mirror of
https://github.com/wassname/pandas-ta.git
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volume indicators added
This commit is contained in:
Kevin Johnson
@@ -6,4 +6,6 @@ pandas_ta/__init__.py
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pandas_ta/core.py
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pandas_ta/overlap.py
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pandas_ta/performance.py
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pandas_ta/statistics.py
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pandas_ta/utils.py
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pandas_ta/volatility.py
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@@ -0,0 +1,815 @@
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# -*- coding: utf-8 -*-
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"""
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.. module:: volume
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:synopsis: Volume Indicators.
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.. moduleauthor:: Dario Lopez Padial (Bukosabino)
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"""
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import numpy as np
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import pandas as pd
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from .utils import get_drift, get_offset, signed_series, verify_series
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from .momentum import roc
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from .overlap import hl2, hlc3, ema
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def ad(high, low, close, volume, open_=None, offset=None, **kwargs):
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"""Indicator: Accumulation/Distribution (AD)"""
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# Validate Arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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volume = verify_series(volume)
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offset = get_offset(offset)
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# Calculate Result
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if open_ is not None:
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open_ = verify_series(open_)
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ad = close - open_ # AD with Open
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else:
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ad = 2 * close - high - low # AD with High, Low, Close
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hl_range = high - low
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ad *= volume / hl_range
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ad = ad.cumsum()
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# Offset
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if offset != 0:
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ad = ad.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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ad.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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ad.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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ad.name = f"AD"
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ad.category = 'volume'
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return ad
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def adosc(high, low, close, volume, open_=None, fast=None, slow=None, offset=None, **kwargs):
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"""Indicator: Accumulation/Distribution Oscillator"""
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# Validate Arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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volume = verify_series(volume)
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fast = int(fast) if fast and fast > 0 else 12
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slow = int(slow) if slow and slow > 0 else 26
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if slow < fast:
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fast, slow = slow, fast
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offset = get_offset(offset)
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# Calculate Result
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ad_ = ad(high=high, low=low, close=close, volume=volume, open_=open_)
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fast_ad = ema(close=ad_, length=fast)
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slow_ad = ema(close=ad_, length=slow)
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adosc = fast_ad - slow_ad
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# Offset
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if offset != 0:
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adosc = adosc.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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adosc.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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adosc.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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adosc.name = f"ADOSC_{fast}_{slow}"
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adosc.category = 'volume'
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return adosc
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def cmf(high, low, close, volume, open_=None, length=None, offset=None, **kwargs):
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"""Indicator: Chaikin Money Flow (CMF)"""
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# Validate Arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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volume = verify_series(volume)
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length = int(length) if length and length > 0 else 20
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min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
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offset = get_offset(offset)
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# Calculate Result
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if open_ is not None:
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open_ = verify_series(open_)
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ad = close - open_ # AD with Open
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else:
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ad = 2 * close - high - low # AD with High, Low, Close
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hl_range = high - low
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ad *= volume / hl_range
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cmf = ad.rolling(length, min_periods=min_periods).sum() / volume.rolling(length, min_periods=min_periods).sum()
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# Offset
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if offset != 0:
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cmf = cmf.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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cmf.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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cmf.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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cmf.name = f"CMF_{length}"
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cmf.category = 'volume'
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return cmf
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def efi(close, volume, length=None, drift=None, mamode=None, offset=None, **kwargs):
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"""Indicator: Elder's Force Index (EFI)"""
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# Validate arguments
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close = verify_series(close)
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volume = verify_series(volume)
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length = int(length) if length and length > 0 else 13
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min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
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drift = get_drift(drift)
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mamode = mamode.lower() if mamode else None
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offset = get_offset(offset)
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# Calculate Result
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pv_diff = close.diff(drift) * volume
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if mamode == 'sma':
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efi = pv_diff.rolling(length, min_periods=min_periods).mean()
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else:
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efi = pv_diff.ewm(span=length, min_periods=min_periods).mean()
