Create technical_indicators.py

technical_indicators.py

@@ -0,0 +1,956 @@
+# technical_indicators.py
+
+import pandas as pd
+import numpy as np
+from math import sqrt
+
+def SMA(ohlc, period=14, column="Close"):
+    """Simple Moving Average"""
+    return pd.Series(ohlc[column].rolling(window=period).mean(), name=f"SMA_{period}")
+
+def SMM(ohlc, period= 9, column= "Close"):
+        """
+        Simple moving median, an alternative to moving average. SMA, when used to estimate the underlying trend in a time series,
+        is susceptible to rare events such as rapid shocks or other anomalies. A more robust estimate of the trend is the simple moving median over n time periods.
+        """
+
+        return pd.Series(
+            ohlc[column].rolling(window=period).median(),
+            name="{0} period SMM".format(period),
+        )
+
+def SSMA(ohlc,period = 9, column = "Close",adjust = True):
+        """
+        Smoothed simple moving average.
+
+        :param ohlc: data
+        :param period: range
+        :param column: open/close/high/low column of the DataFrame
+        :return: result Series
+        """
+
+        return pd.Series(
+            ohlc[column]
+            .ewm(ignore_na=False, alpha=1.0 / period, min_periods=0, adjust=adjust)
+            .mean(),
+            name="{0} period SSMA".format(period),
+        )
+
+def EMA(ohlc, period=14, column="Close", adjust=True):
+    """Exponential Moving Average"""
+    return pd.Series(ohlc[column].ewm(span=period, adjust=adjust).mean(), name=f"EMA_{period}")
+
+def RSI(ohlc, period=14, column="Close", adjust=True):
+    """Relative Strength Index"""
+    delta = ohlc[column].diff()
+    up, down = delta.copy(), delta.copy()
+    up[up < 0] = 0
+    down[down > 0] = 0
+    _gain = up.ewm(com=period - 1, adjust=adjust).mean()
+    _loss = abs(down.ewm(com=period - 1, adjust=adjust).mean())
+    RS = _gain / _loss
+    return pd.Series(100 - (100 / (1 + RS)), name=f"RSI_{period}")
+
+
+def DEMA(ohlc,period = 9,column = "Close",adjust = True):
+        """
+        Double Exponential Moving Average - attempts to remove the inherent lag associated to Moving Averages
+         by placing more weight on recent values. The name suggests this is achieved by applying a double exponential
+        smoothing which is not the case. The name double comes from the fact that the value of an EMA (Exponential Moving Average) is doubled.
+        To keep it in line with the actual data and to remove the lag the value 'EMA of EMA' is subtracted from the previously doubled EMA.
+        Because EMA(EMA) is used in the calculation, DEMA needs 2 * period -1 samples to start producing values in contrast to the period
+        samples needed by a regular EMA
+        """
+
+        DEMA = (
+            2 * EMA(ohlc, period)
+            - EMA(ohlc, period).ewm(span=period, adjust=adjust).mean()
+        )
+
+        return pd.Series(DEMA, name="{0} period DEMA".format(period))
+
+def TEMA(ohlc, period = 9, adjust = True):
+        """
+        Triple exponential moving average - attempts to remove the inherent lag associated to Moving Averages by placing more weight on recent values.
+        The name suggests this is achieved by applying a triple exponential smoothing which is not the case. The name triple comes from the fact that the
+        value of an EMA (Exponential Moving Average) is triple.
+        To keep it in line with the actual data and to remove the lag the value 'EMA of EMA' is subtracted 3 times from the previously tripled EMA.
+        Finally 'EMA of EMA of EMA' is added.
+        Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 * period - 2 samples to start producing values in contrast to the period samples
+        needed by a regular EMA.
+        """
+
+        triple_ema = 3 * EMA(ohlc, period)
+        ema_ema_ema = (
+            EMA(ohlc, period)
+            .ewm(ignore_na=False, span=period, adjust=adjust)
+            .mean()
+            .ewm(ignore_na=False, span=period, adjust=adjust)
+            .mean()
+        )
+
+        TEMA = (
+            triple_ema
+            - 3 * EMA(ohlc, period).ewm(span=period, adjust=adjust).mean()
+            + ema_ema_ema
+        )
+
+        return pd.Series(TEMA, name="{0} period TEMA".format(period))
+
+def TRIMA(ohlc, period = 18,column="Close"):
+        """
+        The Triangular Moving Average (TRIMA) [also known as TMA] represents an average of prices,
+        but places weight on the middle prices of the time period.
+        The calculations double-smooth the data using a window width that is one-half the length of the series.
+        source: https://www.thebalance.com/triangular-moving-average-tma-description-and-uses-1031203
+        """
+
+        weights = np.concatenate([np.arange(1, period // 2 + 1), np.arange(period // 2, 0, -1)])
+        weights = weights / weights.sum()
+        def triangular(x):
+            return np.dot(x, weights)
+        return pd.Series(ohlc[column].rolling(period).apply(triangular, raw=True), name=f"TRIMA_{period}")
+
+def TRIX(ohlc,period = 20,column = "Close",adjust = True):
+        """
+        The TRIX indicator calculates the rate of change of a triple exponential moving average.
+        The values oscillate around zero. Buy/sell signals are generated when the TRIX crosses above/below zero.
+        A (typically) 9 period exponential moving average of the TRIX can be used as a signal line.
+        A buy/sell signals are generated when the TRIX crosses above/below the signal line and is also above/below zero.
+
+        The TRIX was developed by Jack K. Hutson, publisher of Technical Analysis of Stocks & Commodities magazine,
+        and was introduced in Volume 1, Number 5 of that magazine.
