def cci(high, low, close, length=None, c=None, offset=None, **kwargs): """Indicator: Commodity Channel Index (CCI)""" # Validate Arguments high = verify_series(high) low = verify_series(low) close = verify_series(close) length = int(length) if length and length > 0 else 14 c = float(c) if c and c > 0 else 0.015 offset = get_offset(offset) # Calculate Result typical_price = hlc3(high=high, low=low, close=close) mean_typical_price = sma(typical_price, length=length) mad_typical_price = mad(typical_price, length=length) cci = typical_price - mean_typical_price cci /= c * mad_typical_price # Offset if offset != 0: cci = cci.shift(offset) # Handle fills if "fillna" in kwargs: cci.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: cci.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it cci.name = f"CCI_{length}_{c}" cci.category = "momentum" return cci
def aberration(high, low, close, length=None, atr_length=None, offset=None, **kwargs): """Indicator: Aberration (ABER)""" # Validate arguments high = verify_series(high) low = verify_series(low) close = verify_series(close) length = int(length) if length and length > 0 else 5 atr_length = int(atr_length) if atr_length and atr_length > 0 else 15 offset = get_offset(offset) # Calculate Result atr_ = atr(high=high, low=low, close=close, length=atr_length) jg = hlc3(high=high, low=low, close=close) zg = sma(jg, length) sg = zg + atr_ xg = zg - atr_ # Offset if offset != 0: zg = zg.shift(offset) sg = sg.shift(offset) xg = xg.shift(offset) atr_ = atr_.shift(offset) # Handle fills if "fillna" in kwargs: zg.fillna(kwargs["fillna"], inplace=True) sg.fillna(kwargs["fillna"], inplace=True) xg.fillna(kwargs["fillna"], inplace=True) atr_.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: zg.fillna(method=kwargs["fill_method"], inplace=True) sg.fillna(method=kwargs["fill_method"], inplace=True) xg.fillna(method=kwargs["fill_method"], inplace=True) atr_.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = f"_{length}_{atr_length}" zg.name = f"ABER_ZG{_props}" sg.name = f"ABER_SG{_props}" xg.name = f"ABER_XG{_props}" atr_.name = f"ABER_ATR{_props}" zg.category = sg.category = "volatility" xg.category = atr_.category = zg.category # Prepare DataFrame to return data = {zg.name: zg, sg.name: sg, xg.name: xg, atr_.name: atr_} aberdf = DataFrame(data) aberdf.name = f"ABER{_props}" aberdf.category = zg.category return aberdf
def kvo(high, low, close, volume, fast=None, slow=None, signal=None, mamode=None, drift=None, offset=None, **kwargs): """Indicator: Klinger Volume Oscillator (KVO)""" # Validate arguments fast = int(fast) if fast and fast > 0 else 34 slow = int(slow) if slow and slow > 0 else 55 signal = int(signal) if signal and signal > 0 else 13 mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema" _length = max(fast, slow, signal) high = verify_series(high, _length) low = verify_series(low, _length) close = verify_series(close, _length) volume = verify_series(volume, _length) drift = get_drift(drift) offset = get_offset(offset) if high is None or low is None or close is None or volume is None: return # Calculate Result signed_volume = volume * signed_series(hlc3(high, low, close), 1) sv = signed_volume.loc[signed_volume.first_valid_index():, ] kvo = ma(mamode, sv, length=fast) - ma(mamode, sv, length=slow) kvo_signal = ma(mamode, kvo.loc[kvo.first_valid_index():, ], length=signal) # Offset if offset != 0: kvo = kvo.shift(offset) kvo_signal = kvo_signal.shift(offset) # Handle fills if "fillna" in kwargs: kvo.fillna(kwargs["fillna"], inplace=True) kvo_signal.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: kvo.fillna(method=kwargs["fill_method"], inplace=True) kvo_signal.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = f"_{fast}_{slow}_{signal}" kvo.name = f"KVO{_props}" kvo_signal.name = f"KVOs{_props}" kvo.category = kvo_signal.category = "volume" # Prepare DataFrame to return data = {kvo.name: kvo, kvo_signal.name: kvo_signal} df = DataFrame(data) df.name = f"KVO{_props}" df.category = kvo.category return df
def mfi(high, low, close, volume, length=None, talib=None, drift=None, offset=None, **kwargs): """Indicator: Money Flow Index (MFI)""" # Validate arguments length = int(length) if length and length > 0 else 14 high = verify_series(high, length) low = verify_series(low, length) close = verify_series(close, length) volume = verify_series(volume, length) drift = get_drift(drift) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if high is None or low is None or close is None or volume is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import MFI mfi = MFI(high, low, close, volume, length) else: typical_price = hlc3(high=high, low=low, close=close) raw_money_flow = typical_price * volume tdf = DataFrame({"diff": 0, "rmf": raw_money_flow, "+mf": 0, "-mf": 0}) tdf.loc[(typical_price.diff(drift) > 0), "diff"] = 1 tdf.loc[tdf["diff"] == 1, "+mf"] = raw_money_flow tdf.loc[(typical_price.diff(drift) < 0), "diff"] = -1 tdf.loc[tdf["diff"] == -1, "-mf"] = raw_money_flow psum = tdf["+mf"].rolling(length).sum() nsum = tdf["-mf"].rolling(length).sum() tdf["mr"] = psum / nsum mfi = 100 * psum / (psum + nsum) tdf["mfi"] = mfi # Offset if offset != 0: mfi = mfi.shift(offset) # Handle fills if "fillna" in kwargs: mfi.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: mfi.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it mfi.name = f"MFI_{length}" mfi.category = "volume" return mfi
