Plotting result in TypeError: Index.get_loc() got an unexpected keyword argument 'method'

[trueToastedCode]

Expected Behavior

Plotting should work.

Actual Behavior

Fails, but only using large dataframe. >>> bt.plot()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [86], in <cell line: 1>()
----> 1 bt.plot()

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/backtesting/backtesting.py:1592, in Backtest.plot(self, results, filename, plot_width, plot_equity, plot_return, plot_pl, plot_volume, plot_drawdown, smooth_equity, relative_equity, superimpose, resample, reverse_indicators, show_legend, open_browser)
   1589         raise RuntimeError('First issue `backtest.run()` to obtain results.')
   1590     results = self._results
-> 1592 return plot(
   1593     results=results,
   1594     df=self._data,
   1595     indicators=results._strategy._indicators,
   1596     filename=filename,
   1597     plot_width=plot_width,
   1598     plot_equity=plot_equity,
   1599     plot_return=plot_return,
   1600     plot_pl=plot_pl,
   1601     plot_volume=plot_volume,
   1602     plot_drawdown=plot_drawdown,
   1603     smooth_equity=smooth_equity,
   1604     relative_equity=relative_equity,
   1605     superimpose=superimpose,
   1606     resample=resample,
   1607     reverse_indicators=reverse_indicators,
   1608     show_legend=show_legend,
   1609     open_browser=open_browser)

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/backtesting/_plotting.py:203, in plot(results, df, indicators, filename, plot_width, plot_equity, plot_return, plot_pl, plot_volume, plot_drawdown, smooth_equity, relative_equity, superimpose, resample, reverse_indicators, show_legend, open_browser)
    201 # Limit data to max_candles
    202 if is_datetime_index:
--> 203     df, indicators, equity_data, trades = _maybe_resample_data(
    204         resample, df, indicators, equity_data, trades)
    206 df.index.name = None  # Provides source name @index
    207 df['datetime'] = df.index  # Save original, maybe datetime index

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/backtesting/_plotting.py:153, in _maybe_resample_data(resample_rule, df, indicators, equity_data, trades)
    150     return f
    152 if len(trades):  # Avoid pandas "resampling on Int64 index" error
--> 153     trades = trades.assign(count=1).resample(freq, on='ExitTime', label='right').agg(dict(
    154         TRADES_AGG,
    155         ReturnPct=_weighted_returns,
    156         count='sum',
    157         EntryBar=_group_trades('EntryTime'),
    158         ExitBar=_group_trades('ExitTime'),
    159     )).dropna()
    161 return df, indicators, equity_data, trades

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/resample.py:329, in Resampler.aggregate(self, func, *args, **kwargs)
    321 @doc(
    322     _shared_docs["aggregate"],
    323     see_also=_agg_see_also_doc,
   (...)
    327 )
    328 def aggregate(self, func=None, *args, **kwargs):
--> 329     result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
    330     if result is None:
    331         how = func

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/apply.py:163, in Apply.agg(self)
    160     return self.apply_str()
    162 if is_dict_like(arg):
--> 163     return self.agg_dict_like()
    164 elif is_list_like(arg):
    165     # we require a list, but not a 'str'
    166     return self.agg_list_like()

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/apply.py:420, in Apply.agg_dict_like(self)
    417         results = {key: colg.agg(how) for key, how in arg.items()}
    418     else:
    419         # key used for column selection and output
--> 420         results = {
    421             key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()
    422         }
    424 # set the final keys
    425 keys = list(arg.keys())

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/apply.py:421, in <dictcomp>(.0)
    417         results = {key: colg.agg(how) for key, how in arg.items()}
    418     else:
    419         # key used for column selection and output
    420         results = {
--> 421             key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()
    422         }
    424 # set the final keys
    425 keys = list(arg.keys())

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/groupby/generic.py:269, in SeriesGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
    266     return self._python_agg_general(func, *args, **kwargs)
    268 try:
--> 269     return self._python_agg_general(func, *args, **kwargs)
    270 except KeyError:
    271     # KeyError raised in test_groupby.test_basic is bc the func does
    272     #  a dictionary lookup on group.name, but group name is not
    273     #  pinned in _python_agg_general, only in _aggregate_named
    274     result = self._aggregate_named(func, *args, **kwargs)

