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 talib 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 import warnings from pandas.errors import PerformanceWarning # Suppress PerformanceWarning warnings.simplefilter(action="ignore", category=PerformanceWarning) logger = logging.getLogger(__name__) class kalthetank(IStrategy): ''' ______ __ __ __ __ ______ __ __ __ __ __ ______ / \ / | _/ | / | / | / \ / \ / | / | / | / | / \ /$$$$$$ |$$ |____ / $$ | _______ $$ | /$$/ /$$$$$$ |$$ \ $$ | _$$ |_ $$ | $$ | _______ /$$$$$$ | _______ $$ | $$/ $$ \ $$$$ | / |$$ |/$$/ $$ ___$$ |$$$ \$$ | / $$ | $$ |__$$ | / |$$$ \$$ | / | $$ | $$$$$$$ | $$ | /$$$$$$$/ $$ $$< / $$< $$$$ $$ | $$$$$$/ $$ $$ |/$$$$$$$/ $$$$ $$ |/$$$$$$$/ $$ | __ $$ | $$ | $$ | $$ | $$$$$ \ _$$$$$ |$$ $$ $$ | $$ | __$$$$$$$$ |$$ | $$ $$ $$ |$$ \ $$ \__/ |$$ | $$ | _$$ |_ $$ \_____ $$ |$$ \ / \__$$ |$$ |$$$$ | $$ |/ | $$ |$$ \_____ $$ \$$$$ | $$$$$$ | $$ $$/ $$ | $$ |/ $$ |$$ |$$ | $$ |$$ $$/ $$ | $$$ |______$$ $$/ $$ |$$ |$$ $$$/ / $$/ $$$$$$/ $$/ $$/ $$$$$$/ $$$$$$$/ $$/ $$/ $$$$$$/ $$/ $$// |$$$$/ $$/ $$$$$$$/ $$$$$$/ $$$$$$$/ $$$$$$/ ''' ### Strategy parameters ### timeframe = '1h' exit_profit_only = True### 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 = 200 use_custom_stoploss = True trailing_stop = False locked_stoploss = {} minimal_roi = {} # Stoploss: stoploss = -0.20 # 15Fail Safe Default do not hyper opt Stoploss Space # Trailing stop: trailing_stop = False # value loaded from strategy trailing_stop_positive = None # value loaded from strategy trailing_stop_positive_offset = 0.0 # value loaded from strategy trailing_only_offset_is_reached = False # value loaded from strategy ### Hyperoptable parameters ### # DCA position_adjustment_enable = True max_epa = IntParameter(0, 1, default = 1 ,space='buy', optimize=True, load=True) # of additional buys. max_dca_multiplier = DecimalParameter(low=1.0, high=5.0, default=3, decimals=1 ,space='buy', optimize=True, load=True) use_static = BooleanParameter(default=True, space="buy", optimize=True, load=True) filldelay = IntParameter(100, 300, default = 122 ,space='buy', optimize=True, load=True) max_entry_position_adjustment = max_epa.value # indicators u_window_size = IntParameter(120, 300, default=282, space='buy', optimize=True) l_window_size = IntParameter(20, 70, default=42, space='buy', optimize=True) Q = DecimalParameter(low=0.0001, high=0.5, default=0.001, decimals=4 ,space='buy', optimize=True, load=True) R = DecimalParameter(low=0.001, high=0.5, default=0.1, decimals=3 ,space='buy', 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=1.00, default=1.00, decimals=3 ,space='buy', optimize=True, load=True) last_entry_price = None # protections cooldown_lookback = IntParameter(4, 12, default=12, space="protection", optimize=True) stop_duration = IntParameter(4, 12, default=5, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) # Risk Level Stake size adjustments based on Location to stake0 = DecimalParameter(low=0.9, high=1.1, default=1.0, decimals=1 ,space='buy', optimize=True, load=True) stake1 = DecimalParameter(low=0.9, high=1.1, default=1.0, decimals=1 ,space='buy', optimize=True, load=True) stake2 = DecimalParameter(low=0.9, high=1.1, default=1.0, decimals=1 ,space='buy', optimize=True, load=True) stake3 = DecimalParameter(low=0.9, high=1.1, default=1.0, decimals=1 ,space='buy', optimize=True, load=True) # negative stoploss use_stop1 = BooleanParameter(default=False, space="protection", optimize=True, load=True) use_stop2 = BooleanParameter(default=False, space="protection", optimize=True, load=True) use_stop3 = BooleanParameter(default=False, space="protection", optimize=True, load=True) use_stop4 = BooleanParameter(default=False, space="protection", optimize=True, load=True) # roi # negative stoploss use_roi1 = BooleanParameter(default=False, space="protection", optimize=True, load=True) use_roi2 = BooleanParameter(default=False, space="protection", optimize=True, load=True) use_roi3 = BooleanParameter(default=False, space="protection", optimize=True, load=True) use_roi4 = BooleanParameter(default=False, space="protection", optimize=True, load=True) time0 = IntParameter(low=1440, high=2400, default=1638, space='protection', optimize=True, load=True) time1 = IntParameter(low=2400, high=5000, default=2901, space='protection', optimize=True, load=True) time2 = IntParameter(low=5000, high=10000, default=4500, space='protection', optimize=True, load=True) time3 = IntParameter(low=5000, high=10000, default=6981, space='protection', optimize=True, load=True) # block specific exit timer time4 = IntParameter(low=240, high=480, default=343, space='sell', optimize=True, load=True) # trading buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) buy_cl1 = DecimalParameter(low=0.15, high=0.4, default=0.37, decimals=2, space='buy', optimize=True, load=True) buy_cl2 = DecimalParameter(low=0.15, high=0.4, default=0.16, decimals=2, space='buy', optimize=True, load=True) buy_cl3 = DecimalParameter(low=0.15, high=0.4, default=0.16, decimals=2, space='buy', optimize=True, load=True) buy_cl4 = DecimalParameter(low=0.15, high=0.4, default=0.15, decimals=2, space='buy', optimize=True, load=True) buy_cls = DecimalParameter(low=0.15, high=0.4, default=0.25, decimals=2, space='buy', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=55, space='sell', optimize=True, load=True) sell_cl1 = DecimalParameter(low=0.75, high=0.95, default=0.9, decimals=2, space='sell', optimize=True, load=True) sell_cl2 = DecimalParameter(low=0.75, high=0.95, default=0.77, decimals=2, space='sell', optimize=True, load=True) sell_cl3 = DecimalParameter(low=0.75, high=0.95, default=0.9, decimals=2, space='sell', optimize=True, load=True) sell_cl4 = DecimalParameter(low=0.75, high=0.95, default=0.77, decimals=2, space='sell', optimize=True, load=True) sell_cls = DecimalParameter(low=0.75, high=0.95, default=0.93, decimals=2, space='sell', optimize=True, load=True) # Entry Exit Logic Selection use0 = BooleanParameter(default=False, space="buy", optimize=True, load=True) use1 = BooleanParameter(default=False, space="buy", optimize=True, load=True) use2 = BooleanParameter(default=True, space="buy", optimize=True, load=True) use3 = BooleanParameter(default=True, space="buy", optimize=True, load=True) use4 = BooleanParameter(default=True, space="buy", optimize=True, load=True) use5 = BooleanParameter(default=False, space="buy", optimize=True, load=True) use6 = BooleanParameter(default=False, space="buy", optimize=True, load=True) use7 = BooleanParameter(default=True, space="buy", optimize=True, load=True) use8 = BooleanParameter(default=True, space="buy", optimize=True, load=True) use9 = BooleanParameter(default=True, space="buy", optimize=True, load=True) use10 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use11 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use12 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use13 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use14 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use15 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use16 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use17 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use18 = BooleanParameter(default=True, space="sell", optimize=True, load=True) use19 = BooleanParameter(default=True, space="sell", optimize=True, load=True) ### 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": 1, "stop_duration_candles": self.stop_duration.value, "only_per_pair": False }) return prot ### Dollar Cost Averaging ### ### Custom Functions ### # This is called when placing the initial order (opening trade) # Let unlimited stakes leave funds open for DCA orders def custom_stake_amount( self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs ) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() # EP0 = current_candle['lower_envelope'] # EP1 = current_candle['lower_envelope_h0'] # EP2 = current_candle['lower_envelope_h1'] # EP3 = current_candle['lower_envelope_h2'] # Define balance thresholds and stake multipliers # EDIT These to match your funding level <--- !!!!! # balance = self.wallets.get_total_stake_amount() # if balance < 3000: # balance_multiplier = 0.9 # self.config['max_open_trades'] = 5 # elif balance < 7500: # balance_multiplier = 0.95 # Increase stake by 20% # self.config['max_open_trades'] = 6 # elif balance < 10000: # balance_multiplier = 1.0 # No increase # self.config['max_open_trades'] = 5 # elif balance < 15000: # balance_multiplier = 1.05 # Increase stake by 20% # self.config['max_open_trades'] = 8 # elif balance < 20000: # balance_multiplier = 1.1 # Increase stake by 50% # self.config['max_open_trades'] = 10 # else: # balance_multiplier = 1.0 # Double stake for larger balances # Static or calculated staking if self.use_static.value == 'True': return (balance / (self.config["max_open_trades"] * self.max_dca_multiplier.value)) #* balance_multiplier # num_tr = self.config['max_open_trades'] # # Adjust stake based on DCA, balance multiplier, and Location # if current_rate < EP0: # Longest envelope typically the least frequent to be crossed # calculated_stake = (proposed_stake / self.max_dca_multiplier.value) * balance_multiplier # elif EP0 <= current_rate < EP1: # calculated_stake = (proposed_stake / self.max_dca_multiplier.value) * self.stake3.value * balance_multiplier # elif EP1 <= current_rate < EP2: # calculated_stake = (proposed_stake / self.max_dca_multiplier.value) * self.stake2.value * balance_multiplier # elif EP2 <= current_rate < EP3: # Shortest envelope typically the most frequent to be crossed # calculated_stake = (proposed_stake / self.max_dca_multiplier.value) * self.stake1.value * balance_multiplier # else: calculated_stake = (proposed_stake / self.max_dca_multiplier.value) * self.stake0.value # * balance_multiplier # logger.info(f'{pair} using {calculated_stake} instead of {proposed_stake} | Trade Slots: {num_tr} Balance X:{balance_multiplier}') # self.dp.send_msg(f'{pair} using {calculated_stake} instead of {proposed_stake} | Trade Slots: {num_tr} Balance X:{balance_multiplier}') return min(max(calculated_stake, min_stake or 0), max_stake) def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = trade.nr_of_successful_entries trade_duration = (current_time - trade.open_date_utc).seconds / 60 last_fill = (current_time - trade.date_last_filled_utc).seconds / 60 current_candle = dataframe.iloc[-1].squeeze() TP0 = current_candle['h2_move_mean'] TP1 = current_candle['h1_move_mean'] TP2 = current_candle['h0_move_mean'] TP3 = current_candle['cycle_move_mean'] display_profit = current_profit * 100 tp0 = TP0 * 100 tp1 = TP1 * 100 tp2 = TP2 * 100 tp3 = TP3 * 100 dca_str = '' dcaDisp = display_profit+tp1 if display_profit < 0: dca_str = f'| DCA Dist: {dcaDisp:.3}% | Last Fill: {last_fill:.5}m"' if current_candle['enter_long'] is not None: signal = current_candle['enter_long'] if (self.dp.runmode.value in ('live', 'dry_run')): if current_profit is not None: logger.info(f"{trade.pair} - Current Profit: {display_profit:.3}% # of Entries: {trade.nr_of_successful_entries} {dca_str}") logger.info(f"{trade.pair} - TP0: {tp0:.3}% | TP1: {tp1:.3}% | TP2: {tp2:.3}% | TP3: {tp3:.3}%") # Take Profit if m00n if current_profit > TP2 and trade.nr_of_successful_exits == 0: # Take quarter of the profit at next fib%% return -(trade.stake_amount / 2) if current_profit > TP3 and trade.nr_of_successful_exits == 1: # Take half of the profit at last fib%% return -(trade.stake_amount / 2) if current_profit > (TP3 * 1.5) and trade.nr_of_successful_exits == 2: # Take half of the profit at last fib%% return -(trade.stake_amount / 2) if current_profit > (TP3 * 2.0) and