def fisher(high, low, length=None, signal=None, offset=None, **kwargs): """Indicator: Fisher Transform (FISHT)""" # Validate Arguments length = int(length) if length and length > 0 else 9 signal = int(signal) if signal and signal > 0 else 1 _length = max(length, signal) high = verify_series(high, _length) low = verify_series(low, _length) offset = get_offset(offset) if high is None or low is None: return # Calculate Result hl2_ = hl2(high, low) highest_hl2 = hl2_.rolling(length).max() lowest_hl2 = hl2_.rolling(length).min() hlr = high_low_range(highest_hl2, lowest_hl2) hlr[hlr < 0.001] = 0.001 position = ((hl2_ - lowest_hl2) / hlr) - 0.5 v = 0 m = high.size result = [npNaN for _ in range(0, length - 1)] + [0] for i in range(length, m): v = 0.66 * position.iloc[i] + 0.67 * v if v < -0.99: v = -0.999 if v > 0.99: v = 0.999 result.append(0.5 * (nplog((1 + v) / (1 - v)) + result[i - 1])) fisher = Series(result, index=high.index) signalma = fisher.shift(signal) # Offset if offset != 0: fisher = fisher.shift(offset) signalma = signalma.shift(offset) # Handle fills if "fillna" in kwargs: fisher.fillna(kwargs["fillna"], inplace=True) signalma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: fisher.fillna(method=kwargs["fill_method"], inplace=True) signalma.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = f"_{length}_{signal}" fisher.name = f"FISHERT{_props}" signalma.name = f"FISHERTs{_props}" fisher.category = signalma.category = "momentum" # Prepare DataFrame to return data = {fisher.name: fisher, signalma.name: signalma} df = DataFrame(data) df.name = f"FISHERT{_props}" df.category = fisher.category return df
def eom( high, low, close, volume, length=None, divisor=None, drift=None, offset=None, **kwargs, ): """Indicator: Ease of Movement (EOM)""" # 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 divisor = divisor if divisor and divisor > 0 else 100000000 drift = get_drift(drift) offset = get_offset(offset) # Calculate Result high_low_range = non_zero_range(high, low) distance = hl2(high=high, low=low) - hl2(high=high.shift(drift), low=low.shift(drift)) box_ratio = volume / divisor box_ratio /= high_low_range eom = distance / box_ratio eom = sma(eom, length=length) # Offset if offset != 0: eom = eom.shift(offset) # Handle fills if "fillna" in kwargs: eom.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: eom.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it eom.name = f"EOM_{length}_{divisor}" eom.category = "volume" return eom
def fisher(high, low, length=None, signal=None, offset=None, **kwargs): """Indicator: Fisher Transform (FISHT)""" # Validate Arguments high = verify_series(high) low = verify_series(low) length = int(length) if length and length > 0 else 9 signal = int(signal) if signal and signal > 0 else 5 offset = get_offset(offset) # Calculate Result m = high.size hl2_ = hl2(high, low) max_high = hl2_.rolling(length).max() min_low = hl2_.rolling(length).min() hl2_range = max_high - min_low hl2_range[hl2_range < 1e-5] = 0.001 position = (hl2_ - min_low) / hl2_range v = 0 fish = 0 result = [npNaN for _ in range(0, length - 1)] for i in range(length - 1, m): v = 0.66 * (position[i] - 0.5) + 0.67 * v if v > 0.99: v = 0.999 if v < -0.99: v = -0.999 fish = 0.5 * (fish + nplog((1 + v) / (1 - v))) result.append(fish) fisher = Series(result, index=high.index) signalma = ema(fisher, length=signal) # Offset if offset != 0: fisher = fisher.shift(offset) signalma = signalma.shift(offset) # Handle fills if "fillna" in kwargs: fisher.fillna(kwargs["fillna"], inplace=True) signalma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: fisher.fillna(method=kwargs["fill_method"], inplace=True) signalma.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = f"_{length}_{signal}" fisher.name = f"FISHERT{_props}" signalma.name = f"FISHERTs{_props}" fisher.category = signalma.category = "momentum" # Prepare DataFrame to return data = {fisher.name: fisher, signalma.name: signalma} df = DataFrame(data) df.name = f"FISHERT{_props}" df.category = fisher.category return df