+        """
+
+        data = ohlc[column]
+
+        def _ema(data, period, adjust):
+            return pd.Series(data.ewm(span=period, adjust=adjust).mean())
+
+        m = _ema(_ema(_ema(data, period, adjust), period, adjust), period, adjust)
+
+        return pd.Series(100 * (m.diff() / m), name="{0} period TRIX".format(period))
+
+def VAMA(ohlcv,period = 8, column = "Close",colvol="Volume"):
+        """
+        Volume Adjusted Moving Average
+        """
+
+        vp = ohlcv[colvol] * ohlcv[column]
+        volsum = ohlcv[colvol].rolling(window=period).mean()
+        volRatio = pd.Series(vp / volsum, name="VAMA")
+        cumSum = (volRatio * ohlcv[column]).rolling(window=period).sum()
+        cumDiv = volRatio.rolling(window=period).sum()
+
+        return pd.Series(cumSum / cumDiv, name="{0} period VAMA".format(period))
+
+def ER(ohlc, period=10, column="Close"):
+    """Efficiency Ratio"""
+    change = ohlc[column].diff(period).abs()
+    total_change = ohlc[column].diff().abs().rolling(window=period).sum()
+    return pd.Series(change / total_change, name="ER")
+
+def KAMA(ohlc, er=10, ema_fast=2, ema_slow=30, period=20, column="Close", adjust=True):
+    """Kaufman Adaptive Moving Average"""
+    efficiency_ratio = ER(ohlc, er, column=column)
+    fast_alpha = 2 / (ema_fast + 1)
+    slow_alpha = 2 / (ema_slow + 1)
+    smoothing_constant = (efficiency_ratio * (fast_alpha - slow_alpha) + slow_alpha) ** 2
+
+    sma = ohlc[column].rolling(window=period).mean()
+    kama = [float("nan")] * len(ohlc)
+
+    # Build KAMA line
+    for i in range(period, len(ohlc)):
+        if np.isnan(kama[i - 1]):
+            kama[i] = sma.iloc[i]
+        else:
+            kama[i] = kama[i - 1] + smoothing_constant.iloc[i] * (ohlc[column].iloc[i] - kama[i - 1])
+
+    return pd.Series(kama, index=ohlc.index, name=f"{period} period KAMA")
+
+def ZLEMA(ohlc,period = 26,adjust = True,column = "Close"):
+        """ZLEMA is an abbreviation of Zero Lag Exponential Moving Average. It was developed by John Ehlers and Rick Way.
+        ZLEMA is a kind of Exponential moving average but its main idea is to eliminate the lag arising from the very nature of the moving averages
+        and other trend following indicators. As it follows price closer, it also provides better price averaging and responds better to price swings."""
+
+        lag = int((period - 1) / 2)
+
+        ema = pd.Series(
+            (ohlc[column] + (ohlc[column].diff(lag))),
+            name="{0} period ZLEMA.".format(period),
+        )
+
+        zlema = pd.Series(
+            ema.ewm(span=period, adjust=adjust).mean(),
+            name="{0} period ZLEMA".format(period),
+        )
+
+        return zlema
+
+def WMA(ohlc, period=14, column="Close"):
+    """Weighted Moving Average"""
+    weights = np.arange(1, period + 1)
+    def linear(w):
+        def _inner(x):
+            return np.dot(x, w) / w.sum()
+        return _inner
+    close = ohlc[column]
+    return pd.Series(close.rolling(period, min_periods=period).apply(linear(weights), raw=True), name=f"WMA_{period}")
+
+def HMA(ohlc, period=20, column="Close"):
+    """Hull Moving Average"""
+    half_length = int(period / 2)
+    sqrt_length = int(sqrt(period))
+    wma_half = WMA(ohlc, half_length, column)
+    wma_full = WMA(ohlc, period, column)
+    hma = WMA(pd.DataFrame({column: 2 * wma_half - wma_full}), sqrt_length, column)
+    return hma.rename(f"HMA_{period}")
+
+def EVWMA(ohlcv, period=20, high="High", low="Low", close="Close", colvol="Volume", adjust=True):
+    """Ehlers Volatility Weighted Moving Average"""
+    tr = pd.concat([
+        ohlcv[high] - ohlcv[low],
+        abs(ohlcv[high] - ohlcv[close].shift()),
+        abs(ohlcv[low] - ohlcv[close].shift())
+    ], axis=1).max(axis=1)
+    vol_weight = ohlcv[colvol] / tr.rolling(window=period).mean()
+    return pd.Series((vol_weight * ohlcv[close]).ewm(span=period, adjust=adjust).mean(), name="EVWMA")
+
+def TP(ohlc,high="High",low="Low",column="Close"):
+        """Typical Price refers to the arithmetic average of the high, low, and closing prices for a given period."""
+
+        return pd.Series((ohlc[high] + ohlc[low] + ohlc[column]) / 3, name="TP")
+
+def VWAP(ohlcv,colvol="Volume"):
+        """
+        The volume weighted average price (VWAP) is a trading benchmark used especially in pension plans.
+        VWAP is calculated by adding up the dollars traded for every transaction (price multiplied by number of shares traded) and then dividing
+        by the total shares traded for the day.
+        """
+
+        return pd.Series(
+            ((ohlcv[colvol] * TP(ohlcv,open="Open",close="Close",high="High",low="Low")).cumsum()) / ohlcv[colvol].cumsum(),
+            name="VWAP.",
+        )
+
+def FRAMA(ohlc, period=20, batch=10, column="Close", adjust=True):
+    """Fractal Adaptive Moving Average"""
+    assert period % 2 == 0, "FRAMA period must be even"
+    c = ohlc[column].copy()
+    window = batch * 2
+    hh = c.rolling(batch).max()
+    ll = c.rolling(batch).min()
+    n1 = (hh - ll) / batch
+    n2 = n1.shift(batch)
+    hh2 = c.rolling(window).max()
+    ll2 = c.rolling(window).min()
+    n3 = (hh2 - ll2) / window
+    D = (np.log(n1 + n2) - np.log(n3)) / np.log(2)
+    alp = np.exp(-4.6 * (D - 1))
+    alp = np.clip(alp, 0.01, 1).values
+    filt = np.zeros(len(c))
+    for i in range(len(c)):
+        if i < window:
+            filt[i] = c.iloc[i]
+        else:
+            filt[i] = c.iloc[i] * alp[i] + (1 - alp[i]) * filt[i - 1]
+    return pd.Series(filt, index=ohlc.index, name=f"FRAMA_{period}")
+
+def MACD(ohlc, period_fast = 12, period_slow = 26,signal = 9,column = "Close",adjust = True):
+        """
+        MACD, MACD Signal and MACD difference.
+        The MACD Line oscillates above and below the zero line, which is also known as the centerline.