def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=None, drift=None, offset=None, **kwargs): """Indicator: Klinger Volume Oscillator (KVO)""" # Validate arguments fast = int(fast) if fast and fast > 0 else 34 slow = int(slow) if slow and slow > 0 else 55 length_sig = int(length_sig) if length_sig and length_sig > 0 else 13 mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema" _length = max(fast, slow, length_sig) high = verify_series(high, _length) low = verify_series(low, _length) close = verify_series(close, _length) volume = verify_series(volume, _length) drift = get_drift(drift) offset = get_offset(offset) if high is None or low is None or close is None or volume is None: return # Calculate Result mom = hlc3(high, low, close).diff(drift) trend = npWhere(mom > 0, 1, 0) + npWhere(mom < 0, -1, 0) dm = non_zero_range(high, low) m = high.size cm = [0] * m for i in range(1, m): cm[i] = (cm[i - 1] + dm[i]) if trend[i] == trend[i - 1] else (dm[i - 1] + dm[i]) vf = 100 * volume * trend * abs(2 * dm / cm - 1) kvo = ma(mamode, vf, length=fast) - ma(mamode, vf, length=slow) kvo_signal = ma(mamode, kvo, length=length_sig) # Offset if offset != 0: kvo = kvo.shift(offset) kvo_signal = kvo_signal.shift(offset) # Handle fills if "fillna" in kwargs: kvo.fillna(kwargs["fillna"], inplace=True) kvo_signal.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: kvo.fillna(method=kwargs["fill_method"], inplace=True) kvo_signal.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it kvo.name = f"KVO_{fast}_{slow}" kvo_signal.name = f"KVOSig_{length_sig}" kvo.category = kvo_signal.category = "volume" # Prepare DataFrame to return data = {kvo.name: kvo, kvo_signal.name: kvo_signal} kvoandsig = DataFrame(data) kvoandsig.name = f"KVO_{fast}_{slow}_{length_sig}" kvoandsig.category = kvo.category return kvoandsig
def mfi(high, low, close, volume, length=None, drift=None, offset=None, **kwargs): """Indicator: Money Flow Index (MFI)""" # Validate arguments high = verify_series(high) low = verify_series(low) close = verify_series(close) volume = verify_series(volume) length = int(length) if length and length > 0 else 14 drift = get_drift(drift) offset = get_offset(offset) # Calculate Result typical_price = hlc3(high=high, low=low, close=close) raw_money_flow = typical_price * volume tdf = DataFrame({"diff": 0, "rmf": raw_money_flow, "+mf": 0, "-mf": 0}) tdf.loc[(typical_price.diff(drift) > 0), "diff"] = 1 tdf.loc[tdf["diff"] == 1, "+mf"] = raw_money_flow tdf.loc[(typical_price.diff(drift) < 0), "diff"] = -1 tdf.loc[tdf["diff"] == -1, "-mf"] = raw_money_flow psum = tdf["+mf"].rolling(length).sum() nsum = tdf["-mf"].rolling(length).sum() tdf["mr"] = psum / nsum mfi = 100 * psum / (psum + nsum) tdf["mfi"] = mfi # Offset if offset != 0: mfi = mfi.shift(offset) # Handle fills if "fillna" in kwargs: mfi.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: mfi.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it mfi.name = f"MFI_{length}" mfi.category = "volume" return mfi
def cci(high, low, close, length=None, c=None, talib=None, offset=None, **kwargs): """Indicator: Commodity Channel Index (CCI)""" # Validate Arguments length = int(length) if length and length > 0 else 14 c = float(c) if c and c > 0 else 0.015 high = verify_series(high, length) low = verify_series(low, length) close = verify_series(close, length) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if high is None or low is None or close is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import CCI cci = CCI(high, low, close, length) else: typical_price = hlc3(high=high, low=low, close=close) mean_typical_price = sma(typical_price, length=length) mad_typical_price = mad(typical_price, length=length) cci = typical_price - mean_typical_price cci /= c * mad_typical_price # Offset if offset != 0: cci = cci.shift(offset) # Handle fills if "fillna" in kwargs: cci.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: cci.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it cci.name = f"CCI_{length}_{c}" cci.category = "momentum" return cci