# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/63cd46ea087ed8ed01f7d54d8cfcf1dbed166b04/TGMA.py import logging import numpy as np import pandas as pd from technical import qtpylib from pandas import DataFrame from datetime import datetime, timezone from typing import Optional from functools import reduce import talib.abstract as ta import pandas_ta as pta from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter, merge_informative_pair) import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade logger = logging.getLogger(__name__) class Github_jaredrsommer_freqtradestrategies__TGMA__20260509_225308(IStrategy): ''' ______ __ __ __ __ ______ __ __ __ __ __ ______ / \ / | _/ | / | / | / \ / \ / | / | / | / | / \ /$$$$$$ |$$ |____ / $$ | _______ $$ | /$$/ /$$$$$$ |$$ \ $$ | _$$ |_ $$ | $$ | _______ /$$$$$$ | _______ $$ | $$/ $$ \ $$$$ | / |$$ |/$$/ $$ ___$$ |$$$ \$$ | / $$ | $$ |__$$ | / |$$$ \$$ | / | $$ | $$$$$$$ | $$ | /$$$$$$$/ $$ $$< / $$< $$$$ $$ | $$$$$$/ $$ $$ |/$$$$$$$/ $$$$ $$ |/$$$$$$$/ $$ | __ $$ | $$ | $$ | $$ | $$$$$ \ _$$$$$ |$$ $$ $$ | $$ | __$$$$$$$$ |$$ | $$ $$ $$ |$$ \ $$ \__/ |$$ | $$ | _$$ |_ $$ \_____ $$ |$$ \ / \__$$ |$$ |$$$$ | $$ |/ | $$ |$$ \_____ $$ \$$$$ | $$$$$$ | $$ $$/ $$ | $$ |/ $$ |$$ |$$ | $$ |$$ $$/ $$ | $$$ |______$$ $$/ $$ |$$ |$$ $$$/ / $$/ $$$$$$/ $$/ $$/ $$$$$$/ $$$$$$$/ $$/ $$/ $$$$$$/ $$/ $$// |$$$$/ $$/ $$$$$$$/ $$$$$$/ $$$$$$$/ $$$$$$/ ''' ### Strategy parameters ### timeframe = '1h' exit_profit_only = False ### No selling at a loss ignore_roi_if_entry_signal = True process_only_new_candles = True can_short = False use_exit_signal = True startup_candle_count = 20 use_custom_stoploss = True trailing_stop = False locked_stoploss = {} minimal_roi = {} # Stoploss: stoploss = -0.12 # Fail Safe Default do not hyper opt Stoploss Space # Trailing stop: trailing_stop = True # value loaded from strategy trailing_stop_positive = 0.035 # value loaded from strategy trailing_stop_positive_offset = 0.013 # value loaded from strategy trailing_only_offset_is_reached = True # value loaded from strategy # indicators u_window_size = IntParameter(60, 240, default=240, space='buy', optimize=True) l_window_size = IntParameter(20, 40, default=30, space='buy', optimize=True) #limits buylimit = IntParameter(0, 15, default=10, space='buy', optimize=True) selllimit = IntParameter(15, 23, default=20, space='sell', optimize=True) # CooldownPeriod cooldown_lookback = IntParameter(0, 12, default=5, space="protection", optimize=True, load=True) # StoplossGuard use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True, load=True) stop_duration = IntParameter(6, 40, default=39, space="protection", optimize=True, load=True) stop_protection_only_per_pair = BooleanParameter(default=True, space="protection", optimize=True, load=True) stop_protection_only_per_side = BooleanParameter(default=False, space="protection", optimize=True, load=True) stop_protection_trade_limit = IntParameter(1, 10, default=4, space="protection", optimize=True, load=True) stop_protection_required_profit = DecimalParameter(-0.10, 0.01, default=-0.04, decimals=2, space="protection", optimize=True, load=True) # LowProfitPairs use_lowprofit_protection = BooleanParameter(default=False, space="protection", optimize=True, load=True) lowprofit_protection_lookback = IntParameter(1, 24, default=10, space="protection", optimize=True, load=True) lowprofit_trade_limit = IntParameter(1, 10, default=6, space="protection", optimize=True, load=True) lowprofit_stop_duration = IntParameter(1, 70, default=65, space="protection", optimize=True, load=True) lowprofit_required_profit = DecimalParameter(-0.10, 0.00, default=-0.04, decimals=2, space="protection", optimize=True, load=True) lowprofit_only_per_pair = BooleanParameter(default=True, space="protection", optimize=True, load=True) # MaxDrawdown use_maxdrawdown_protection = BooleanParameter(default=False, space="protection", optimize=True, load=True) maxdrawdown_protection_lookback = IntParameter(1, 10, default=6, space="protection", optimize=True, load=True) maxdrawdown_trade_limit = IntParameter(1, 20, default=10, space="protection", optimize=True, load=True) maxdrawdown_stop_duration = IntParameter(1, 40, default=6, space="protection", optimize=True, load=True) maxdrawdown_allowed_drawdown = DecimalParameter(-0.10, 0.00, default=-0.04, decimals=2, space="protection", optimize=True, load=True) # Custom Entry increment = DecimalParameter(low=1.0005, high=1.002, default=1.001, decimals=4 ,space='buy', optimize=True, load=True) entryX = DecimalParameter(low=0.995, high=1.01, default=1.00, decimals=3 ,space='buy', optimize=True, load=True) last_entry_price = None # Position Management base_trades = IntParameter(4, 10, default=6, space="protection", optimize=True) stepSize = IntParameter(1, 4, default=2, space="protection", optimize=True) def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) entry_price = ((dataframe['close'].iat[-1] + dataframe['open'].iat[-1] + proposed_rate + proposed_rate) / 4) * self.entryX.value if entry_price >= proposed_rate: entry_price = proposed_rate if (self.dp.runmode.value in ('live', 'dry_run')): logger.info(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}") self.dp.send_msg(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}") # Check if there is a stored last entry price and if it matches the proposed entry price if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0001: # Tolerance for floating-point comparison entry_price *= self.increment.value # Increment by 0.2%% if (self.dp.runmode.value in ('live', 'dry_run')): logger.info(f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}.") # Update the last entry price self.last_entry_price = entry_price # Define balance thresholds and stake multipliers # EDIT These to match your funding level <--- !!!!! # balance = self.wallets.get_total_stake_amount() # max_balance = 40000 # if balance > 10000: # max_open_trades_at_max_balance = self.base_trades.value + int((max_balance - 10000) / 10000) * self.stepSize.value # self.config['max_open_trades'] = min(self.base_trades.value + int((balance - 10000) / 10000) * self.stepSize.value, max_open_trades_at_max_balance) # else: # self.config['max_open_trades'] = self.base_trades.value return entry_price ### protections ### @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": 24 * 3, "trade_limit": self.stop_protection_trade_limit.value, "stop_duration_candles": self.stop_duration.value, "only_per_pair": self.stop_protection_only_per_pair.value, "required_profit": self.stop_protection_required_profit.value, "only_per_side": self.stop_protection_only_per_side.value }) if self.use_lowprofit_protection.value: prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.lowprofit_protection_lookback.value, "trade_limit": self.lowprofit_trade_limit.value, "stop_duration_candles": self.lowprofit_stop_duration.value, "required_profit": self.lowprofit_required_profit.value, "only_per_pair": self.lowprofit_only_per_pair.value }) if self.use_maxdrawdown_protection.value: prot.append({ "method": "MaxDrawdown", "lookback_period_candles": self.maxdrawdown_protection_lookback.value, "trade_limit": self.maxdrawdown_trade_limit.value, "stop_duration_candles": self.maxdrawdown_stop_duration.value, "max_allowed_drawdown": self.maxdrawdown_allowed_drawdown.value }) return prot # Plot configuration for the UI plot_config = { "main_plot": { "ma": {"color": "blue"}, "gradient": {"color": "orange", "type": "scatter"}, }, } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Initialize Hurst Cycles for startup errors cycle_period = 80 harmonics = [0, 0, 0] harmonics[0] = 40 harmonics[1] = 27 harmonics[2] = 20 if len(dataframe) < self.u_window_size.value: raise ValueError(f"Insufficient data points for FFT: {len(dataframe)}. Need at least {self.u_window_size.value} data points.") # Perform FFT to identify cycles with a rolling window freq, power = perform_fft(dataframe['ha_close'], window_size=self.u_window_size.value) if len(freq) == 0 or len(power) == 0: raise ValueError("FFT resulted in zero or invalid frequencies. Check the data or the FFT implementation.") # Filter out the zero-frequency component and limit the frequency to below 500 positive_mask = (1 / freq > self.l_window_size.value) & (1 / freq < self.u_window_size.value) positive_freqs = freq[positive_mask] positive_power = power[positive_mask] # Check if there are valid frequencies after filtering if len(positive_power) == 0: raise ValueError("No positive frequencies meet the filtering criteria. Adjust window sizes or check the data.") # Convert frequencies to periods cycle_periods = 1 / positive_freqs print(cycle_periods) # Set a threshold to filter out insignificant cycles based on power power_threshold = 0 if len(positive_power) == 0 else 0.01 * np.max(positive_power) significant_indices = positive_power > power_threshold significant_periods = cycle_periods[significant_indices] significant_power = positive_power[significant_indices] # Identify the dominant cycle dominant_freq_index = np.argmax(significant_power) dominant_freq = positive_freqs[dominant_freq_index] # logger.info(f'{pair} Hurst Exponent: {dominant_freq}') cycle_period = int(np.abs(1 / dominant_freq)) if dominant_freq != 0 else 100 if cycle_period == np.inf: raise ValueError("No dominant frequency found. Check the data or the method used.") # Calculate harmonics for the dominant cycle harmonics = [cycle_period / (i + 1) for i in range(1, 4)] # print(cycle_period, harmonics) self.cp = int(cycle_period) self.h0 = int(harmonics[0]) self.h1 = int(harmonics[1]) self.h2 = int(harmonics[2]) dataframe['Cycle'] = dataframe['ha_close'].ewm(span=int(cycle_period)).mean() dataframe['H0'] = dataframe['ha_close'].ewm(span=int(harmonics[0])).mean() dataframe['H1'] = dataframe['ha_close'].ewm(span=int(harmonics[1])).mean() dataframe['H2'] = dataframe['ha_close'].ewm(span=int(harmonics[2])).mean() # Apply rolling window operation to the 'OHLC4' column rolling_windowc = dataframe['ha_close'].rolling(cycle_period) rolling_windowh0 = dataframe['ha_close'].rolling(int(harmonics[0])) rolling_windowh1 = dataframe['ha_close'].rolling(int(harmonics[1])) rolling_windowh2 = dataframe['ha_close'].rolling(int(harmonics[2])) # Calculate the peak-to-peak value on the resulting rolling window data ptp_valuec = rolling_windowc.apply(lambda x: np.ptp(x)) ptp_valueh0 = rolling_windowh0.apply(lambda x: np.ptp(x)) ptp_valueh1 = rolling_windowh1.apply(lambda x: np.ptp(x)) ptp_valueh2 = rolling_windowh2.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['cycle_move'] = ptp_valuec / dataframe['ha_close'] dataframe['h0_move'] = ptp_valueh0 / dataframe['ha_close'] dataframe['h1_move'] = ptp_valueh1 / dataframe['ha_close'] dataframe['h2_move'] = ptp_valueh2 / dataframe['ha_close'] dataframe['cycle_move_mean'] = dataframe['cycle_move'].rolling(self.cp).mean() dataframe['h0_move_mean'] = dataframe['h0_move'].rolling(self.cp).mean() dataframe['h1_move_mean'] = dataframe['h1_move'].rolling(self.cp).mean() dataframe['h2_move_mean'] = dataframe['h2_move'].rolling(self.cp).mean() # Add EMA3 for each cycle period dataframe['ema3Cycle'] = ta.EMA(dataframe['Cycle'], timeperiod=3) dataframe['ema3H0'] = ta.EMA(dataframe['H0'], timeperiod=3) dataframe['ema3H1'] = ta.EMA(dataframe['H1'], timeperiod=3) dataframe['ema3H2'] = ta.EMA(dataframe['H2'], timeperiod=3) dataframe['selllimit'] = self.selllimit.value # Call Github_jaredrsommer_freqtradestrategies__TGMA__20260509_225308 as a class method dataframe = self.calculate_tgma(dataframe, "H2", self.h2) dataframe = self.calculate_tgma(dataframe, "H1", self.h1) dataframe = self.calculate_tgma(dataframe, "H0", self.h0) dataframe = self.calculate_tgma(dataframe, "Cycle", self.cp) dataframe['Composite'] = (dataframe['gradientH2'] + dataframe['gradientH1'] + dataframe['gradientH0'] + dataframe['gradientCycle'])/4 dataframe['Comp_signal'] = ta.SMA(dataframe['Composite'], 