+        These crossovers signal that the 12-day EMA has crossed the 26-day EMA. The direction, of course, depends on the direction of the moving average cross.
+        Positive MACD indicates that the 12-day EMA is above the 26-day EMA. Positive values increase as the shorter EMA diverges further from the longer EMA.
+        This means upside momentum is increasing. Negative MACD values indicates that the 12-day EMA is below the 26-day EMA.
+        Negative values increase as the shorter EMA diverges further below the longer EMA. This means downside momentum is increasing.
+
+        Signal line crossovers are the most common MACD signals. The signal line is a 9-day EMA of the MACD Line.
+        As a moving average of the indicator, it...curs when the MACD turns up and crosses above the signal line.
+        A bearish crossover occurs when the MACD turns down and crosses below the signal line.
+        """
+
+        EMA_fast = pd.Series(
+            ohlc[column].ewm(ignore_na=False, span=period_fast, adjust=adjust).mean(),
+            name="EMA_fast",
+        )
+        EMA_slow = pd.Series(
+            ohlc[column].ewm(ignore_na=False, span=period_slow, adjust=adjust).mean(),
+            name="EMA_slow",
+        )
+        MACD = pd.Series(EMA_fast - EMA_slow, name="MACD")
+        MACD_signal = pd.Series(
+            MACD.ewm(ignore_na=False, span=signal, adjust=adjust).mean(), name="SIGNAL"
+        )
+
+        return pd.concat([MACD, MACD_signal], axis=1)
+
+
+def BOLLINGER(ohlc, period=20, dev=2, column="Close"):
+    """Bollinger Bands"""
+    sma = ohlc[column].rolling(window=period).mean()
+    std = ohlc[column].rolling(window=period).std()
+    upper_band = sma + std * dev
+    lower_band = sma - std * dev
+    return pd.DataFrame({"BB_UPPER": upper_band, "BB_LOWER": lower_band})
+
+def STOCH(ohlc, period = 14,close="Close",high="High",low="Low"):
+        """Stochastic oscillator %K
+         The stochastic oscillator is a momentum indicator comparing the closing price of a security
+         to the range of its prices over a certain period of time.
+         The sensitivity of the oscillator to market movements is reducible by adjusting that time
+         period or by taking a moving average of the result.
+        """
+
+        highest_high = ohlc[high].rolling(center=False, window=period).max()
+        lowest_low = ohlc[low].rolling(center=False, window=period).min()
+
+        STOCH = pd.Series(
+            (ohlc[close] - lowest_low) / (highest_high - lowest_low) * 100,
+            name="{0} period STOCH %K".format(period),
+        )
+
+        return STOCH
+
+def STOCHD(ohlc, period = 3, stoch_period = 14,close="Close",high="High",low="Low"):
+        """Stochastic oscillator %D
+        STOCH%D is a 3 period simple moving average of %K.
+        """
+
+        return pd.Series(
+            STOCH(ohlc, period = stoch_period,close=close,high=high,low=low).rolling(center=False, window=period).mean(),
+            name="{0} period STOCH %D.".format(period),
+        )
+
+def STOCHRSI(ohlc, rsi_period=14, stoch_period=14, column="Close", adjust=True):
+    """Stochastic RSI"""
+    rsi = RSI(ohlc, rsi_period, column, adjust)
+    min_val = rsi.rolling(window=stoch_period).min()
+    max_val = rsi.rolling(window=stoch_period).max()
+    stochrsi = 100 * (rsi - min_val) / (max_val - min_val)
+    return pd.Series(stochrsi, name=f"STOCHRSI_{rsi_period}_{stoch_period}")
+
+def CMO(ohlc, period=9, factor=100, column="Close", adjust=True):
+    """Chande Momentum Oscillator"""
+    delta = ohlc[column].diff()
+    up = delta.copy()
+    down = delta.copy()
+    up[up < 0] = 0
+    down[down > 0] = 0
+    _gain = up.ewm(com=period, adjust=adjust).mean()
+    _loss = abs(down.ewm(com=period, adjust=adjust).mean())
+    return pd.Series(factor * ((_gain - _loss) / (_gain + _loss)), name="CMO")
+
+
+def EMV(ohlcv, period=14, high="High", low="Low", colvol="Volume"):
+    """Ease of Movement"""
+    dm = ((ohlcv[high] + ohlcv[low]) / 2) - ((ohlcv[high].shift() + ohlcv[low].shift()) / 2)
+    br = (ohlcv[colvol] / 100000000) / ((ohlcv[high] - ohlcv[low]))
+    emv = dm / br
+    return pd.Series(emv.rolling(window=period).mean(), name="EMV")
+
+
+def CHAIKIN(ohlcv, colvol="Volume", column="Close", high="High", low="Low", adjust=True):
+    """Chaikin Oscillator"""
+    adl = ADL(ohlcv, colvol, column, high, low)
+    return pd.Series(adl.ewm(span=3, adjust=adjust).mean() - adl.ewm(span=10, adjust=adjust).mean(), name="CHAIKIN")
+
+def ADL(ohlcv, colvol="Volume", column="Close", high="High", low="Low"):
+    """Accumulation/Distribution Line"""
+    clv = ((ohlcv[column] - ohlcv[low]) - (ohlcv[high] - ohlcv[column])) / (ohlcv[high] - ohlcv[low])
+    clv = clv.fillna(0)
+    return pd.Series((clv * ohlcv[colvol]).cumsum(), name="ADL")
+
+def OBV(ohlcv, column="Close", colvol="Volume"):
+    """On-Balance Volume"""
+    obv = [0]
+    for i in range(1, len(ohlcv)):
+        if ohlcv[column].iloc[i] > ohlcv[column].iloc[i - 1]:
+            obv.append(obv[-1] + ohlcv[colvol].iloc[i])
+        elif ohlcv[column].iloc[i] < ohlcv[column].iloc[i - 1]:
+            obv.append(obv[-1] - ohlcv[colvol].iloc[i])
+        else:
+            obv.append(obv[-1])
+    return pd.Series(obv, index=ohlcv.index, name="OBV")
+
+
+
+def ADX(ohlc, period=14, high="High", low="Low", close="Close", adjust=True):
+    """Average Directional Index"""
+    tr1 = ohlc[high] - ohlc[low]
+    tr2 = abs(ohlc[high] - ohlc[close].shift())
+    tr3 = abs(ohlc[low] - ohlc[close].shift())
+    tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+    atr = tr.ewm(span=period, min_periods=period).mean()
+
+    up_diff = ohlc[high].diff()
+    down_diff = ohlc[low].diff()
+    plus_dm = pd.Series(np.where((up_diff > down_diff) & (up_diff > 0), up_diff, 0), name="plus_dm")
+    minus_dm = pd.Series(np.where((down_diff > up_diff) & (down_diff > 0), down_diff, 0), name="minus_dm")
+
+    plus_di = 100 * (plus_dm.ewm(span=period, min_periods=period).mean() / atr)
+    minus_di = 100 * (minus_dm.ewm(span=period, min_periods=period).mean() / atr)
+    dx = 100 * abs(plus_di - minus_di) / (plus_di + minus_di)
+    adx = dx.ewm(span=period, min_periods=period).mean()
+    return pd.Series(adx, name=f"ADX_{period}")
+
+def EFI(ohlc, period=13, column="Close", colvol="Volume", adjust=True):
+    """Elder's Force Index"""
+    fi1 = pd.Series(ohlc[colvol] * ohlc[column].diff())
+    return pd.Series(fi1.ewm(ignore_na=False, min_periods=9, span=10, adjust=adjust).mean(), name="EFI")
+
+
+def WOBV(ohlcv, column="Close", colvol="Volume"):
+    """Weighted On-Balance Volume"""
+    obv = [0]
+    for i in range(1, len(ohlcv)):
+        delta = ohlcv[column].iloc[i] - ohlcv[column].iloc[i - 1]
+        obv.append(obv[-1] + delta * ohlcv[colvol].iloc[i])
+    return pd.Series(obv, index=ohlcv.index, name="WOBV")
+
+
+def DMI(ohlc, period=14, high="High", low="Low", column="Close"):
+    """Directional Movement Index"""
+    up_diff = ohlc[high].diff()
+    down_diff = ohlc[low].diff()
+    plus_dm = pd.Series(np.where((up_diff > down_diff) & (up_diff > 0), up_diff, 0), name="plus_dm")
+    minus_dm = pd.Series(np.where((down_diff > up_diff) & (down_diff > 0), down_diff, 0), name="minus_dm")
+    tr = pd.concat([ohlc[high] - ohlc[low], abs(ohlc[high] - ohlc[column].shift()), abs(ohlc[low] - ohlc[column].shift())], axis=1).max(axis=1)
+    atr = tr.ewm(span=period, min_periods=period).mean()
+    plus_di = 100 * (plus_dm.ewm(span=period, min_periods=period).mean() / atr)
+    minus_di = 100 * (minus_dm.ewm(span=period, min_periods=period).mean() / atr)
+    return pd.DataFrame({"+DI": plus_di, "-DI": minus_di})
+
+def CFI(ohlcv, column="Close", colvol="Volume", adjust=True):
+    """Cumulative Force Index"""
+    fi1 = pd.Series(ohlcv[colvol] * ohlcv[column].diff())
+    cfi = pd.Series(fi1.ewm(ignore_na=False, min_periods=9, span=10, adjust=adjust).mean(), name="CFI")
+    return cfi.cumsum()
+
+def EBBP(ohlc, period=13, high="High", low="Low", column="Close", adjust=True):
+    """Elder Bull Power / Bear Power"""
+    ema = ohlc[column].ewm(span=period, adjust=adjust).mean()
+    bull_power = ohlc[high] - ema
+    bear_power = ohlc[low] - ema
+    return pd.DataFrame({"Bull": bull_power, "Bear": bear_power}, index=ohlc.index)
+
+def ROC(ohlc, period=10, column="Close"):
+    """Rate of Change"""
+    return pd.Series(ohlc[column].pct_change(period) * 100, name=f"ROC_{period}")
+
+
+def CCI(ohlc, period=20, high="High", low="Low", close="Close"):
+    """Commodity Channel Index"""
+    tp = (ohlc[high] + ohlc[low] + ohlc[close]) / 3
+    sma = tp.rolling(window=period).mean()
+    mean_deviation = tp.rolling(window=period).apply(lambda x: np.fabs(x - x.mean()).mean())
+    cci = (tp - sma) / (0.015 * mean_deviation)
+    return pd.Series(cci, name=f"CCI_{period}")
+
+def COPP(ohlc, adjust = True):
+        """The Coppock Curve is a momentum indicator, it signals buying opportunities when the indicator moved from negative territory to positive territory."""
+
+        roc1 = ROC(ohlc, 14)
+        roc2 = ROC(ohlc, 11)
+
+        return pd.Series(
+            (roc1 + roc2).ewm(span=10, min_periods=9, adjust=adjust).mean(),
+            name="Coppock Curve",
+        )
+
+def VBM(ohlc, period=14, std_dev=2, column="Close"):
+    """Volatility-Based Momentum"""
+    volatility = ohlc[column].pct_change().rolling(window=period).std() * np.sqrt(period)
+    momentum = ohlc[column].pct_change(period)
+    return pd.Series(momentum / volatility, name="VBM")
+
+
+def QSTICK(ohlc, period=10, open="Open", close="Close"):
+    """Q Stick Indicator"""
+    return pd.Series(ohlc[close].pct_change(period) - ohlc[open].pct_change(period), name="QSTICK")
+
+def WTO(ohlc, channel_length=10, average_length=21, adjust=True):
+    """Wave Trend Oscillator"""
+    ap = (ohlc["High"] + ohlc["Low"] + ohlc["Close"]) / 3
+    esa = ap.ewm(span=average_length, adjust=adjust).mean()
+    d = pd.Series((ap - esa).abs().ewm(span=channel_length, adjust=adjust).mean(), name="d")
+    ci = (ap - esa) / (0.015 * d)
+    wt1 = pd.Series(ci.ewm(span=average_length, adjust=adjust).mean(), name="WT1.")
+    wt2 = pd.Series(wt1.rolling(window=4).mean(), name="WT2.")