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/groupby/generic.py:288, in SeriesGroupBy._python_agg_general(self, func, *args, **kwargs)
    285 f = lambda x: func(x, *args, **kwargs)
    287 obj = self._obj_with_exclusions
--> 288 result = self.grouper.agg_series(obj, f)
    289 res = obj._constructor(result, name=obj.name)
    290 return self._wrap_aggregated_output(res)

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/groupby/ops.py:994, in BaseGrouper.agg_series(self, obj, func, preserve_dtype)
    987 if len(obj) > 0 and not isinstance(obj._values, np.ndarray):
    988     # we can preserve a little bit more aggressively with EA dtype
    989     #  because maybe_cast_pointwise_result will do a try/except
    990     #  with _from_sequence.  NB we are assuming here that _from_sequence
    991     #  is sufficiently strict that it casts appropriately.
    992     preserve_dtype = True
--> 994 result = self._aggregate_series_pure_python(obj, func)
    996 npvalues = lib.maybe_convert_objects(result, try_float=False)
    997 if preserve_dtype:

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/groupby/ops.py:1015, in BaseGrouper._aggregate_series_pure_python(self, obj, func)
   1012 splitter = self._get_splitter(obj, axis=0)
   1014 for i, group in enumerate(splitter):
-> 1015     res = func(group)
   1016     res = libreduction.extract_result(res)
   1018     if not initialized:
   1019         # We only do this validation on the first iteration

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/pandas/core/groupby/generic.py:285, in SeriesGroupBy._python_agg_general.<locals>.<lambda>(x)
    283 def _python_agg_general(self, func, *args, **kwargs):
    284     func = com.is_builtin_func(func)
--> 285     f = lambda x: func(x, *args, **kwargs)
    287     obj = self._obj_with_exclusions
    288     result = self.grouper.agg_series(obj, f)

File ~/miniconda3/envs/tensorflow/lib/python3.10/site-packages/backtesting/_plotting.py:148, in _maybe_resample_data.<locals>._group_trades.<locals>.f(s, new_index, bars)
    145 if s.size:
    146     # Via int64 because on pandas recently broken datetime
    147     mean_time = int(bars.loc[s.index].view(int).mean())
--> 148     new_bar_idx = new_index.get_loc(mean_time, method='nearest')
    149     return new_bar_idx

TypeError: Index.get_loc() got an unexpected keyword argument 'method'

Steps to Reproduce

def CLOSE():
    return df.Close

def ATR():
    return df.ATR

def SIGNAL():
    return df.TRADE_SIGNAL_0

class MyStrategy(Strategy):
    def init(self):
        super().init()
        self.close = self.I(CLOSE)
        self.atr = self.I(ATR)
        self.signal = self.I(SIGNAL)

    def next(self):
        global pbar
        super().next()
        pbar.update(1)

        if self.trades:
            return

        if self.signal == LONG_SIGNAL:
            sl = self.close - self.atr * SL_FACTOR
            tp = self.close + self.atr * SL_FACTOR * TP_FACTOR

            self.buy(sl=sl, tp=tp, size=0.99)

pbar = tqdm(total=len(df))
bt = Backtest(df, MyStrategy, cash=100_000, margin=1/1, commission=0.0)
stats = bt.run()
print(stats)
bt.plot()

[ ! ] Error only occurs when using huge dataframe, in my case it had a length of 499197. Same code however worked when making it smaller.

Additional info

• Backtesting version: 0.3.3
• bokeh.__version__: 2.4.3
• OS: macOS Ventura (arm64)

[vladGriguta]

hey @trueToastedCode, to save you some time (which I spent digging into this), this issue has been addressed by this commit. Although it is not released, you can just install the most recent version of the library from here

[rbarton29]

I am getting the same issue on a large dataframe.

When I shrink the dataframe (~1,000 rows), I get a different error message: TypeError: bokeh.models.tools.Toolbar() got multiple values for keyword argument 'logo'

It seems with the latter issue I am also limited by the bokeh developers to release a bug fix.

I have Mac OS Ventura 13.5, M1 chip, Backtesting 0.3.3, Bokeh 3.2.2

[valentinmk]

To install latest version from repo instead of PYPI, please find instruction in here: https://github.com/kernc/backtesting.py/issues/987#issuecomment-1571602520