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# Offset
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if offset != 0:
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efi = efi.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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efi.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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efi.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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efi.name = f"EFI_{length}"
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efi.category = 'volume'
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return efi
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def eom(high, low, close, volume, length=None, divisor=None, drift=None, offset=None, **kwargs):
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"""Indicator: Ease of Movement (EOM)"""
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# Validate arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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volume = verify_series(volume)
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length = int(length) if length and length > 0 else 14
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min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
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divisor = divisor if divisor and divisor > 0 else 100000000
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drift = get_drift(drift)
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offset = get_offset(offset)
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# Calculate Result
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hl_range = high - low
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distance = hl2(high=high, low=low) - hl2(high=high.shift(drift), low=low.shift(drift))
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box_ratio = (volume / divisor) / hl_range
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eom = distance / box_ratio
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eom = eom.rolling(length, min_periods=min_periods).mean()
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# Offset
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if offset != 0:
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eom = eom.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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eom.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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eom.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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eom.name = f"EOM_{length}_{divisor}"
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eom.category = 'volume'
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return eom
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def mfi(high, low, close, volume, length=None, drift=None, offset=None, **kwargs):
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"""Indicator: Money Flow Index (MFI)"""
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# Validate arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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volume = verify_series(volume)
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length = int(length) if length and length > 0 else 14
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drift = get_drift(drift)
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offset = get_offset(offset)
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# Calculate Result
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typical_price = hlc3(high=high, low=low, close=close)
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raw_money_flow = typical_price * volume
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tdf = pd.DataFrame({'diff': 0, 'rmf': raw_money_flow, '+mf': 0, '-mf': 0})
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tdf.loc[(typical_price.diff(drift) > 0), 'diff'] = 1
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tdf.loc[tdf['diff'] == 1, '+mf'] = raw_money_flow
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tdf.loc[(typical_price.diff(drift) < 0), 'diff'] = -1
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tdf.loc[tdf['diff'] == -1, '-mf'] = raw_money_flow
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psum = tdf['+mf'].rolling(length).sum()
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nsum = tdf['-mf'].rolling(length).sum()
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tdf['mr'] = psum / nsum
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mfi = 100 * psum / (psum + nsum)
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tdf['mfi'] = mfi
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# Offset
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if offset != 0:
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mfi = mfi.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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mfi.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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mfi.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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mfi.name = f"MFI_{length}"
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mfi.category = 'momentum'
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return mfi
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def nvi(close, volume, length=None, initial=None, offset=None, **kwargs):
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"""Indicator: Negative Volume Index (NVI)"""
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# Validate arguments
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close = verify_series(close)
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volume = verify_series(volume)
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length = int(length) if length and length > 0 else 1
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min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
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initial = int(initial) if initial and initial > 0 else 1000
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offset = get_offset(offset)
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# Calculate Result
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roc_ = roc(close=close)
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signed_volume = signed_series(volume, initial=1)
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nvi = signed_volume[signed_volume < 0].abs() * roc_
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nvi.fillna(0, inplace=True)
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nvi.iloc[0]= initial
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nvi = nvi.cumsum()
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# Offset
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if offset != 0:
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nvi = nvi.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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nvi.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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nvi.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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nvi.name = f"NVI_{length}"
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nvi.category = 'volume'
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return nvi
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def obv(close, volume, offset=None, **kwargs):
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"""Indicator: On Balance Volume (OBV)"""
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# Validate arguments
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close = verify_series(close)
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volume = verify_series(volume)
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offset = get_offset(offset)
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# Calculate Result
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signed_volume = signed_series(close, initial=1) * volume
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obv = signed_volume.cumsum()
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# Offset
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if offset != 0:
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obv = obv.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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obv.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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obv.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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obv.name = f"OBV"
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obv.category = 'volume'
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return obv
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def pvol(close, volume, signed=True, offset=None, **kwargs):
