# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/9d66f71ce21f2f65feaad6bb967215be22a3cfd6/HilbertSineWave.py from freqtrade.strategy import IStrategy import numpy as np import pandas as pd from math import sin, cos, atan, floor, pi ''' ______ __ __ __ __ ______ __ __ __ __ __ ______ / \ / | _/ | / | / | / \ / \ / | / | / | / | / \ /$$$$$$ |$$ |____ / $$ | _______ $$ | /$$/ /$$$$$$ |$$ \ $$ | _$$ |_ $$ | $$ | _______ /$$$$$$ | _______ $$ | $$/ $$ \ $$$$ | / |$$ |/$$/ $$ ___$$ |$$$ \$$ | / $$ | $$ |__$$ | / |$$$ \$$ | / | $$ | $$$$$$$ | $$ | /$$$$$$$/ $$ $$< / $$< $$$$ $$ | $$$$$$/ $$ $$ |/$$$$$$$/ $$$$ $$ |/$$$$$$$/ $$ | __ $$ | $$ | $$ | $$ | $$$$$ \ _$$$$$ |$$ $$ $$ | $$ | __$$$$$$$$ |$$ | $$ $$ $$ |$$ \ $$ \__/ |$$ | $$ | _$$ |_ $$ \_____ $$ |$$ \ / \__$$ |$$ |$$$$ | $$ |/ | $$ |$$ \_____ $$ \$$$$ | $$$$$$ | $$ $$/ $$ | $$ |/ $$ |$$ |$$ | $$ |$$ $$/ $$ | $$$ |______$$ $$/ $$ |$$ |$$ $$$/ / $$/ $$$$$$/ $$/ $$/ $$$$$$/ $$$$$$$/ $$/ $$/ $$$$$$/ $$/ $$// |$$$$/ $$/ $$$$$$$/ $$$$$$/ $$$$$$$/ $$$$$$/ ''' class Github_jaredrsommer_freqtradestrategies__HilbertSineWave__20260518_195114(IStrategy): # Strategy parameters timeframe = '1h' minimal_roi = {"0": 0.1} stoploss = -0.05 # Custom parameters alpha = 0.09 # Smoothing factor amplitude_factor = 2.0 # Controls the amplitude of support/resistance levels def custom_percentile_rank(self, series, window, percent=50): """Custom implementation of percentile rank similar to Pine Script's percentile_nearest_rank""" rolling = series.rolling(window=window, min_periods=1) return rolling.apply(lambda x: np.percentile(x, percent) if not np.all(np.isnan(x)) else np.nan, raw=True) def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: # Calculate Smooth price (handle initial NaN values) dataframe['smooth'] = ( dataframe['close'] + 2 * dataframe['close'].shift(1).fillna(dataframe['close']) + 2 * dataframe['close'].shift(2).fillna(dataframe['close']) + dataframe['close'].shift(3).fillna(dataframe['close']) ) / 6 # Initialize cycle dataframe['cycle'] = 0.0 for i in range(2, len(dataframe)): dataframe.loc[dataframe.index[i], 'cycle'] = ( (1 - 0.5 * self.alpha) * (1 - 0.5 * self.alpha) * (dataframe['smooth'].iloc[i] - 2 * dataframe['smooth'].iloc[i-1] + dataframe['smooth'].iloc[i-2]) + 2 * (1 - self.alpha) * dataframe['cycle'].iloc[i-1] - (1 - self.alpha) * (1 - self.alpha) * dataframe['cycle'].iloc[i-2] ) # Initialize inst_period with default value dataframe['inst_period'] = 15.0 # Initial value before calculations # Calculate Q1 and I1 dataframe['q1'] = ( 0.0962 * dataframe['cycle'] + 0.5769 * dataframe['cycle'].shift(2).fillna(0) - 0.5769 * dataframe['cycle'].shift(4).fillna(0) - 0.0962 * dataframe['cycle'].shift(6).fillna(0) ) * (0.5 + 0.08 * dataframe['inst_period'].shift(1)) dataframe['i1'] = dataframe['cycle'].shift(3).fillna(0) # Calculate DeltaPhase with NaN handling denominator = (1 + dataframe['i1']*dataframe['i1'].shift(1)/(dataframe['q1']*dataframe['q1'].shift(1))) dataframe['delta_phase'] = np.where( (dataframe['q1'] != 0) & (dataframe['q1'].shift(1) != 0) & (denominator != 0), (dataframe['i1']/dataframe['q1'] - dataframe['i1'].shift(1)/dataframe['q1'].shift(1))/denominator, np.nan ) dataframe['delta_phase'] = dataframe['delta_phase'].clip(lower=0.1, upper=1.1) # Calculate median delta using custom percentile rank dataframe['median_delta'] = self.custom_percentile_rank(dataframe['delta_phase'], window=5, percent=50) dataframe['dc'] = np.where(dataframe['median_delta'] == 0, 15, 6.28318/dataframe['median_delta'].replace(0, np.nan) + 0.5) # Update inst_period after dc is calculated for i in range(1, len(dataframe)): dataframe.loc[dataframe.index[i], 'inst_period'] = ( 0.33 * dataframe['dc'].iloc[i] + 0.67 * dataframe['inst_period'].iloc[i-1] ) # Handle NaN in dc_period calculation dataframe['dc_period'] = dataframe['inst_period'].apply(lambda x: floor(x) if not pd.isna(x) else 15) # Calculate Sine and LeadSine def calculate_sines(row): dc_period = int(row['dc_period']) real_part = 0 imag_part = 0 for count in range(dc_period): cycle_shift = dataframe['cycle'].shift(count).iloc[row.name] if pd.isna(cycle_shift): cycle_shift = 0 real_part += sin(6.28318 * count / dc_period) * cycle_shift imag_part += cos(6.28318 * count / dc_period) * cycle_shift dc_phase = atan(real_part/imag_part) if abs(imag_part) > 0.001 else (1.572963 * np.sign(real_part)) dc_phase += 1.572963 if imag_part < 0: dc_phase += 3.1415926 if dc_phase > 5.49778705: dc_phase -= 6.28318 return pd.Series({ 'sine': sin(dc_phase), 'lead_sine': sin(dc_phase + 0.78539815) }) sines = dataframe.apply(calculate_sines, axis=1) dataframe['sine'] = sines['sine'].fillna(0) dataframe['lead_sine'] = sines['lead_sine'].fillna(0) # Calculate support/resistance levels with separate columns dataframe['support_green'] = np.nan dataframe['resistance_red'] = np.nan dc_offset = lambda x: int(floor(x['dc_period']/8)) prev_support = True curr_value = 0 for i in range(len(dataframe)): offset = dc_offset(dataframe.iloc[i]) if i < offset: continue lead_sine_shift = dataframe['lead_sine'].iloc[i - offset] sine_shift = dataframe['sine'].iloc[i - offset] if pd.isna(lead_sine_shift) or pd.isna(sine_shift): continue if lead_sine_shift <= sine_shift and prev_support: curr_value = dataframe['high'].iloc[i] * (1 + 0.01 * self.amplitude_factor) dataframe.loc[dataframe.index[i], 'resistance_red'] = curr_value prev_support = False elif lead_sine_shift > sine_shift and not prev_support: curr_value = dataframe['low'].iloc[i] * (1 - 0.01 * self.amplitude_factor) dataframe.loc[dataframe.index[i], 'support_green'] = curr_value prev_support = True else: if prev_support: dataframe.loc[dataframe.index[i], 'support_green'] = curr_value else: dataframe.loc[dataframe.index[i], 'resistance_red'] = curr_value return dataframe def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: dataframe.loc[ (dataframe['close'] <= dataframe['support_green']) & (dataframe['support_green'].notna()), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: dataframe.loc[ (dataframe['close'] >= dataframe['resistance_red']) & (dataframe['resistance_red'].notna()), 'exit_long'] = 1 return dataframe