candlestick.py

""""""
# Import Built-Ins:

# Import Third-Party:

# Import Homebrew:
import jhtalib as jhta


def CDLBODYM(df, n, open='Open', close='Close'):
    """
    Candle Body Momentum
    Returns: list of floats = jhta.CDLBODYM(df, n, open='Open', close='Close')
    Source: book: Trading Systems and Methods
    """
    cdl_list = []
    for i in range(len(df[close])):
        if i + 1 < n:
            cdl = float('NaN')
        else:
            start = i + 1 - n
            end = i + 1
            cdlbodys = jhta.CDLBODYS(df, open, close)[start:end]
            upsum = 0
            downsum = 0
            for i2 in range(len(cdlbodys)):
                if cdlbodys[i2] > 0:
                    upsum = upsum + 1
                else:
                    downsum = downsum + 1
            cdl = upsum / (upsum + downsum)
        cdl_list.append(cdl)
    return cdl_list

def CDLBODYP(df, open='Open', close='Close'):
    """
    Candle Body Percent
    Returns: list of floats = jhta.CDLBODYP(df, open='Open', close='Close')
    """
    return [(df[close][i] - df[open][i]) / df[open][i] for i in range(len(df[close]))]

def CDLBODYS(df, open='Open', close='Close'):
    """
    Candle Body Size
    Returns: list of floats = jhta.CDLBODYS(df, open='Open', close='Close')
    Source: https://www.tradeciety.com/understand-candlesticks-patterns/
    """
    return [df[close][i] - df[open][i] for i in range(len(df[close]))]

def CDLLOWSHAS(df, open='Open', low='Low', close='Close'):
    """
    Candle Lower Shadow Size
    Returns: list of floats = jhta.CDLLOWSHAS(df, open='Open', low='Low', close='Close')
    Source: https://www.tradeciety.com/understand-candlesticks-patterns/
    """
    cdl_list = []
    for i in range(len(df[close])):
        body = df[close][i] - df[open][i]
        if body < 0:
            cdl = df[close][i] - df[low][i]
        else:
            cdl = df[open][i] - df[low][i]
        cdl_list.append(cdl)
    return cdl_list

def CDLUPPSHAS(df, open='Open', high='High', close='Close'):
    """
    Candle Upper Shadow Size
    Returns: list of floats = jhta.CDLUPPSHAS(df, open='Open', high='High', close='Close')
    Source: https://www.tradeciety.com/understand-candlesticks-patterns/
    """
    cdl_list = []
    for i in range(len(df[close])):
        body = df[close][i] - df[open][i]
        if body < 0:
            cdl = df[high][i] - df[open][i]
        else:
            cdl = df[high][i] - df[close][i]
        cdl_list.append(cdl)
    return cdl_list

def CDLWICKS(df, high='High', low='Low'):
    """
    Candle Wick Size
    Returns: list of floats = jhta.CDLWICKS(df, high='High', low='Low')
    Source: https://www.tradeciety.com/understand-candlesticks-patterns/
    """
    return [df[high][i] - df[low][i] for i in range(len(df[low]))]

def GAP(df, high='High', low='Low'):
    """
    Gap
    Returns: list of floats = jhta.GAP(df, high='High', low='Low')
    """
    gap_list = []
    for i in range(len(df[low])):
        if i < 1:
            gap = float('NaN')
        else:
            gap = .0
            if df[low][i] > df[high][i - 1]:
                gap = df[low][i] - df[high][i - 1]
            if df[high][i] < df[low][i - 1]:
                gap = df[high][i] - df[low][i - 1]
        gap_list.append(gap)
    return gap_list

def IMI(df, open='Open', close='Close'):
    """
    Intraday Momentum Index
    Returns: list of floats = jhta.IMI(df, open='Open', close='Close')
    Source: https://www.fmlabs.com/reference/default.htm?url=IMI.htm
    """
    imi_list = []
    upsum = .0
    downsum = .0
    for i in range(len(df[close])):
        if df[close][i] > df[open][i]:
            upsum = upsum + (df[close][i] - df[open][i])
        else:
            downsum = downsum + (df[open][i] - df[close][i])
        imi = 100 * (upsum / (upsum + downsum))
        imi_list.append(imi)
    return imi_list

def INSBAR(df, high='High', low='Low'):
    """
    Inside Bar
    Returns: list of ints = jhta.INSBAR(df, high='High', low='Low')
    """
    insbar_list = []
    for i in range(len(df[low])):
        if i < 1:
            insbar = float('NaN')
        else:
            insbar = 0
            if df[high][i] < df[high][i - 1] and df[low][i] > df[low][i - 1]:
                insbar = 1
        insbar_list.append(insbar)
    return insbar_list

def OUTSBAR(df, high='High', low='Low'):
    """
    Outside Bar
    Returns: list of ints = jhta.OUTSBAR(df, high='High', low='Low')
    """
    outsbar_list = []
    for i in range(len(df[low])):
        if i < 1:
            outsbar = float('NaN')
        else:
            outsbar = 0
            if df[high][i] > df[high][i - 1] and df[low][i] < df[low][i - 1]:
                outsbar = 1
        outsbar_list.append(outsbar)
    return outsbar_list

def QSTICK(df, n, open='Open', close='Close'):
    """
    Qstick
    Returns: list of floats = jhta.QSTICK(df, n, open='Open', close='Close')
    Source: https://www.fmlabs.com/reference/default.htm?url=Qstick.htm
    """
    qstick_list = []
    for i in range(len(df[close])):
        if i + 1 < n:
            qstick = float('NaN')
        else:
            start = i + 1 - n
            end = i + 1
            qstick = sum(jhta.CDLBODYS(df, open, close)[start:end]) / n
        qstick_list.append(qstick)
    return qstick_list

def SHADOWT(df, n, open='Open', high='High', low='Low', close='Close'):
    """
    Shadow Trends
    Returns: dict of lists of floats = jhta.SHADOWT(df, n, open='Open', high='High', low='Low', close='Close')
    Source: book: The New Technical Trader
    """
    shadowt_dict = {'upper': [], 'lower': []}
    for i in range(len(df[close])):
        if i + 1 < n:
            upper = float('NaN')
            lower = float('NaN')
        else:
            start = i + 1 - n
            end = i + 1
            upper = sum(jhta.CDLUPPSHAS(df, open, high, close)[start:end]) / n
            lower = sum(jhta.CDLLOWSHAS(df, open, low, close)[start:end]) / n
        shadowt_dict['upper'].append(upper)
        shadowt_dict['lower'].append(lower)
    return shadowt_dict