# -*- coding: utf-8 -*-
"""
.. module:: volume
   :synopsis: Volume Indicators.

.. moduleauthor:: Dario Lopez Padial (Bukosabino)

"""

import numpy as np
import pandas as pd


def acc_dist_index(high, low, close, volume, fillna=False):
    """Accumulation/Distribution Index (ADI)

    Acting as leading indicator of price movements.

    https://en.wikipedia.org/wiki/Accumulation/distribution_index

    Args:
        high(pandas.Series): dataset 'High' column.
        low(pandas.Series): dataset 'Low' column.
        close(pandas.Series): dataset 'Close' column.
        volume(pandas.Series): dataset 'Volume' column.
        fillna(bool): if True, fill nan values.

    Returns:
        pandas.Series: New feature generated.
    """
    clv = ((close - low) - (high - close)) / (high - low)
    clv = clv.fillna(0.0)  # float division by zero
    ad = clv * volume
    ad = ad + ad.shift(1, fill_value=ad.mean())
    if fillna:
        ad = ad.replace([np.inf, -np.inf], np.nan).fillna(0)
    return pd.Series(ad, name='adi')


def on_balance_volume(close, volume, fillna=False):
    """On-balance volume (OBV)

    It relates price and volume in the stock market. OBV is based on a
    cumulative total volume.

    https://en.wikipedia.org/wiki/On-balance_volume

    Args:
        close(pandas.Series): dataset 'Close' column.
        volume(pandas.Series): dataset 'Volume' column.
        fillna(bool): if True, fill nan values.

    Returns:
        pandas.Series: New feature generated.
    """
    df = pd.DataFrame([close, volume]).transpose()
    df['OBV'] = np.nan
    c1 = close < close.shift(1)
    c2 = close > close.shift(1)
    if c1.any():
        df.loc[c1, 'OBV'] = - volume
    if c2.any():
        df.loc[c2, 'OBV'] = volume
    obv = df['OBV'].cumsum()
    if fillna:
        obv = obv.replace([np.inf, -np.inf], np.nan).fillna(0)
    return pd.Series(obv, name='obv')


def chaikin_money_flow(high, low, close, volume, n=20, fillna=False):
    """Chaikin Money Flow (CMF)

    It measures the amount of Money Flow Volume over a specific period.

    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf

    Args:
        high(pandas.Series): dataset 'High' column.
        low(pandas.Series): dataset 'Low' column.
        close(pandas.Series): dataset 'Close' column.
        volume(pandas.Series): dataset 'Volume' column.
        n(int): n period.
        fillna(bool): if True, fill nan values.

    Returns:
        pandas.Series: New feature generated.
    """
    mfv = ((close - low) - (high - close)) / (high - low)
    mfv = mfv.fillna(0.0)  # float division by zero
    mfv *= volume
    cmf = (mfv.rolling(n, min_periods=0).sum()
           / volume.rolling(n, min_periods=0).sum())
    if fillna:
        cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return pd.Series(cmf, name='cmf')


def force_index(close, volume, n=2, fillna=False):
    """Force Index (FI)

    It illustrates how strong the actual buying or selling pressure is. High
    positive values mean there is a strong rising trend, and low values signify
    a strong downward trend.

    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:force_index

    Args:
        close(pandas.Series): dataset 'Close' column.
        volume(pandas.Series): dataset 'Volume' column.
        n(int): n period.
        fillna(bool): if True, fill nan values.

    Returns:
        pandas.Series: New feature generated.
    """
    fi = close.diff(n) * volume.diff(n)
    if fillna:
        fi = fi.replace([np.inf, -np.inf], np.nan).fillna(0)
    return pd.Series(fi, name='fi_'+str(n))


def ease_of_movement(high, low, close, volume, n=20, fillna=False):
    """Ease of movement (EoM, EMV)

    It relate an asset's price change to its volume and is particularly useful
    for assessing the strength of a trend.

    https://en.wikipedia.org/wiki/Ease_of_movement

    Args:
        high(pandas.Series): dataset 'High' column.
        low(pandas.Series): dataset 'Low' column.
        close(pandas.Series): dataset 'Close' column.
        volume(pandas.Series): dataset 'Volume' column.
        n(int): n period.
        fillna(bool): if True, fill nan values.

