return label for time x at position pos
'''
if self.snt:
jdate = jd.date.fromgregorian(date=mdates.num2date(x))
formatted_date = jdate.strftime(self.fmt)
return formatted_date
ix = int(round(x,0))
if ix >= self.len or ix < 0:
date = None
formatted_date = ''
else:
date = self.dates[ix]
jdate = jd.date.fromgregorian(date=mdates.num2date(date))
formatted_date = jdate.strftime(self.fmt)
return formatted_date
# ---------------------------------------------------
df = pd.read_csv('so_67001540.csv',index_col=0,parse_dates=True)
mpf.plot(df,figratio=(16,9),type="candle",style='yahoo',ylabel='',xrotation=90)
dates = [mdates.date2num(d) for d in df.index]
formatter = JalaliDateTimeFormatter(dates=dates,fmt='%Y-%m-%d')
fig, axlist = mpf.plot(df,figratio=(16,9),
type="candle",style='yahoo',
ylabel='',xrotation=90,
returnfig=True)
axlist[0].xaxis.set_major_formatter(formatter)
mpf.show()
The file 'so_67001540.csv' looks like this:
date,open,high,low,close,alt_date
2021-03-15,67330.0,69200.0,66870.0,68720.0,1399-12-25
2021-03-16,69190.0,71980.0,69000.0,71620.0,1399-12-26
2021-03-17,72450.0,73170.0,71700.0,71820.0,1399-12-27
2021-03-27,71970.0,73580.0,70000.0,73330.0,1400-01-07
2021-03-28,73330.0,73570.0,71300.0,71850.0,1400-01-08
When you run the above script, you should get the following two plots:

Answer 2

Have you tried making these dates
1399-12-25
1399-12-26
1399-12-27
1400-01-07
1400-01-08
the index of the dataframe (maybe that's what you mean by "swapping the indices"?) and set kwarg datetime_format='%Y-%m-%d' ?
I think that should work.
UPDATE:
It appears to me that the problem is that
mplfinace requires a Pandas.DatetimeIndex as the index of your dataframe, and
Pandas.DatetimeIndex is made up of Pandas.Timestamp objects, and
Pandas.Timestamp has limits which preclude dates having years less than 1677:
In [1]: import pandas as pd
In [2]: pd.Timestamp.max
Out[2]: Timestamp('2262-04-11 23:47:16.854775807')
In [3]: pd.Timestamp.min
Out[3]: Timestamp('1677-09-21 00:12:43.145225')
I am going to poke around and see if I can come up with another solution. Internally Matplotlib dates can go to year zero.