+    return pd.concat([wt1, wt2], axis=1)
+
+def SAR(ohlc, af = 0.02, amax = 0.2,high="High",low="Low"):
+        """SAR stands for "stop and reverse," which is the actual indicator used in the system.
+        SAR trails price as the trend extends over time. The indicator is below prices when prices are rising and above prices when prices are falling.
+        In this regard, the indicator stops and reverses when the price trend reverses and breaks above or below the indicator."""
+        high1, low1 = ohlc[high], ohlc[low]
+
+        # Starting values
+        sig0, xpt0, af0 = True, high1[0], af
+        _sar = [low1[0] - (high1 - low1).std()]
+
+        for i in range(1, len(ohlc)):
+            sig1, xpt1, af1 = sig0, xpt0, af0
+
+            lmin = min(low1[i - 1], low1[i])
+            lmax = max(high1[i - 1], high1[i])
+
+            if sig1:
+                sig0 = low1[i] > _sar[-1]
+                xpt0 = max(lmax, xpt1)
+            else:
+                sig0 = high1[i] >= _sar[-1]
+                xpt0 = min(lmin, xpt1)
+
+            if sig0 == sig1:
+                sari = _sar[-1] + (xpt1 - _sar[-1]) * af1
+                af0 = min(amax, af1 + af)
+
+                if sig0:
+                    af0 = af0 if xpt0 > xpt1 else af1
+                    sari = min(sari, lmin)
+                else:
+                    af0 = af0 if xpt0 < xpt1 else af1
+                    sari = max(sari, lmax)
+            else:
+                af0 = af
+                sari = xpt0
+
+            _sar.append(sari)
+
+        return pd.Series(_sar, index=ohlc.index)
+
+def PSAR(ohlc, iaf = 0.02, maxaf = 0.2,high="High",low="Low",close="Close"):
+        """
+        The parabolic SAR indicator, developed by J. Wells Wilder, is used by traders to determine trend direction and potential reversals in price.
+        The indicator uses a trailing stop and reverse method called "SAR," or stop and reverse, to identify suitable exit and entry points.
+        Traders also refer to the indicator as the parabolic stop and reverse, parabolic SAR, or PSAR.
+        https://www.investopedia.com/terms/p/parabolicindicator.asp
+        https://virtualizedfrog.wordpress.com/2014/12/09/parabolic-sar-implementation-in-python/
+        """
+
+        length = len(ohlc)
+        high1, low1, close1 = ohlc[high], ohlc[low], ohlc[close]
+        psar = close1[0 : len(close1)]
+        psarbull = [None] * length
+        psarbear = [None] * length
+        bull = True
+        af = iaf
+        hp = high1[0]
+        lp = low1[0]
+
+        for i in range(2, length):
+            if bull:
+                psar[i] = psar[i - 1] + af * (hp - psar[i - 1])
+            else:
+                psar[i] = psar[i - 1] + af * (lp - psar[i - 1])
+
+            reverse = False
+
+            if bull:
+                if low1[i] < psar[i]:
+                    bull = False
+                    reverse = True
+                    psar[i] = hp
+                    lp = low1[i]
+                    af = iaf
+            else:
+                if high1[i] > psar[i]:
+                    bull = True
+                    reverse = True
+                    psar[i] = lp
+                    hp = high1[i]
+                    af = iaf
+
+            if not reverse:
+                if bull:
+                    if high1[i] > hp:
+                        hp = high1[i]
+                        af = min(af + iaf, maxaf)
+                    if low1[i - 1] < psar[i]:
+                        psar[i] = low1[i - 1]
+                    if low1[i - 2] < psar[i]:
+                        psar[i] = low1[i - 2]
+                else:
+                    if low1[i] < lp:
+                        lp = low1[i]
+                        af = min(af + iaf, maxaf)
+                    if high1[i - 1] > psar[i]:
+                        psar[i] = high1[i - 1]
+                    if high1[i - 2] > psar[i]:
+                        psar[i] = high1[i - 2]
+
+            if bull:
+                psarbull[i] = psar[i]
+            else:
+                psarbear[i] = psar[i]
+
+        psar = pd.Series(psar, name="psar", index=ohlc.index)
+        psarbear = pd.Series(psarbear, name="psarbear", index=ohlc.index)
+        psarbull = pd.Series(psarbull, name="psarbull", index=ohlc.index)
+
+        psar_df = pd.concat([psar, psarbull, psarbear], axis=1)
+
+        return psar_df
+
+def KST(ohlc, r1=10, r2=15, r3=20, r4=30, column="Close"):
+    """Know Sure Thing"""
+    r1 = ROC(ohlc, r1, column).rolling(window=10).mean()
+    r2 = ROC(ohlc, r2, column).rolling(window=10).mean()
+    r3 = ROC(ohlc, r3, column).rolling(window=10).mean()
+    r4 = ROC(ohlc, r4, column).rolling(window=15).mean()
+    k = pd.Series((r1 * 1) + (r2 * 2) + (r3 * 3) + (r4 * 4), name="KST")
+    signal = pd.Series(k.rolling(window=10).mean(), name="signal")
+    return pd.concat([k, signal], axis=1)
+
+def TSI(ohlc,long = 25,short = 13,signal = 13,column = "Close",adjust = True):
+        """True Strength Index (TSI) is a momentum oscillator based on a double smoothing of price changes."""