    Returns:
        pandas.Series: New feature generated.
    """
    emv = (high.diff(1) + low.diff(1)) * (high - low) / (2 * volume)
    emv = emv.rolling(n, min_periods=0).mean()
    if fillna:
        emv = emv.replace([np.inf, -np.inf], np.nan).fillna(0)
    return pd.Series(emv, name='eom_' + str(n))


def volume_price_trend(close, volume, fillna=False):
    """Volume-price trend (VPT)

    Is based on a running cumulative volume that adds or substracts a multiple
    of the percentage change in share price trend and current volume, depending
    upon the investment's upward or downward movements.

    https://en.wikipedia.org/wiki/Volume%E2%80%93price_trend

    Args:
        close(pandas.Series): dataset 'Close' column.
        volume(pandas.Series): dataset 'Volume' column.
        n(int): n period.
        fillna(bool): if True, fill nan values.

    Returns:
        pandas.Series: New feature generated.
    """
    vpt = volume * ((close - close.shift(1, fill_value=close.mean())) / close.shift(1, fill_value=close.mean()))
    vpt = vpt.shift(1, fill_value=vpt.mean()) + vpt
    if fillna:
        vpt = vpt.replace([np.inf, -np.inf], np.nan).fillna(0)
    return pd.Series(vpt, name='vpt')


def negative_volume_index(close, volume, fillna=False):
    """Negative Volume Index (NVI)

    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde

    The Negative Volume Index (NVI) is a cumulative indicator that uses the
    change in volume to decide when the smart money is active. Paul Dysart
    first developed this indicator in the 1930s. [...] Dysart's Negative Volume
    Index works under the assumption that the smart money is active on days
    when volume decreases and the not-so-smart money is active on days when
    volume increases.

    The cumulative NVI line was unchanged when volume increased from one
    period to the other. In other words, nothing was done. Norman Fosback, of
    Stock Market Logic, adjusted the indicator by substituting the percentage
    price change for Net Advances.

    This implementation is the Fosback version.

    If today's volume is less than yesterday's volume then:
        nvi(t) = nvi(t-1) * ( 1 + (close(t) - close(t-1)) / close(t-1) )
    Else
        nvi(t) = nvi(t-1)

    Please note: the "stockcharts.com" example calculation just adds the
    percentange change of price to previous NVI when volumes decline; other
    sources indicate that the same percentage of the previous NVI value should
    be added, which is what is implemented here.

    Args:
        close(pandas.Series): dataset 'Close' column.
        volume(pandas.Series): dataset 'Volume' column.
        fillna(bool): if True, fill nan values with 1000.

    Returns:
        pandas.Series: New feature generated.

    See also:
    https://en.wikipedia.org/wiki/Negative_volume_index
    """
    price_change = close.pct_change()
    vol_decrease = (volume.shift(1) > volume)

    nvi = pd.Series(
        data=np.nan, index=close.index, dtype='float64', name='nvi')

    nvi.iloc[0] = 1000
    for i in range(1, len(nvi)):
        if vol_decrease.iloc[i]:
            nvi.iloc[i] = nvi.iloc[i - 1] * (1.0 + price_change.iloc[i])
        else:
            nvi.iloc[i] = nvi.iloc[i - 1]

    if fillna:
        # IDEA: There shouldn't be any na; might be better to throw exception
        nvi = nvi.replace([np.inf, -np.inf], np.nan).fillna(1000)

    return pd.Series(nvi, name='nvi')


# TODO
def put_call_ratio():
    # will need options volumes for this put/call ratio

    """Put/Call ratio (PCR)
    https://en.wikipedia.org/wiki/Put/call_ratio
    """
    # TODO
    return