def ttm_trend(high, low, close, length=None, offset=None, **kwargs): """Indicator: TTM Trend (TTM_TRND)""" # Validate arguments length = int(length) if length and length > 0 else 6 high = verify_series(high, length) low = verify_series(low, length) close = verify_series(close, length) offset = get_offset(offset) if high is None or low is None or close is None: return # Calculate Result trend_avg = hl2(high, low) for i in range(1, length): trend_avg = trend_avg + hl2(high.shift(i), low.shift(i)) trend_avg = trend_avg / length tm_trend = (close > trend_avg).astype(int) tm_trend.replace(0, -1, inplace=True) # Offset if offset != 0: tm_trend = tm_trend.shift(offset) # Handle fills if "fillna" in kwargs: tm_trend.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: tm_trend.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it tm_trend.name = f"TTM_TRND_{length}" tm_trend.category = "momentum" # Prepare DataFrame to return data = {tm_trend.name: tm_trend} df = DataFrame(data) df.name = f"TTMTREND_{length}" df.category = tm_trend.category return df
def supertrend(high, low, close, length=None, multiplier=None, offset=None, **kwargs): """Indicator: Supertrend""" # Validate Arguments high = verify_series(high) low = verify_series(low) close = verify_series(close) length = int(length) if length and length > 0 else 7 multiplier = float(multiplier) if multiplier and multiplier > 0 else 3. offset = get_offset(offset) # Calculate Results m = close.size dir_, trend = [1] * m, [0] * m long, short = [npNaN] * m, [npNaN] * m hl2_ = hl2(high, low) matr = multiplier * atr(high, low, close, length) upperband = hl2_ + matr lowerband = hl2_ - matr for i in range(1, m): if close.iloc[i] > upperband.iloc[i - 1]: dir_[i] = 1 elif close.iloc[i] < lowerband.iloc[i - 1]: dir_[i] = -1 else: dir_[i] = dir_[i - 1] if dir_[i] > 0 and lowerband.iloc[i] < lowerband.iloc[i - 1]: lowerband.iloc[i] = lowerband.iloc[i - 1] if dir_[i] < 0 and upperband.iloc[i] > upperband.iloc[i - 1]: upperband.iloc[i] = upperband.iloc[i - 1] if dir_[i] > 0: trend[i] = long[i] = lowerband.iloc[i] else: trend[i] = short[i] = upperband.iloc[i] # Prepare DataFrame to return _props = f"_{length}_{multiplier}" df = DataFrame({ f"SUPERT{_props}": trend, f"SUPERTd{_props}": dir_, f"SUPERTl{_props}": long, f"SUPERTs{_props}": short }, index=close.index) df.name = f"SUPERT{_props}" df.category = "overlap" # Apply offset if needed if offset != 0: df = df.shift(offset) # Handle fills if "fillna" in kwargs: df.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: df.fillna(method=kwargs["fill_method"], inplace=True) return df
def pmax(high, low, close, length=None, multiplier=None, mamode=None, offset=None, **kwargs): """Indicator: PMAX""" # Validate Arguments length = int(length) if length and length > 0 else 10 mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema" multiplier = float(multiplier) if multiplier and multiplier > 0 else 3.0 high = verify_series(high, length) low = verify_series(low, length) close = verify_series(close, length) offset = get_offset(offset) if high is None or low is None or close is None: return # Calculate Results m = close.size dir_, trend = [1] * m, [0] * m long, short = [npNaN] * m, [npNaN] * m hl2_ = hl2(high, low) mavg = ma(mamode, hl2_, length=length) matr = multiplier * atr(high, low, close, length) upperband = mavg + matr lowerband = mavg - matr for i in range(1, m): if mavg.iloc[i] > upperband.iloc[i - 1]: dir_[i] = 1 elif mavg.iloc[i] < lowerband.iloc[i - 1]: dir_[i] = -1 else: dir_[i] = dir_[i - 1] if dir_[i] > 0 and lowerband.iloc[i] < lowerband.iloc[i - 1]: lowerband.iloc[i] = lowerband.iloc[i - 1] if dir_[i] < 0 and upperband.iloc[i] > upperband.iloc[i - 1]: upperband.iloc[i] = upperband.iloc[i - 1] if dir_[i] > 0: trend[i] = long[i] = lowerband.iloc[i] else: trend[i] = short[i] = upperband.iloc[i] # Prepare DataFrame to return _props = f"_{length}_{multiplier}" df = DataFrame( { f"PMAX{_props}": trend, f"PMAXd{_props}": dir_, f"PMAXSL{_props}": mavg, f"PMAXlong{_props}": long, f"PMAXshort{_props}": short }, index=close.index) df.name = f"PMAX{_props}" df.category = "overlap" # Apply offset if needed if offset != 0: df = df.shift(offset) # Handle fills if "fillna" in kwargs: df.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: df.fillna(method=kwargs["fill_method"], inplace=True) return df