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"""Indicator: Price-Volume (PVOL)"""
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# Validate arguments
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close = verify_series(close)
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volume = verify_series(volume)
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offset = get_offset(offset)
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# Calculate Result
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if signed:
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pvol = signed_series(close, 1) * close * volume
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else:
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pvol = close * volume
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# Offset
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if offset != 0:
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pvol = pvol.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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pvol.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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pvol.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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pvol.name = f"PVOL"
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pvol.category = 'volume'
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return pvol
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def pvt(close, volume, drift=None, offset=None, **kwargs):
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"""Indicator: Price-Volume Trend (PVT)"""
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# Validate arguments
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close = verify_series(close)
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volume = verify_series(volume)
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drift = get_drift(drift)
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offset = get_offset(offset)
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# Calculate Result
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pv = roc(close=close, length=drift) * volume
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pvt = pv.cumsum()
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# Offset
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if offset != 0:
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pvt = pvt.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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pvt.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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pvt.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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pvt.name = f"PVT"
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pvt.category = 'volume'
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return pvt
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def vp(close, volume, width=None, **kwargs):
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"""Indicator: Volume Profile (VP)"""
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# Validate arguments
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close = verify_series(close)
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volume = verify_series(volume)
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width = int(width) if width and width > 0 else 10
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# Setup
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signed_volume = signed_series(volume, initial=1)
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pos_volume = signed_volume[signed_volume > 0] * volume
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neg_volume = signed_volume[signed_volume < 0] * -volume
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vp = pd.concat([close, pos_volume, neg_volume], axis=1)
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close_col = f"{vp.columns[0]}"
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high_price_col = f"high_{close_col}"
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low_price_col = f"low_{close_col}"
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mean_price_col = f"mean_{close_col}"
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mid_price_col = f"mid_{close_col}"
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volume_col = f"{vp.columns[1]}"
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pos_volume_col = f"pos_{volume_col}"
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neg_volume_col = f"neg_{volume_col}"
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total_volume_col = f"total_{volume_col}"
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vp.columns = [close_col, pos_volume_col, neg_volume_col]
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# sort_close: Sort by close before splitting into ranges. Default: False
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# If False, it sorts by date index or chronological versus by price
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if kwargs.pop('sort_close', False):
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vp.sort_values(by=[close_col], inplace=True)
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# Calculate Result
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vp_ranges = np.array_split(vp, width)
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result = ({
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low_price_col: r[close_col].min(),
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mean_price_col: r[close_col].mean(),
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high_price_col: r[close_col].max(),
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pos_volume_col: r[pos_volume_col].sum(),
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neg_volume_col: r[neg_volume_col].sum(),
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} for r in vp_ranges)
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vpdf = pd.DataFrame(result)
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vpdf[total_volume_col] = vpdf[pos_volume_col] + vpdf[neg_volume_col]
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# Handle fills
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if 'fillna' in kwargs:
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vpdf.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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vpdf.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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vpdf.name = f"VP"
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vpdf.category = 'volume'
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return vpdf
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# Volume Documentation
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ad.__doc__ = \
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"""Accumulation/Distribution (AD)
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Accumulation/Distribution indicator utilizes the relative position
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of the close to it's High-Low range with volume. Then it is cumulated.
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Sources:
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https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/accumulationdistribution-ad/
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Calculation:
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CUM = Cumulative Sum
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if 'open':
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AD = close - open
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else:
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AD = 2 * close - high - low
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hl_range = high - low
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AD = AD * volume / hl_range
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AD = CUM(AD)
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Args:
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high (pd.Series): Series of 'high's
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low (pd.Series): Series of 'low's
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close (pd.Series): Series of 'close's
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volume (pd.Series): Series of 'volume's
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open (pd.Series): Series of 'open's
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.Series: New feature generated.
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"""
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adosc.__doc__ = \
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"""Accumulation/Distribution Oscillator or Chaikin Oscillator
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Accumulation/Distribution Oscillator indicator utilizes
|
||||
Accumulation/Distribution and treats it similarily to MACD
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||||
or APO.
