# -*- coding: utf-8 -*-
from pandas import DataFrame
from .obv import obv
from pandas_ta.overlap import ma
from pandas_ta.trend import long_run, short_run
from pandas_ta.utils import get_offset, verify_series
[docs]def aobv(close, volume, fast=None, slow=None, max_lookback=None, min_lookback=None, mamode=None, offset=None, **kwargs):
"""Indicator: Archer On Balance Volume (AOBV)"""
# Validate arguments
fast = int(fast) if fast and fast > 0 else 4
slow = int(slow) if slow and slow > 0 else 12
max_lookback = int(max_lookback) if max_lookback and max_lookback > 0 else 2
min_lookback = int(min_lookback) if min_lookback and min_lookback > 0 else 2
if slow < fast:
fast, slow = slow, fast
mamode = mamode if isinstance(mamode, str) else "ema"
_length = max(fast, slow, max_lookback, min_lookback)
close = verify_series(close, _length)
volume = verify_series(volume, _length)
offset = get_offset(offset)
if "length" in kwargs: kwargs.pop("length")
run_length = kwargs.pop("run_length", 2)
if close is None or volume is None: return
# Calculate Result
obv_ = obv(close=close, volume=volume, **kwargs)
maf = ma(mamode, obv_, length=fast, **kwargs)
mas = ma(mamode, obv_, length=slow, **kwargs)
# When MAs are long and short
obv_long = long_run(maf, mas, length=run_length)
obv_short = short_run(maf, mas, length=run_length)
# Offset
if offset != 0:
obv_ = obv_.shift(offset)
maf = maf.shift(offset)
mas = mas.shift(offset)
obv_long = obv_long.shift(offset)
obv_short = obv_short.shift(offset)
# # Handle fills
if "fillna" in kwargs:
obv_.fillna(kwargs["fillna"], inplace=True)
maf.fillna(kwargs["fillna"], inplace=True)
mas.fillna(kwargs["fillna"], inplace=True)
obv_long.fillna(kwargs["fillna"], inplace=True)
obv_short.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
obv_.fillna(method=kwargs["fill_method"], inplace=True)
maf.fillna(method=kwargs["fill_method"], inplace=True)
mas.fillna(method=kwargs["fill_method"], inplace=True)
obv_long.fillna(method=kwargs["fill_method"], inplace=True)
obv_short.fillna(method=kwargs["fill_method"], inplace=True)
# Prepare DataFrame to return
_mode = mamode.lower()[0] if len(mamode) else ""
data = {
obv_.name: obv_,
f"OBV_min_{min_lookback}": obv_.rolling(min_lookback).min(),
f"OBV_max_{max_lookback}": obv_.rolling(max_lookback).max(),
f"OBV{_mode}_{fast}": maf,
f"OBV{_mode}_{slow}": mas,
f"AOBV_LR_{run_length}": obv_long,
f"AOBV_SR_{run_length}": obv_short,
}
aobvdf = DataFrame(data)
# Name and Categorize it
aobvdf.name = f"AOBV{_mode}_{fast}_{slow}_{min_lookback}_{max_lookback}_{run_length}"
aobvdf.category = "volume"
return aobvdf