+
+        ## Double smoother price change
+        momentum = pd.Series(ohlc[column].diff())  ## 1 period momentum
+        _EMA25 = pd.Series(
+            momentum.ewm(span=long, min_periods=long - 1, adjust=adjust).mean(),
+            name="_price change EMA25",
+        )
+        _DEMA13 = pd.Series(
+            _EMA25.ewm(span=short, min_periods=short - 1, adjust=adjust).mean(),
+            name="_price change double smoothed DEMA13",
+        )
+
+        ## Double smoothed absolute price change
+        absmomentum = pd.Series(ohlc[column].diff().abs())
+        _aEMA25 = pd.Series(
+            absmomentum.ewm(span=long, min_periods=long - 1, adjust=adjust).mean(),
+            name="_abs_price_change EMA25",
+        )
+        _aDEMA13 = pd.Series(
+            _aEMA25.ewm(span=short, min_periods=short - 1, adjust=adjust).mean(),
+            name="_abs_price_change double smoothed DEMA13",
+        )
+
+        TSI = pd.Series((_DEMA13 / _aDEMA13) * 100, name="TSI")
+        signal = pd.Series(
+            TSI.ewm(span=signal, min_periods=signal - 1, adjust=adjust).mean(),
+            name="signal",
+        )
+
+        return pd.concat([TSI, signal], axis=1)
+
+def FISH(ohlc, period=10, adjust=True, high="High", low="Low"):
+    """Fisher Transform"""
+    med = (ohlc[high] + ohlc[low]) / 2
+    ndaylow = med.rolling(window=period).min()
+    ndayhigh = med.rolling(window=period).max()
+    raw = (2 * ((med - ndaylow) / (ndayhigh - ndaylow))) - 1
+    smooth = raw.ewm(span=5, adjust=adjust).mean()
+    _smooth = smooth.fillna(0)
+    return pd.Series(
+        np.log((1 + _smooth) / (1 - _smooth)).ewm(span=3, adjust=adjust).mean(),
+        name=f"FISH_{period}"
+    )
+
+def ICHIMOKU(ohlc, kijun_period=26, tenkan_period=9, senkou_period=52, chikou_period=26,
+              high="High", low="Low", close="Close", open="Open"):
+    """Ichimoku Cloud"""
+    tenkan_sen = (ohlc[high].rolling(window=tenkan_period).max() +
+                  ohlc[low].rolling(window=tenkan_period).min()) / 2
+    kijun_sen = (ohlc[high].rolling(window=kijun_period).max() +
+                 ohlc[low].rolling(window=kijun_period).min()) / 2
+    senkou_span_a = pd.Series(((tenkan_sen + kijun_sen) / 2).shift(kijun_period), name="SENKOU_A")
+    senkou_span_b = pd.Series(((ohlc[high].rolling(window=senkou_period).max() +
+                                ohlc[low].rolling(window=senkou_period).min()) / 2).shift(kijun_period), name="SENKOU_B")
+    chikou_span = pd.Series(ohlc[close].shift(-chikou_period), name="CHIKOU")
+    return pd.DataFrame({
+        "TENKAN": tenkan_sen,
+        "KIJUN": kijun_sen,
+        "SENKOU_A": senkou_span_a,
+        "SENKOU_B": senkou_span_b,
+        "CHIKOU": chikou_span
+    })
+
+
+def DC(ohlc, period=20, high="High", low="Low", close="Close", adjust=True):
+    """Donchian Channels"""
+    upper = ohlc[high].rolling(window=period).max()
+    lower = ohlc[low].rolling(window=period).min()
+    middle = (upper + lower) / 2
+    return pd.DataFrame({"DC_U": upper, "DC_L": lower, "DC_M": middle})
+
+
+
+def MFI(ohlc, period=14, high="High", low="Low", close="Close", colvol="Volume"):
+    """Money Flow Index"""
+    tp = TP(ohlc, high=high, low=low, column=close)
+    rmf = tp * ohlc[colvol]  # Raw Money Flow
+    mf_sign = np.sign(tp.diff())  # Positive or negative money flow
+    pos_mf = np.where(mf_sign == 1, rmf, 0)
+    neg_mf = np.where(mf_sign == -1, rmf, 0)
+
+    pos_mf_sum = pd.Series(pos_mf).rolling(window=period).sum()
+    neg_mf_sum = pd.Series(neg_mf).rolling(window=period).sum()
+
+    mfratio = pos_mf_sum / neg_mf_sum
+    mfi = 100 - (100 / (1 + mfratio))
+
+    return pd.Series(mfi, name=f"{period} period MFI")
+
+def MOM(ohlc, period = 10, column = "Close"):
+        """Market momentum is measured by continually taking price differences for a fixed time interval.
+        To construct a 10-day momentum line, simply subtract the closing price 10 days ago from the last closing price.
+        This positive or negative value is then plotted around a zero line."""
+
+        return pd.Series(ohlc[column].diff(period), name="MOM".format(period))
+
+def DYMI(ohlc, column = "Close", adjust = True):
+        """
+        The Dynamic Momentum Index is a variable term RSI. The RSI term varies from 3 to 30. The variable
+        time period makes the RSI more responsive to short-term moves. The more volatile the price is,
+        the shorter the time period is. It is interpreted in the same way as the RSI, but provides signals earlier.
+        Readings below 30 are considered oversold, and levels over 70 are considered overbought. The indicator
+        oscillates between 0 and 100.
