def obv(close, volume, talib=None, offset=None, **kwargs): """Indicator: On Balance Volume (OBV)""" # Validate arguments close = verify_series(close) volume = verify_series(volume) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True # Calculate Result if Imports["talib"] and mode_tal: from talib import OBV obv = OBV(close, volume) else: signed_volume = signed_series(close, initial=1) * volume obv = signed_volume.cumsum() # Offset if offset != 0: obv = obv.shift(offset) # Handle fills if "fillna" in kwargs: obv.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: obv.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it obv.name = f"OBV" obv.category = "volume" return obv
def nvi(close, volume, length=None, initial=None, offset=None, **kwargs): """Indicator: Negative Volume Index (NVI)""" # Validate arguments close = verify_series(close) volume = verify_series(volume) length = int(length) if length and length > 0 else 1 min_periods = int( kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[ "min_periods"] is not None else length initial = int(initial) if initial and initial > 0 else 1000 offset = get_offset(offset) # Calculate Result roc_ = roc(close=close, length=length) signed_volume = signed_series(volume, initial=1) nvi = signed_volume[signed_volume < 0].abs() * roc_ nvi.fillna(0, inplace=True) nvi.iloc[0] = initial nvi = nvi.cumsum() # Offset if offset != 0: nvi = nvi.shift(offset) # Handle fills if "fillna" in kwargs: nvi.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: nvi.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it nvi.name = f"NVI_{length}" nvi.category = "volume" return nvi
def pvol(close, volume, offset=None, **kwargs): """Indicator: Price-Volume (PVOL)""" # Validate arguments close = verify_series(close) volume = verify_series(volume) offset = get_offset(offset) signed = kwargs.pop("signed", False) # Calculate Result if signed: pvol = signed_series(close, 1) * close * volume else: pvol = close * volume # Offset if offset != 0: pvol = pvol.shift(offset) # Handle fills if "fillna" in kwargs: pvol.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: pvol.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it pvol.name = f"PVOL" pvol.category = "volume" return pvol
def pvi(close, volume, length=None, initial=None, offset=None, **kwargs): """Indicator: Positive Volume Index (PVI)""" # Validate arguments length = int(length) if length and length > 0 else 1 # min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length initial = int(initial) if initial and initial > 0 else 1000 close = verify_series(close, length) volume = verify_series(volume, length) offset = get_offset(offset) if close is None or volume is None: return # Calculate Result signed_volume = signed_series(volume, 1) pvi = roc(close=close, length=length) * signed_volume[signed_volume > 0].abs() pvi.fillna(0, inplace=True) pvi.iloc[0] = initial pvi = pvi.cumsum() # Offset if offset != 0: pvi = pvi.shift(offset) # Handle fills if "fillna" in kwargs: pvi.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: pvi.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it pvi.name = f"PVI_{length}" pvi.category = "volume" return pvi
def obv(close, volume, offset=None, **kwargs): """Indicator: On Balance Volume (OBV)""" # Validate arguments close = verify_series(close) volume = verify_series(volume) offset = get_offset(offset) # Calculate Result signed_volume = signed_series(close, initial=1) * volume obv = signed_volume.cumsum() # Offset if offset != 0: obv = obv.shift(offset) # Handle fills if "fillna" in kwargs: obv.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: obv.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it obv.name = f"OBV" obv.category = "volume" return obv
def kvo(high, low, close, volume, fast=None, slow=None, signal=None, mamode=None, drift=None, offset=None, **kwargs): """Indicator: Klinger Volume Oscillator (KVO)""" # Validate arguments fast = int(fast) if fast and fast > 0 else 34 slow = int(slow) if slow and slow > 0 else 55 signal = int(signal) if signal and signal > 0 else 13 mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema" _length = max(fast, slow, signal) high = verify_series(high, _length) low = verify_series(low, _length) close = verify_series(close, _length) volume = verify_series(volume, _length) drift = get_drift(drift) offset = get_offset(offset) if high is None or low is None or close is None or volume is None: return # Calculate Result signed_volume = volume * signed_series(hlc3(high, low, close), 1) sv = signed_volume.loc[signed_volume.first_valid_index():, ] kvo = ma(mamode, sv, length=fast) - ma(mamode, sv, length=slow) kvo_signal = ma(mamode, kvo.loc[kvo.first_valid_index():, ], length=signal) # Offset if offset != 0: kvo = kvo.shift(offset) kvo_signal = kvo_signal.shift(offset) # Handle fills if "fillna" in kwargs: kvo.fillna(kwargs["fillna"], inplace=True) kvo_signal.