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||||
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||||
Sources:
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||||
https://www.investopedia.com/articles/active-trading/031914/understanding-chaikin-oscillator.asp
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||||
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Calculation:
|
||||
Default Inputs:
|
||||
fast=12, slow=26
|
||||
AD = Accum/Dist
|
||||
ad = AD(high, low, close, open)
|
||||
fast_ad = EMA(ad, fast)
|
||||
slow_ad = EMA(ad, slow)
|
||||
ADOSC = fast_ad - slow_ad
|
||||
|
||||
Args:
|
||||
high (pd.Series): Series of 'high's
|
||||
low (pd.Series): Series of 'low's
|
||||
close (pd.Series): Series of 'close's
|
||||
open (pd.Series): Series of 'open's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
fast (int): The short period. Default: 12
|
||||
slow (int): The long period. Default: 26
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
cmf.__doc__ = \
|
||||
"""Chaikin Money Flow (CMF)
|
||||
|
||||
Chailin Money Flow measures the amount of money flow volume over a specific
|
||||
period in conjunction with Accumulation/Distribution.
|
||||
|
||||
Sources:
|
||||
https://www.tradingview.com/wiki/Chaikin_Money_Flow_(CMF)
|
||||
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
length=20
|
||||
if 'open':
|
||||
ad = close - open
|
||||
else:
|
||||
ad = 2 * close - high - low
|
||||
|
||||
hl_range = high - low
|
||||
ad = ad * volume / hl_range
|
||||
CMF = SUM(ad, length) / SUM(volume, length)
|
||||
|
||||
Args:
|
||||
high (pd.Series): Series of 'high's
|
||||
low (pd.Series): Series of 'low's
|
||||
close (pd.Series): Series of 'close's
|
||||
open (pd.Series): Series of 'open's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
length (int): The short period. Default: 20
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
efi.__doc__ = \
|
||||
"""Elder's Force Index (EFI)
|
||||
|
||||
Elder's Force Index measures the power behind a price movement using price
|
||||
and volume as well as potential reversals and price corrections.
|
||||
|
||||
Sources:
|
||||
https://www.tradingview.com/wiki/Elder%27s_Force_Index_(EFI)
|
||||
https://www.motivewave.com/studies/elders_force_index.htm
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
length=20, drift=1, mamode=None
|
||||
EMA = Exponential Moving Average
|
||||
SMA = Simple Moving Average
|
||||
|
||||
pv_diff = close.diff(drift) * volume
|
||||
if mamode == 'sma':
|
||||
EFI = SMA(pv_diff, length)
|
||||
else:
|
||||
EFI = EMA(pv_diff, length)
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
length (int): The short period. Default: 13
|
||||
drift (int): The diff period. Default: 1
|
||||
mamode (str): Two options: None or 'sma'. Default: None
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
eom.__doc__ = \
|
||||
"""Ease of Movement (EOM)
|
||||
|
||||
Ease of Movement is a volume based oscillator that is designed to measure the
|
||||
relationship between price and volume flucuating across a zero line.
|
||||
|
||||
Sources:
|
||||
https://www.tradingview.com/wiki/Ease_of_Movement_(EOM)
|
||||
https://www.motivewave.com/studies/ease_of_movement.htm
|
||||
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ease_of_movement_emv
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
length=14, divisor=100000000, drift=1
|
||||
SMA = Simple Moving Average
|
||||
hl_range = high - low
|
||||
distance = 0.5 * (high - high.shift(drift) + low - low.shift(drift))
|
||||
box_ratio = (volume / divisor) / hl_range
|
||||
eom = distance / box_ratio
|
||||
EOM = SMA(eom, length)
|
||||
|
||||
Args:
|
||||
high (pd.Series): Series of 'high's
|
||||
low (pd.Series): Series of 'low's
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
length (int): The short period. Default: 14
|
||||
drift (int): The diff period. Default: 1
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
mfi.__doc__ = \
|
||||
"""Money Flow Index (MFI)
|
||||
|
||||
Money Flow Index is an oscillator indicator that is used to measure buying and
|
||||
selling pressure by utilizing both price and volume.
