When I run

btalib.dx(btc_df.high, btc_df.low, btc_df.close)

or

btalib.dm(btc_df.high, btc_df.low)

with btc_df.high, btc_df.low and btc_df.close being columns from a pandas data frame (series of floats).

I get:

TypeError Traceback (most recent call last)
in
1 # import talib
2
----> 3 btalib.dx(btc_df.high, btc_df.low, btc_df.close)

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/btalib/indicator.py in call(cls, *args, **kwargs)
150 # Auto-call base classes
151 for b_init in reversed(list(dict.fromkeys(b.init for b in bases))):
--> 152 b_init(self, *args, **kwargs)
153
154 # delete old aliases only meant for operational purposes

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/btalib/indicators/directionalmove.py in init(self)
95 if self._plus: # upvmoe > 0 and where upmove > downmove
96 pdm = upmove.clip(lower=0.0) * (upmove > downmove)
---> 97 self._pdm = self._smoother(pdm, **smoothargs)
98
99 if self._minus: # downmove > 0 and where downmove > upmove

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/btalib/indicator.py in call(cls, *args, **kwargs)
150 # Auto-call base classes
151 for b_init in reversed(list(dict.fromkeys(b.init for b in bases))):
--> 152 b_init(self, *args, **kwargs)
153
154 # delete old aliases only meant for operational purposes

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/btalib/indicators/directionalmove.py in init(self)
50 p = self.p.period
51 _ewm = self.i0._ewm(span=p, _pearly=self.p._pearly, _seed=self.p._seed)
---> 52 self.o.smacc = _ewm._lfilter(alpha=1.0, beta=(p - 1) / p)
53
54 def _talib(self, kwdict):

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/btalib/meta/lines.py in _lfilter(self, alpha, beta)
341 return scipy.signal.lfilter([alpha], [1.0, -beta], x)
342
--> 343 return self._apply(_sp_lfilter) # trigger getattr for _apply
344
345 def _mean(self): # meant for ewm with dynamic alpha

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/btalib/meta/lines.py in call_op(*args, **kwargs)
377 sargs.append(arg)
378
--> 379 result[self._minidx:] = r = op(*sargs, **kwargs) # run/store
380 result = result.astype(r.dtype, copy=False)
381 return self._line._clone(result, period=self._minperiod)

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/pandas/core/window/rolling.py in _apply(self, func, name, numba_cache_key, **kwargs)
467 return result
468
--> 469 return self._apply_blockwise(homogeneous_func, name)
470
471 def aggregate(self, func, *args, **kwargs):

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/pandas/core/window/rolling.py in _apply_blockwise(self, homogeneous_func, name)
382 """
383 if self._selected_obj.ndim == 1:
--> 384 return self._apply_series(homogeneous_func, name)
385
386 obj = self._create_data(self._selected_obj)

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/pandas/core/window/rolling.py in _apply_series(self, homogeneous_func, name)
371 raise DataError("No numeric types to aggregate") from err
372
--> 373 result = homogeneous_func(values)
374 return obj._constructor(result, index=obj.index, name=obj.name)
375

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/pandas/core/window/rolling.py in homogeneous_func(values)
459 result = np.apply_along_axis(calc, self.axis, values)
460 else:
--> 461 result = calc(values)
462 result = np.asarray(result)
463

~/Documents/external_projects/trading/venv/lib/python3.8/site-packages/pandas/core/window/rolling.py in calc(x)
453 closed=self.closed,
454 )
--> 455 return func(x, start, end, min_periods)
456
457 with np.errstate(all="ignore"):

TypeError: _sp_lfilter() takes 1 positional argument but 4 were given

Python version 3.8.2

Package versions:

bta-lib 1.0.0
numpy 1.19.5
pandas 1.2.1
scipy 1.6.0