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: kvo.fillna(method=kwargs["fill_method"], inplace=True) kvo_signal.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = f"_{fast}_{slow}_{signal}" kvo.name = f"KVO{_props}" kvo_signal.name = f"KVOs{_props}" kvo.category = kvo_signal.category = "volume" # Prepare DataFrame to return data = {kvo.name: kvo, kvo_signal.name: kvo_signal} df = DataFrame(data) df.name = f"KVO{_props}" df.category = kvo.category return df
def vp(close, volume, width=None, **kwargs): """Indicator: Volume Profile (VP)""" # Validate arguments width = int(width) if width and width > 0 else 10 close = verify_series(close, width) volume = verify_series(volume, width) sort_close = kwargs.pop("sort_close", False) if close is None or volume is None: return # Setup signed_volume = signed_series(volume, initial=1) pos_volume = signed_volume[signed_volume > 0] * volume neg_volume = signed_volume[signed_volume < 0] * -volume vp = concat([close, pos_volume, neg_volume], axis=1) close_col = f"{vp.columns[0]}" high_price_col = f"high_{close_col}" low_price_col = f"low_{close_col}" mean_price_col = f"mean_{close_col}" mid_price_col = f"mid_{close_col}" volume_col = f"{vp.columns[1]}" pos_volume_col = f"pos_{volume_col}" neg_volume_col = f"neg_{volume_col}" total_volume_col = f"total_{volume_col}" vp.columns = [close_col, pos_volume_col, neg_volume_col] # sort_close: Sort by close before splitting into ranges. Default: False # If False, it sorts by date index or chronological versus by price if sort_close: vp.sort_values(by=[close_col], inplace=True) # Calculate Result vp_ranges = array_split(vp, width) result = ({ low_price_col: r[close_col].min(), mean_price_col: r[close_col].mean(), high_price_col: r[close_col].max(), pos_volume_col: r[pos_volume_col].sum(), neg_volume_col: r[neg_volume_col].sum(), } for r in vp_ranges) vpdf = DataFrame(result) vpdf[total_volume_col] = vpdf[pos_volume_col] + vpdf[neg_volume_col] # Handle fills if "fillna" in kwargs: vpdf.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: vpdf.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it vpdf.name = f"VP_{width}" vpdf.category = "volume" return vpdf
def vp(close, volume, width=None, **kwargs): """Indicator: Volume Profile (VP)""" # Validate arguments width = int(width) if width and width > 0 else 10 close = verify_series(close, width) volume = verify_series(volume, width) sort_close = kwargs.pop("sort_close", False) if close is None or volume is None: return # Setup signed_price = signed_series(close, 1) pos_volume = volume * signed_price[signed_price > 0] pos_volume.name = volume.name neg_volume = -volume * signed_price[signed_price < 0] neg_volume.name = volume.name vp = concat([close, pos_volume, neg_volume], axis=1) close_col = f"{vp.columns[0]}" high_price_col = f"high_{close_col}" low_price_col = f"low_{close_col}" mean_price_col = f"mean_{close_col}" volume_col = f"{vp.columns[1]}" pos_volume_col = f"pos_{volume_col}" neg_volume_col = f"neg_{volume_col}" total_volume_col = f"total_{volume_col}" vp.columns = [close_col, pos_volume_col, neg_volume_col] # sort_close: Sort by close before splitting into ranges. Default: False # If False, it sorts by date index or chronological versus by price if sort_close: vp[mean_price_col] = vp[close_col] vpdf = vp.groupby(cut(vp[close_col], width, include_lowest=True, precision=2)).agg({ mean_price_col: mean, pos_volume_col: sum, neg_volume_col: sum, }) vpdf[low_price_col] = [x.left for x in vpdf.index] vpdf[high_price_col] = [x.right for x in vpdf.index] vpdf = vpdf.reset_index(drop=True) vpdf = vpdf[[low_price_col, mean_price_col, high_price_col, pos_volume_col, neg_volume_col]] else: vp_ranges = array_split(vp, width) result = ({ low_price_col: r[close_col].min(), mean_price_col: r[close_col].mean(), high_price_col: r[close_col].max(), pos_volume_col: r[pos_volume_col].sum(), neg_volume_col: r[neg_volume_col].sum(), } for r in vp_ranges) vpdf = DataFrame(result) vpdf[total_volume_col] = vpdf[pos_volume_col] + vpdf[neg_volume_col] # Handle fills if "fillna" in kwargs: vpdf.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: vpdf.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it vpdf.name = f"VP_{width}" vpdf.category = "volume" return vpdf