|
||||
|
||||
Sources:
|
||||
https://www.tradingview.com/wiki/Money_Flow_(MFI)
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
length=14, drift=1
|
||||
tp = typical_price = hlc3 = (high + low + close) / 3
|
||||
rmf = raw_money_flow = tp * volume
|
||||
|
||||
pmf = pos_money_flow = SUM(rmf, length) if tp.diff(drift) > 0 else 0
|
||||
nmf = neg_money_flow = SUM(rmf, length) if tp.diff(drift) < 0 else 0
|
||||
|
||||
MFR = money_flow_ratio = pmf / nmf
|
||||
MFI = money_flow_index = 100 * pmf / (pmf + nmf)
|
||||
|
||||
Args:
|
||||
high (pd.Series): Series of 'high's
|
||||
low (pd.Series): Series of 'low's
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
length (int): The sum period. Default: 14
|
||||
drift (int): The difference period. Default: 1
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
nvi.__doc__ = \
|
||||
"""Negative Volume Index (NVI)
|
||||
|
||||
The Negative Volume Index is a cumulative indicator that uses volume change in
|
||||
an attempt to identify where smart money is active.
|
||||
|
||||
Sources:
|
||||
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde
|
||||
https://www.motivewave.com/studies/negative_volume_index.htm
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
length=20, initial=1000
|
||||
ROC = Rate of Change
|
||||
|
||||
roc = ROC(close, 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()
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
length (int): The short period. Default: 13
|
||||
initial (int): The short period. Default: 1000
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
obv.__doc__ = \
|
||||
"""On Balance Volume (OBV)
|
||||
|
||||
On Balance Volume is a cumulative indicator to measure buying and selling
|
||||
pressure.
|
||||
|
||||
Sources:
|
||||
https://www.tradingview.com/wiki/On_Balance_Volume_(OBV)
|
||||
https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/
|
||||
https://www.motivewave.com/studies/on_balance_volume.htm
|
||||
|
||||
Calculation:
|
||||
signed_volume = signed_series(close, initial=1) * volume
|
||||
obv = signed_volume.cumsum()
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
pvol.__doc__ = \
|
||||
"""Price-Volume (PVOL)
|
||||
|
||||
Returns a series of the product of price and volume.
|
||||
|
||||
Calculation:
|
||||
if signed:
|
||||
pvol = signed_series(close, 1) * close * volume
|
||||
else:
|
||||
pvol = close * volume
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
signed (bool): Keeps the sign of the difference in 'close's. Default: True
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
|
||||
pvt.__doc__ = \
|
||||
"""Price-Volume Trend (PVT)
|
||||
|
||||
The Price-Volume Trend utilizes the Rate of Change with volume to
|
||||
and it's cumulative values to determine money flow.
|
||||
|
||||
Sources:
|
||||
https://www.tradingview.com/wiki/Price_Volume_Trend_(PVT)
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
drift=1
|
||||
ROC = Rate of Change
|
||||
pv = ROC(close, drift) * volume
|
||||
PVT = pv.cumsum()
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
drift (int): The diff period. Default: 1
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
vp.__doc__ = \
|
||||
"""Volume Profile (VP)
|
||||
|
||||
Calculates the Volume Profile by slicing price into ranges. Note: Value Area is not calculated.
|
||||
|
||||
Sources:
|
||||
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:volume_by_price
|
||||
https://www.tradingview.com/wiki/Volume_Profile
|
||||
http://www.ranchodinero.com/volume-tpo-essentials/
|
||||
https://www.tradingtechnologies.com/blog/2013/05/15/volume-at-price/
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
width=10
|
||||
|
||||
vp = pd.concat([close, pos_volume, neg_volume], axis=1)
|
||||
vp_ranges = np.array_split(vp, width)
|
||||
result = ({high_close, low_close, mean_close, neg_volume, pos_volume} foreach range in vp_ranges)
|
||||
vpdf = pd.DataFrame(result)
|
||||
vpdf['total_volume'] = vpdf['pos_volume'] + vpdf['neg_volume']
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
volume (pd.Series): Series of 'volume's
|
||||
width (int): How many ranges to distrubute price into. Default: 10
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
sort_close (value, optional): Whether to sort by close before splitting into ranges. Default: False
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: New feature generated.
|
||||
"""
|
||||
Reference in New Issue
Block a user