def amat(close=None, fast=None, slow=None, mamode=None, lookback=None, offset=None, **kwargs): """Indicator: Archer Moving Averages Trends (AMAT)""" # Validate Arguments close = verify_series(close) fast = int(fast) if fast and fast > 0 else 8 slow = int(slow) if slow and slow > 0 else 21 lookback = int(lookback) if lookback and lookback > 0 else 2 mamode = mamode.lower() if mamode else "ema" offset = get_offset(offset) # Calculate Result if mamode == "hma": fast_ma = hma(close=close, length=fast, **kwargs) slow_ma = hma(close=close, length=slow, **kwargs) elif mamode == "linreg": fast_ma = linreg(close=close, length=fast, **kwargs) slow_ma = linreg(close=close, length=slow, **kwargs) elif mamode == "rma": fast_ma = rma(close=close, length=fast, **kwargs) slow_ma = rma(close=close, length=slow, **kwargs) elif mamode == "sma": fast_ma = sma(close=close, length=fast, **kwargs) slow_ma = sma(close=close, length=slow, **kwargs) elif mamode == "wma": fast_ma = wma(close=close, length=fast, **kwargs) slow_ma = wma(close=close, length=slow, **kwargs) else: # "ema" fast_ma = ema(close=close, length=fast, **kwargs) slow_ma = ema(close=close, length=slow, **kwargs) mas_long = long_run(fast_ma, slow_ma, length=lookback) mas_short = short_run(fast_ma, slow_ma, length=lookback) # Offset if offset != 0: mas_long = mas_long.shift(offset) mas_short = mas_short.shift(offset) # # Handle fills if "fillna" in kwargs: mas_long.fillna(kwargs["fillna"], inplace=True) mas_short.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: mas_long.fillna(method=kwargs["fill_method"], inplace=True) mas_short.fillna(method=kwargs["fill_method"], inplace=True) # Prepare DataFrame to return amatdf = DataFrame({ f"AMAT_{mas_long.name}": mas_long, f"AMAT_{mas_short.name}": mas_short }) # Name and Categorize it amatdf.name = f"AMAT_{mamode.upper()}_{fast}_{slow}_{lookback}" amatdf.category = "trend" return amatdf
def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs): """Indicator: Chande Forcast Oscillator (CFO)""" # Validate Arguments close = verify_series(close) length = int(length) if length and length > 0 else 9 scalar = float(scalar) if scalar else 100 drift = get_drift(drift) offset = get_offset(offset) # Finding linear regression of Series cfo = scalar * (close - linreg(close, length=length, tsf=True)) cfo /= close # Offset if offset != 0: cfo = cfo.shift(offset) # Handle fills if "fillna" in kwargs: cfo.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: cfo.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it cfo.name = f"CFO_{length}" cfo.category = "momentum" return cfo
def cti(close, length=None, offset=None, **kwargs) -> Series: """Indicator: Correlation Trend Indicator""" length = int(length) if length and length > 0 else 12 close = verify_series(close, length) offset = get_offset(offset) if close is None: return cti = linreg(close, length=length, r=True) # Offset if offset != 0: cti = cti.shift(offset) # Handle fills if "fillna" in kwargs: cti.fillna(method=kwargs["fillna"], inplace=True) if "fill_method" in kwargs: cti.fillna(method=kwargs["fill_method"], inplace=True) cti.name = f"CTI_{length}" cti.category = "momentum" return cti
def squeeze(high, low, close, bb_length=None, bb_std=None, kc_length=None, kc_scalar=None, mom_length=None, mom_smooth=None, use_tr=None, offset=None, **kwargs): """Indicator: Squeeze Momentum (SQZ)""" # Validate arguments high = verify_series(high) low = verify_series(low) close = verify_series(close) offset = get_offset(offset) bb_length = int(bb_length) if bb_length and bb_length > 0 else 20 bb_std = float(bb_std) if bb_std and bb_std > 0 else 2. kc_length = int(kc_length) if kc_length and kc_length > 0 else 20 kc_scalar = float(kc_scalar) if kc_scalar and kc_scalar > 0 else 1.5 mom_length = int(mom_length) if mom_length and mom_length > 0 else 12 mom_smooth = int(mom_smooth) if mom_smooth and mom_smooth > 0 else 6 use_tr = kwargs.setdefault("tr", True) asint = kwargs.pop("asint", True) mamode = kwargs.pop("mamode", "sma").lower() lazybear = kwargs.pop("lazybear", False) detailed = kwargs.pop("detailed", False) def simplify_columns(df, n=3): df.columns = df.columns.str.lower() return [c.split('_')[0][n - 1:n] for c in df.columns] # Calculate Result bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode) kch = kc(high, low, close, length=kc_length, scalar=kc_scalar, mamode=mamode, tr=use_tr) # Simplify KC and BBAND column names for dynamic access bbd.columns = simplify_columns(bbd) kch.columns = simplify_columns(kch) if lazybear: highest_high = high.rolling(kc_length).max() lowest_low = low.rolling(kc_length).min() avg_ = 0.25 * (highest_high + lowest_low) + 0.5 * kch.b squeeze = linreg(close - avg_, length=kc_length) else: momo = mom(close, length=mom_length) if mamode == "ema": squeeze = ema(momo, length=mom_smooth) else: squeeze = sma(momo, length=mom_smooth) # Classify Squeezes squeeze_on = (bbd.l > kch.l) & (bbd.u < kch.u) squeeze_off = (bbd.l < kch.l) & (bbd.u > kch.u) no_squeeze = ~squeeze_on & ~squeeze_off # Offset if offset != 0: squeeze = squeeze.shift(offset) squeeze_on = squeeze_on.shift(offset) squeeze_off = squeeze_off.shift(offset) no_squeeze = no_squeeze.shift(offset) # Handle fills if "fillna" in kwargs: squeeze.fillna(kwargs["fillna"], inplace=True) squeeze_on.fillna(kwargs["fillna"], inplace=True) squeeze_off.fillna(kwargs["fillna"], inplace=True) no_squeeze.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: squeeze.fillna(method=kwargs["fill_method"], inplace=True) squeeze_on.fillna(method=kwargs["fill_method"], inplace=True) squeeze_off.fillna(method=kwargs["fill_method"], inplace=True) no_squeeze.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = "" if use_tr else "hlr" _props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar}" _props += "_LB" if lazybear else "" squeeze.name = f"SQZ{_props}" data = { squeeze.name: squeeze, f"SQZ_ON": squeeze_on.astype(int) if asint else squeeze_on, f"SQZ_OFF": squeeze_off.astype(int) if asint else squeeze_off, f"SQZ_NO": no_squeeze.astype(int) if asint else no_squeeze } df = DataFrame(data) df.name = squeeze.name df.category = squeeze.category = "momentum" # Detailed Squeeze Series if detailed: pos_squeeze = squeeze[squeeze >= 0] neg_squeeze = squeeze[squeeze < 0] pos_inc, pos_dec = unsigned_differences(pos_squeeze, asint=True) neg_inc, neg_dec = unsigned_differences(neg_squeeze, asint=True) pos_inc *= squeeze pos_dec *= squeeze neg_dec *= squeeze neg_inc *= squeeze pos_inc.replace(0, npNaN, inplace=True) pos_dec.replace(0, npNaN, inplace=True) neg_dec.replace(0, npNaN, inplace=True) neg_inc.replace(0, npNaN, inplace=True) sqz_inc = squeeze * increasing(squeeze) sqz_dec = squeeze * decreasing(squeeze) sqz_inc.replace(0, npNaN, inplace=True) sqz_dec.replace(0, npNaN, inplace=True) # Handle fills if "fillna" in kwargs: sqz_inc.fillna(kwargs["fillna"], inplace=True) sqz_dec.fillna(kwargs["fillna"], inplace=True) pos_inc.fillna(kwargs["fillna"], inplace=True) pos_dec.fillna(kwargs["fillna"], inplace=True) neg_dec.fillna(kwargs["fillna"], inplace=True) neg_inc.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: sqz_inc.fillna(method=kwargs["fill_method"], inplace=True) sqz_dec.fillna(method=kwargs["fill_method"], inplace=True) pos_inc.fillna(method=kwargs["fill_method"], inplace=True) pos_dec.fillna(method=kwargs["fill_method"], inplace=True) neg_dec.fillna(method=kwargs["fill_method"], inplace=True) neg_inc.fillna(method=kwargs["fill_method"], inplace=True) df[f"SQZ_INC"] = sqz_inc df[f"SQZ_DEC"] = sqz_dec df[f"SQZ_PINC"] = pos_inc df[f"SQZ_PDEC"] = pos_dec df[f"SQZ_NDEC"] = neg_dec df[f"SQZ_NINC"] = neg_inc return df