4) dataframe['minh2'], dataframe['maxh2'] = calculate_minima_maxima(dataframe, self.h2) dataframe['minh1'], dataframe['maxh1'] = calculate_minima_maxima(dataframe, self.h1) dataframe['minh0'], dataframe['maxh0'] = calculate_minima_maxima(dataframe, self.h0) dataframe['mincp'], dataframe['maxcp'] = calculate_minima_maxima(dataframe, self.cp) return dataframe def calculate_tgma(self, dataframe: DataFrame, avg_str: str, length: int) -> DataFrame: # Parameters ma_length = int(length/3) steps = length # Max gradient steps # Initialize Gradient Strength qty_adv_dec = np.zeros(len(dataframe)) # Calculate Gradient for i in range(1, len(dataframe)): if np.isnan(dataframe[f"{avg_str}"].iloc[i]) or np.isnan(dataframe[f"ema3{avg_str}"].iloc[i]): qty_adv_dec[i] = qty_adv_dec[i - 1] continue chg = dataframe[f"{avg_str}"].iloc[i] - dataframe[f"{avg_str}"].iloc[i - 1] is_bull = dataframe[f"{avg_str}"].iloc[i] > dataframe[f"ema3{avg_str}"].iloc[i] is_bear = dataframe[f"{avg_str}"].iloc[i] < dataframe[f"ema3{avg_str}"].iloc[i] if is_bull: qty_adv_dec[i] = qty_adv_dec[i - 1] + 1 if chg > 0 else qty_adv_dec[i - 1] - 1 elif is_bear: qty_adv_dec[i] = qty_adv_dec[i - 1] - 1 if chg < 0 else qty_adv_dec[i - 1] + 1 qty_adv_dec[i] = max(1, min(steps, qty_adv_dec[i])) # Normalize the gradient values gradient_normalized = qty_adv_dec dataframe[f"gradient{avg_str}"] = gradient_normalized dataframe[f'buylimit{avg_str}'] = dataframe[f"gradient{avg_str}"].shift(2) # Calculate gradient impulse dataframe['gradientImpulse'] = (abs(dataframe[f'buylimit{avg_str}'].shift() - dataframe[f"gradient{avg_str}"].shift()) - abs(dataframe[f'buylimit{avg_str}'].shift(2) - dataframe[f"gradient{avg_str}"].shift(2))) # Assign color mapping dataframe[f"color{avg_str}"] = np.where( dataframe[f"{avg_str}"] > dataframe[f"ema3{avg_str}"], gradient_normalized, -gradient_normalized, ) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Define the buy conditions. """ # steps = self.h2 # Max Gradient Steps dataframe.loc[ # (dataframe["ma"] > dataframe["close"]) & # (dataframe["gradient"].shift() > dataframe["ema3"]) & # Bullish MA crossing above EMA (dataframe["gradientH2"] < dataframe['selllimit']) & (qtpylib.crossed_above(dataframe["gradientH2"], dataframe['buylimitH2'])), #& # (dataframe["gradient"] > (steps/2)), # Strong gradient "buy" ] = 1 # df.loc[ # ( # (df['ha_trend_cp'] < self.suppress_cp.value) & # (df['ha_trend_cp'] > df['ha_trend_lower_cp']) & # (df['ha_trend_cp'].shift() < df['ha_trend_lower_cp'].shift()) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'ha trend cp') return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Define the sell conditions. """ steps = self.h2 # Max Gradient Steps dataframe.loc[ # (dataframe["gradientImpulse"] > 1) & # Bearish MA crossing below EMA (qtpylib.crossed_below(dataframe["gradientH2"], dataframe['buylimitH2'])),# & # (dataframe["gradient"] < (steps/2)), # Weak gradient "sell" ] = 1 return dataframe def perform_fft(price_data, window_size=None): if window_size is not None: # Apply rolling window to smooth the data price_data = price_data.rolling(window=window_size, center=True).mean().dropna() normalized_data = (price_data - np.mean(price_data)) / np.std(price_data) n = len(normalized_data) fft_data = np.fft.fft(normalized_data) freq = np.fft.fftfreq(n) power = np.abs(fft_data) ** 2 power[np.isinf(power)] = 0 return freq, power def calculate_minima_maxima(df, window): if df is None or df.empty: return np.zeros(0), np.zeros(0) # Return empty arrays instead of None minima = np.zeros(len(df)) maxima = np.zeros(len(df)) for i in range(window, len(df)): # Ensure index does not go out of bounds window_data = df['ha_close'].iloc[i - window:i + 1] if df['ha_close'].iloc[i] == window_data.min() and (window_data == df['ha_close'].iloc[i]).sum() == 1: minima[i] = -window if df['ha_close'].iloc[i] == window_data.max() and (window_data == df['ha_close'].iloc[i]).sum() == 1: maxima[i] = window return minima, maxima