def kc(high, low, close, length=None, scalar=None, mamode=None, offset=None, **kwargs): """Indicator: Keltner Channels (KC)""" # Validate arguments high = verify_series(high) low = verify_series(low) close = verify_series(close) length = int(length) if length and length > 0 else 20 scalar = float(scalar) if scalar and scalar > 0 else 2 mamode = mamode.lower() if mamode else None offset = get_offset(offset) # Calculate Result use_tr = kwargs.pop("tr", True) if use_tr: range_ = true_range(high, low, close) else: range_ = high_low_range(high, low) _mode = "" if mamode == "sma": basis = sma(close, length) band = sma(range_, length=length) _mode += "s" elif mamode is None or mamode == "ema": basis = ema(close, length=length) band = ema(range_, length=length) lower = basis - scalar * band upper = basis + scalar * band # Offset if offset != 0: lower = lower.shift(offset) basis = basis.shift(offset) upper = upper.shift(offset) # Handle fills if "fillna" in kwargs: lower.fillna(kwargs["fillna"], inplace=True) basis.fillna(kwargs["fillna"], inplace=True) upper.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: lower.fillna(method=kwargs["fill_method"], inplace=True) basis.fillna(method=kwargs["fill_method"], inplace=True) upper.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = f"{_mode if len(_mode) else ''}_{length}_{scalar}" lower.name = f"KCL{_props}" basis.name = f"KCB{_props}" upper.name = f"KCU{_props}" basis.category = upper.category = lower.category = "volatility" # Prepare DataFrame to return data = {lower.name: lower, basis.name: basis, upper.name: upper} kcdf = DataFrame(data) kcdf.name = f"KC{_props}" kcdf.category = basis.category return kcdf
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 cdl_doji( open_, high, low, close, length=None, factor=None, scalar=None, asint=True, offset=None, **kwargs, ): """Candle Type: Doji""" # Validate Arguments open_ = verify_series(open_) high = verify_series(high) low = verify_series(low) close = verify_series(close) length = int(length) if length and length > 0 else 10 factor = float(factor) if is_percent(factor) else 10 scalar = float(scalar) if scalar else 100 offset = get_offset(offset) naive = kwargs.pop("naive", False) # Calculate Result body = real_body(open_, close).abs() hl_range = high_low_range(high, low).abs() hl_range_avg = sma(hl_range, length) doji = body < 0.01 * factor * hl_range_avg if naive: doji.iloc[:length] = body < 0.01 * factor * hl_range if asint: doji = scalar * doji.astype(int) # Offset if offset != 0: doji = doji.shift(offset) # Handle fills if "fillna" in kwargs: doji.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: doji.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it doji.name = f"CDL_DOJI_{length}_{0.01 * factor}" doji.category = "candles" return doji