def aobv(close, volume, fast=None, slow=None, mamode=None, max_lookback=None, min_lookback=None, offset=None, **kwargs): """Indicator: Archer On Balance Volume (AOBV)""" # Validate arguments close = verify_series(close) volume = verify_series(volume) offset = get_offset(offset) fast = int(fast) if fast and fast > 0 else 4 slow = int(slow) if slow and slow > 0 else 12 max_lookback = int( max_lookback) if max_lookback and max_lookback > 0 else 2 min_lookback = int( min_lookback) if min_lookback and min_lookback > 0 else 2 if slow < fast: fast, slow = slow, fast mamode = mamode.upper() if mamode else None run_length = kwargs.pop("run_length", 2) # Calculate Result obv_ = obv(close=close, volume=volume, **kwargs) if mamode is None or mamode == "EMA": mamode = "EMA" maf = ema(close=obv_, length=fast, **kwargs) mas = ema(close=obv_, length=slow, **kwargs) elif mamode == "HMA": maf = hma(close=obv_, length=fast, **kwargs) mas = hma(close=obv_, length=slow, **kwargs) elif mamode == "LINREG": maf = linreg(close=obv_, length=fast, **kwargs) mas = linreg(close=obv_, length=slow, **kwargs) elif mamode == "SMA": maf = sma(close=obv_, length=fast, **kwargs) mas = sma(close=obv_, length=slow, **kwargs) elif mamode == "WMA": maf = wma(close=obv_, length=fast, **kwargs) mas = wma(close=obv_, length=slow, **kwargs) # When MAs are long and short obv_long = long_run(maf, mas, length=run_length) obv_short = short_run(maf, mas, length=run_length) # Offset if offset != 0: obv_ = obv_.shift(offset) maf = maf.shift(offset) mas = mas.shift(offset) obv_long = obv_long.shift(offset) obv_short = obv_short.shift(offset) # # Handle fills if "fillna" in kwargs: obv_.fillna(kwargs["fillna"], inplace=True) maf.fillna(kwargs["fillna"], inplace=True) mas.fillna(kwargs["fillna"], inplace=True) obv_long.fillna(kwargs["fillna"], inplace=True) obv_short.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: obv_.fillna(method=kwargs["fill_method"], inplace=True) maf.fillna(method=kwargs["fill_method"], inplace=True) mas.fillna(method=kwargs["fill_method"], inplace=True) obv_long.fillna(method=kwargs["fill_method"], inplace=True) obv_short.fillna(method=kwargs["fill_method"], inplace=True) # Prepare DataFrame to return data = { obv_.name: obv_, f"OBV_min_{min_lookback}": obv_.rolling(min_lookback).min(), f"OBV_max_{max_lookback}": obv_.rolling(max_lookback).max(), f"OBV_{maf.name}": maf, f"OBV_{mas.name}": mas, f"AOBV_LR_{run_length}": obv_long, f"AOBV_SR_{run_length}": obv_short, } aobvdf = DataFrame(data) # Name and Categorize it aobvdf.name = ( f"AOBV_{mamode}_{fast}_{slow}_{min_lookback}_{max_lookback}_{run_length}" ) aobvdf.category = "volume" return aobvdf
def inertia(close=None, high=None, low=None, length=None, rvi_length=None, scalar=None, refined=None, thirds=None, mamode=None, drift=None, offset=None, **kwargs): """Indicator: Inertia (INERTIA)""" # Validate Arguments length = int(length) if length and length > 0 else 20 rvi_length = int(rvi_length) if rvi_length and rvi_length > 0 else 14 scalar = float(scalar) if scalar and scalar > 0 else 100 refined = False if refined is None else True thirds = False if thirds is None else True mamode = mamode if isinstance(mamode, str) else "ema" _length = max(length, rvi_length) close = verify_series(close, _length) drift = get_drift(drift) offset = get_offset(offset) if close is None: return if refined or thirds: high = verify_series(high, _length) low = verify_series(low, _length) if high is None or low is None: return # Calculate Result if refined: _mode = "r" rvi_ = rvi(close, high=high, low=low, length=rvi_length, scalar=scalar, refined=refined, mamode=mamode) elif thirds: _mode = "t" rvi_ = rvi(close, high=high, low=low, length=rvi_length, scalar=scalar, thirds=thirds, mamode=mamode) else: _mode = "" rvi_ = rvi(close, length=rvi_length, scalar=scalar, mamode=mamode) inertia = linreg(rvi_, length=length) # Offset if offset != 0: inertia = inertia.shift(offset) # Handle fills if "fillna" in kwargs: inertia.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: inertia.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category _props = f"_{length}_{rvi_length}" inertia.name = f"INERTIA{_mode}{_props}" inertia.category = "momentum" return inertia