+        https://www.investopedia.com/terms/d/dynamicmomentumindex.asp
+        """
+
+        def _get_time(close):
+            # Value available from 14th period
+            sd = close.rolling(5).std()
+            asd = sd.rolling(10).mean()
+            v = sd / asd
+            t = 14 / v.round()
+            t[t.isna()] = 0
+            t = t.map(lambda x: int(min(max(x, 5), 30)))
+            return t
+
+        def _dmi(index):
+            time = t.iloc[index]
+            if (index - time) < 0:
+                subset = ohlc.iloc[0:index]
+            else:
+                subset = ohlc.iloc[(index - time) : index]
+            return RSI(subset, period=time, column = column,adjust=adjust).values[-1]
+
+        dates = pd.Series(ohlc.index)
+        periods = pd.Series(data=range(14, len(dates)), index=ohlc.index[14:].values)
+        t = _get_time(ohlc[column])
+        return periods.map(lambda x: _dmi(x))
+
+def VPT(ohlcv, colvol="Volume", column="Close", open="Open", high="High", low="Low"):
+    """Volume Price Trend"""
+    hilow = (ohlcv[high] - ohlcv[low]) * 100
+    openclose = (ohlcv[column] - ohlcv[open]) * 100
+    vol = ohlcv[colvol] / hilow
+    spreadvol = (openclose * vol).cumsum()
+    vpt = spreadvol + spreadvol
+    return pd.Series(vpt, name="VPT")
+
+def FVE(ohlcv, period=22, factor=0.3, colvol="Volume", column="Close", open="Open", high="High", low="Low"):
+    """Fractal Volume Efficiency"""
+    mf = (ohlcv[column] - ((ohlcv[high] + ohlcv[low]) / 2))
+    smav = ohlcv[column].rolling(window=period).mean()
+    vol_shift = pd.Series(np.where(mf > factor * ohlcv[column] / 100,
+                                 ohlcv[colvol],
+                                 np.where(mf < -factor * ohlcv[column] / 100,
+                                          -ohlcv[colvol], 0)),
+                           index=ohlcv.index)
+    _sum = vol_shift.rolling(window=period).sum()
+    return pd.Series((_sum / smav) / period * 100, name="FVE")
+
+def PPO(ohlcv, fast=12, slow=26, signal=9, column="Close", colvol="Volume", adjust=True):
+    """Price Percentage Oscillator"""
+    _fast = ohlcv[column].ewm(span=fast, adjust=adjust).mean()
+    _slow = ohlcv[column].ewm(span=slow, adjust=adjust).mean()
+    ppo = pd.Series(((_fast - _slow) / _slow) * 100, name="PPO")
+    signal_line = ppo.ewm(span=signal, adjust=adjust).mean()
+    histogram = pd.Series(ppo - signal_line, name="PPO_histo")
+    return pd.DataFrame({"PPO": ppo, "PPO_signal": signal_line, "PPO_histo": histogram})
+
+def VW_MACD(ohlcv, period_fast=12, period_slow=26, signal=9, column="Close", colvol="Volume", adjust=True):
+    """Volume Weighted MACD"""
+    vp = (ohlcv[column] * ohlcv[colvol]).ewm(span=period_fast, adjust=adjust).mean()
+    vslow = (ohlcv[column] * ohlcv[colvol]).ewm(span=period_slow, adjust=adjust).mean()
+    vfast = (ohlcv[column] * ohlcv[colvol]).ewm(span=period_fast, adjust=adjust).mean()
+    macd = pd.Series(vp - vslow, name="VW_MACD")
+    signal_line = macd.ewm(span=signal, adjust=adjust).mean()
+    return pd.DataFrame({"VW_MACD": macd, "Signal": signal_line})
+
+
+def AO(ohlc, high="High", low="Low"):
+    """Awesome Oscillator"""
+    median_price = (ohlc[high] + ohlc[low]) / 2
+    ao = median_price.rolling(window=5).mean() - median_price.rolling(window=34).mean()
+    return pd.Series(ao, name="AO")
+
+def MI(ohlc, period=9, adjust=True, high="High", low="Low"):
+    """Mass Index"""
+    _range = ohlc[high] - ohlc[low]
+    EMA9 = _range.ewm(span=period, ignore_na=False, adjust=adjust).mean()
+    DEMA9 = EMA9.ewm(span=period, ignore_na=False, adjust=adjust).mean()
+    mass = EMA9 / DEMA9
+    return pd.Series(mass.rolling(window=25).sum(), name="MI")
+
+
+def PZO(ohlcv, period=14, column="Close", colvol="Volume", adjust=True):
+    """Price Zone Oscillator"""
+    pzo = ohlcv[column].pct_change(period)
+    return pd.Series(pzo.ewm(span=period, adjust=adjust).mean(), name="PZO")
+
+def UO(ohlc, period=14, high="High", low="Low", close="Close", column="Close"):
+    """Ultimate Oscillator"""
+    bp = ohlc[column] - ohlc[[low, column]].min(axis=1)
+    tr = pd.concat([
+        ohlc[high] - ohlc[low],
+        abs(ohlc[high] - ohlc[close].shift()),
+        abs(ohlc[low] - ohlc[close].shift())
+    ], axis=1).max(axis=1)
+    avg7 = bp.rolling(window=7).sum() / tr.rolling(window=7).sum()
+    avg14 = bp.rolling(window=14).sum() / tr.rolling(window=14).sum()
+    avg28 = bp.rolling(window=28).sum() / tr.rolling(window=28).sum()
+    uo = (avg7 * 4 + avg14 * 2 + avg28) / (4 + 2 + 1)
+    return pd.Series(uo * 100, name="UO")
+
+def BASP(ohlc, period = 40, adjust = True,colvol="Volume",high="High",low="Low",close="Close"):
+        """BASP indicator serves to identify buying and selling pressure."""
+
+        sp = ohlc[high] - ohlc[close]
+        bp = ohlc[close] - ohlc[low]
+        spavg = sp.ewm(span=period, adjust=adjust).mean()
+        bpavg = bp.ewm(span=period, adjust=adjust).mean()
+
+        nbp = bp / bpavg
+        nsp = sp / spavg
+
+        varg = ohlc[colvol].ewm(span=period, adjust=adjust).mean()
+        nv = ohlc[colvol] / varg
+
+        nbfraw = pd.Series(nbp * nv, name="Buy.")
+        nsfraw = pd.Series(nsp * nv, name="Sell.")
+
+        return pd.concat([nbfraw, nsfraw], axis=1)
+
+def BASPN(ohlcv, period=40, adjust=True, colvol="Volume", high="High", low="Low", close="Close"):
+    """Normalized Buyer/Seller Pressure"""
+    sp = ohlcv[high] - ohlcv[close]
+    bp = ohlcv[close] - ohlcv[low]
+    spavg = sp.ewm(span=period, adjust=adjust).mean()
+    bpavg = bp.ewm(span=period, adjust=adjust).mean()
+    nbp = bp / bpavg
+    nsp = sp / spavg
+    nbf = pd.Series((nbp * (ohlcv[colvol] / spavg)).ewm(span=20, adjust=adjust).mean(), name="Buy.")
+    nsf = pd.Series((nsp * (ohlcv[colvol] / spavg)).ewm(span=20, adjust=adjust).mean(), name="Sell.")
