Source code for pandas_ta.overlap.vwap

# -*- coding: utf-8 -*-
from .hlc3 import hlc3
from pandas_ta.utils import get_offset, is_datetime_ordered, verify_series

def vwap(high, low, close, volume, anchor=None, offset=None, **kwargs):
    """Indicator: Volume Weighted Average Price (VWAP)"""
    # Validate Arguments
    high = verify_series(high)
    low = verify_series(low)
    close = verify_series(close)
    volume = verify_series(volume)
    anchor = anchor.upper() if anchor and isinstance(anchor, str) and len(anchor) >= 1 else "D"
    offset = get_offset(offset)

    typical_price = hlc3(high=high, low=low, close=close)
    if not is_datetime_ordered(volume):
        print(f"[!] VWAP volume series is not datetime ordered. Results may not be as expected.")
    if not is_datetime_ordered(typical_price):
        print(f"[!] VWAP price series is not datetime ordered. Results may not be as expected.")

    # Calculate Result
    wp = typical_price * volume
    vwap  = wp.groupby(wp.index.to_period(anchor)).cumsum()
    vwap /= volume.groupby(volume.index.to_period(anchor)).cumsum()

    # Offset
    if offset != 0:
        vwap = vwap.shift(offset)

    # Handle fills
    if "fillna" in kwargs:
        vwap.fillna(kwargs["fillna"], inplace=True)
    if "fill_method" in kwargs:
        vwap.fillna(method=kwargs["fill_method"], inplace=True)

    # Name & Category
    vwap.name = f"VWAP_{anchor}"
    vwap.category = "overlap"

    return vwap


vwap.__doc__ = \
"""Volume Weighted Average Price (VWAP)

The Volume Weighted Average Price that measures the average typical price
by volume.  It is typically used with intraday charts to identify general
direction.

Sources:
    https://www.tradingview.com/wiki/Volume_Weighted_Average_Price_(VWAP)
    https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/volume-weighted-average-price-vwap/
    https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vwap_intraday

Calculation:
    tp = typical_price = hlc3(high, low, close)
    tpv = tp * volume
    VWAP = tpv.cumsum() / volume.cumsum()

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
    anchor (str): How to anchor VWAP. Depending on the index values, it will
        implement various Timeseries Offset Aliases as listed here:
        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
        Default: "D".
    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.
"""