+    return pd.DataFrame({"BASPN_Buy": nbf, "BASPN_Sell": nsf})
+
+def IFT_RSI(ohlc, rsi_period=5, wma_period=9, column="Close", adjust=True):
+    """Inverse Fisher Transform RSI"""
+    rsi = RSI(ohlc, rsi_period, column, adjust)
+    v1 = pd.Series(0.1 * (rsi - 50), name="v1")
+    weights = np.arange(1, wma_period + 1)
+    d = (wma_period * (wma_period + 1)) / 2
+    _wma = v1.rolling(wma_period, min_periods=wma_period)
+    v2 = _wma.apply(lambda x: np.dot(x, weights) / d, raw=True)
+    ift = pd.Series(((v2 ** 2 - 1) / (v2 ** 2 + 1)), name="IFT_RSI")
+    return ift
+
+
+def PIVOT(ohlc, open="Open", close="Close", high="High", low="Low"):
+    """Classic Pivot Points"""
+    df = ohlc.shift()
+    pp = pd.Series((df[high] + df[low] + df[close]) / 3, name="pivot")
+    r1 = pd.Series(2 * pp - df[low], name="r1")
+    r2 = pd.Series(pp + (df[high] - df[low]), name="r2")
+    r3 = pd.Series(df[high] + 2 * (pp - df[low]), name="r3")
+    s1 = pd.Series(2 * pp - df[high], name="s1")
+    s2 = pd.Series(pp - (df[high] - df[low]), name="s2")
+    s3 = pd.Series(pp - 2 * (df[high] - df[low]), name="s3")
+    return pd.concat([pp, s1, s2, s3, r1, r2, r3], axis=1)
+
+def PIVOT_FIB(ohlc, open="Open", close="Close", high="High", low="Low"):
+    """Fibonacci Pivot Points"""
+    df = ohlc.shift()
+    pp = pd.Series((df[high] + df[low] + df[close]) / 3, name="pivot")
+    s1 = pd.Series(pp - 0.382 * (df[high] - df[low]), name="s1")
+    s2 = pd.Series(pp - 0.618 * (df[high] - df[low]), name="s2")
+    s3 = pd.Series(pp - 1.0 * (df[high] - df[low]), name="s3")
+    r1 = pd.Series(pp + 0.382 * (df[high] - df[low]), name="r1")
+    r2 = pd.Series(pp + 0.618 * (df[high] - df[low]), name="r2")
+    r3 = pd.Series(pp + 1.0 * (df[high] - df[low]), name="r3")
+    return pd.concat([pp, s1, s2, s3, r1, r2, r3], axis=1)
+
+def KC(ohlc, period=20, atr_period=10, kc_mult=2, high="High", low="Low", column="Close", adjust=True):
+    """Keltner Channels"""
+    tp = (ohlc[high] + ohlc[low] + ohlc[column]) / 3
+    kc_middle = tp.ewm(span=period, adjust=adjust).mean()
+    tr = pd.concat([
+        ohlc[high] - ohlc[low],
+        abs(ohlc[high] - ohlc[column].shift()),
+        abs(ohlc[low] - ohlc[column].shift())
+    ], axis=1).max(axis=1)
+    mean_dev = tr.ewm(span=atr_period, adjust=adjust).mean()
+    kc_upper = kc_middle + kc_mult * mean_dev
+    kc_lower = kc_middle - kc_mult * mean_dev
+    return pd.DataFrame({
+        "KC_MIDDLE": kc_middle,
+        "KC_UPPER": kc_upper,
+        "KC_LOWER": kc_lower
+    })
+
+def APZ(ohlc, period=21, dev_factor=2, column="Close", high="High", low="Low", adjust=True):
+    """Adaptive Price Zone"""
+    ma = ohlc[column].ewm(span=period, adjust=adjust).mean()
+    std = ohlc[column].pct_change().rolling(window=period).std() * dev_factor
+    upper_band = ma + std * ohlc[column]
+    lower_band = ma - std * ohlc[column]
+    return pd.DataFrame({"APZ_UPPER": upper_band, "APZ_LOWER": lower_band})
+
+def VZO(ohlc,period = 14,column = "Close",colvol="Volume",adjust = True):
+        """VZO uses price, previous price and moving averages to compute its oscillating value.
+        It is a leading indicator that calculates buy and sell signals based on oversold / overbought conditions.
+        Oscillations between the 5% and 40% levels mark a bullish trend zone, while oscillations between -40% and 5% mark a bearish trend zone.
+        Meanwhile, readings above 40% signal an overbought condition, while readings above 60% signal an extremely overbought condition.
+        Alternatively, readings below -40% indicate an oversold condition, which becomes extremely oversold below -60%."""
+
+        sign = lambda a: (a > 0) - (a < 0)
+        r = ohlc[column].diff().apply(sign) * ohlc[colvol]
+        dvma = r.ewm(span=period, adjust=adjust).mean()
+        vma = ohlc[colvol].ewm(span=period, adjust=adjust).mean()
+
+        return pd.Series(100 * (dvma / vma), name="VZO")
+
+
+
+def TR(ohlc,high="High",low="Low",close="Close"):
+        """True Range is the maximum of three price ranges.
+        Most recent period's high minus the most recent period's low.
+        Absolute value of the most recent period's high minus the previous close.
+        Absolute value of the most recent period's low minus the previous close."""
+
+        TR1 = pd.Series(ohlc[high] - ohlc[low]).abs()  # True Range = High less Low
+
+        TR2 = pd.Series(
+            ohlc[high] - ohlc[close].shift()
+        ).abs()  # True Range = High less Previous Close
+
+        TR3 = pd.Series(
+            ohlc[close].shift() - ohlc[low]
+        ).abs()  # True Range = Previous Close less Low
+
+        _TR = pd.concat([TR1, TR2, TR3], axis=1)
+
+        _TR["TR"] = _TR.max(axis=1)
+
+        return pd.Series(_TR["TR"], name="TR")
+
+def ATR(ohlc, period = 14,high="High",low="Low",close="Close"):
+        """Average True Range is moving average of True Range."""
+
+        mytr=TR(ohlc,high=high,low=low,close=close)
+        return pd.Series(
+            mytr.rolling(center=False, window=period).mean(),
+            name="{0} period ATR".format(period),
+        )
+
+def CHANDELIER(ohlc, short_period=22, long_period=22, k=3, high="High", low="Low"):
+    """Chandelier Exit"""
+    long_stop = ohlc[high].rolling(window=long_period).max() - ATR(ohlc, 22) * k
+    short_stop = ohlc[low].rolling(window=short_period).min() + ATR(ohlc, 22) * k
+    return pd.DataFrame({"CHANDELIER_Long": long_stop, "CHANDELIER_Short": short_stop})