trade.nr_of_successful_exits == 3: # Take profit at last fib%% return -(trade.stake_amount) # Take Profit Early if DCA was used if current_profit > TP0 and trade.nr_of_successful_exits == 0 and count_of_entries == 2: # Take half of the profit at next fib%% return -(trade.stake_amount / 2) if current_profit > TP1 and trade.nr_of_successful_exits == 1 and count_of_entries == 2: # Take profit at last fib%% return -(trade.stake_amount) if trade.nr_of_successful_entries == self.max_epa.value + 1: return None # Block concurrent buys and Hold otherwise if the dip is not large enough if current_profit > -TP1: return None try: # This returns first order stake size # Modify the following parameters to enable more levels or different buy size: # max_entry_position_adjustment = 3 # max_dca_multiplier = 3.5 stake_amount = filled_entries[0].cost # This then calculates current safety order size when a secondary buy signal is generated. if (last_fill > self.filldelay.value): if (signal == 1 and current_profit < -TP1): if count_of_entries >= 1: stake_amount = stake_amount * 2 else: stake_amount = stake_amount return stake_amount # This then calculates current safety order size when below -Take Profit 2. if (last_fill > self.filldelay.value): if (current_profit < -TP2) and (current_profit > -TP3): if count_of_entries >= 1: stake_amount = stake_amount * 2.5 else: stake_amount = stake_amount return stake_amount # This accommadates a one shot at buying the dip on a big wick with one # large buy if the funds are available... if (last_fill > self.filldelay.value): if (current_profit < -TP3): if count_of_entries == 1: stake_amount = stake_amount * 4 else: stake_amount = stake_amount return stake_amount except Exception as exception: return None return None ### Trailing Stop ### def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() SLT0 = current_candle['h2_move_mean'] SLT1 = current_candle['h1_move_mean'] SLT2 = current_candle['h0_move_mean'] SLT3 = current_candle['cycle_move_mean'] # sortBop = current_candle['sort_bop'] display_profit = current_profit * 100 if current_profit < -0.01: if pair in self.locked_stoploss: del self.locked_stoploss[pair] if (self.dp.runmode.value in ('live', 'dry_run')): self.dp.send_msg(f'*** {pair} *** Stoploss reset.') logger.info(f'*** {pair} *** Stoploss reset.') return self.stoploss new_stoploss = None if SLT3 is not None and current_profit > SLT3: new_stoploss = (SLT2 - SLT1) level = 4 elif SLT2 is not None and current_profit > SLT2: new_stoploss = (SLT2 - SLT1) level = 3 # # in the future toggle these on certain conditions with indicators. # elif SLT1 is not None and current_profit > SLT1 and sortBop < -0.2: # new_stoploss = (SLT1 - SLT0) # level = 2 # elif SLT0 is not None and current_profit > SLT0 and sortBop < -0.3: # new_stoploss = (SLT1 - SLT0) # level = 1 if new_stoploss is not None: if pair not in self.locked_stoploss or new_stoploss > self.locked_stoploss[pair]: self.locked_stoploss[pair] = new_stoploss if (self.dp.runmode.value in ('live', 'dry_run')): self.dp.send_msg(f'*** {pair} *** Profit {level} {display_profit:.3f}%% - New stoploss: {new_stoploss:.4f} activated') logger.info(f'*** {pair} *** Profit {level} {display_profit:.3f}%% - New stoploss: {new_stoploss:.4f} activated') return self.locked_stoploss[pair] return self.stoploss 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] + dataframe['low'].iat[-1] + proposed_rate) / 4) * self.entryX.value 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 return entry_price # Custom_Exits def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = trade.nr_of_successful_entries trade_duration = (current_time - trade.open_date_utc).seconds / 60 last_fill = (current_time - trade.date_last_filled_utc).seconds / 60 current_candle = dataframe.iloc[-1].squeeze() TP0 = current_candle['h2_move_mean'] TP1 = current_candle['h1_move_mean'] TP2 = current_candle['h0_move_mean'] TP3 = current_candle['cycle_move_mean'] ### roi ### if current_profit > TP3 and trade_duration > self.time0.value and self.use_roi1.value == True: return 'Roi 0 - Easy $$$' if current_profit > TP2 and trade_duration > self.time1.value and self.use_roi2.value == True: return 'Roi 1 - ol reliable' if current_profit > TP1 and trade_duration > self.time2.value and self.use_roi3.value == True: return 'Roi 2 - Avg Joe' if current_profit > TP0 and trade_duration > self.time3.value and self.use_roi4.value == True: return 'Roi 3 - Better than Nothing' ### negative stoploss ### if current_profit < -TP3 and self.use_stop1.value == True: return 'Failsafe 3 - REKTd' if current_profit < -TP2 and self.use_stop2.value == True: return 'Failsafe 2 - Ooo that hurts' if current_profit < -TP1 and self.use_stop3.value == True: return 'Failsafe 1 - Leverage is risky' if current_profit < -TP0 and self.use_stop4.value == True: return 'Failsafe 0 - Wasnt a good idea...' return False def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade_duration = (current_time - trade.open_date_utc).seconds / 60 if exit_reason == 'roi' and trade.calc_profit_ratio(rate) < 0.003: logger.info(f"{trade.pair} ROI is below 0%") # self.dp.send_msg(f'{trade.pair} ROI is below 0') return False if exit_reason == 'partial_exit' and trade.calc_profit_ratio(rate) < 0.005: logger.info(f"{trade.pair} partial exit is below 0%") # self.dp.send_msg(f'{trade.pair} partial exit is below 0') return False if exit_reason == 'trailing_stop_loss' and trade.calc_profit_ratio(rate) < 0.005: logger.info(f"{trade.pair} trailing stop price is below 0%") # self.dp.send_msg(f'{trade.pair} trailing stop price is below 0') return False return True ### NORMAL INDICATORS ### def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair = metadata['pair'] heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] dataframe['kalman'] = kalman_filter(dataframe['ha_close'], self.Q.value, self.R.value) # 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['kalman'], 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 # 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['dc_EWM'] = dataframe['kalman'].ewm(span=int(cycle_period)).mean() dataframe['dc_1/2'] = dataframe['kalman'].ewm(span=int(harmonics[0])).mean() dataframe['dc_1/3'] = dataframe['kalman'].ewm(span=int(harmonics[1])).mean() dataframe['dc_1/4'] = dataframe['kalman'].ewm(span=int(harmonics[2])).mean() # Apply rolling window operation to the 'OHLC4' column rolling_windowc = dataframe['kalman'].rolling(cycle_period) rolling_windowh0 = dataframe['kalman'].rolling(int(harmonics[0])) rolling_windowh1 = dataframe['kalman'].rolling(int(harmonics[1])) rolling_windowh2 = dataframe['kalman'].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['kalman'] dataframe['h0_move'] = ptp_valueh0 / dataframe['kalman'] dataframe['h1_move'] = ptp_valueh1 / dataframe['kalman'] dataframe['h2_move'] = ptp_valueh2 / dataframe['kalman'] 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() # RSI dataframe['rsi'] = ta.RSI(dataframe) dataframe['rsi_ma'] = ta.SMA(dataframe['rsi'], timeperiod=10) # WaveTrend using OHLC4 or HA close - 3/21 ap = (0.25 * (dataframe['high'] + dataframe['low'] + dataframe["close"] + dataframe["open"])) dataframe['esa'] = ta.EMA(ap, timeperiod = 10) dataframe['d'] = ta.EMA(abs(ap - dataframe['esa']), timeperiod = 10) dataframe['wave_ci'] = (ap-dataframe['esa']) / (0.015 * dataframe['d']) dataframe['wave_t1'] = ta.EMA(dataframe['wave_ci'], timeperiod = 21) dataframe['wave_t2'] = ta.SMA(dataframe['wave_t1'], timeperiod = 4) dataframe['max_high_h2'] = dataframe['kalman'].rolling(self.h2).max() dataframe['min_low_h2'] = dataframe['kalman'].rolling(self.h2).min() dataframe['closePos_h2'] = (dataframe['kalman'] - dataframe['min_low_h2']) / (dataframe['max_high_h2'] - dataframe['min_low_h2']) dataframe['closePos_h2_cod'] = detect_change_of_direction(dataframe['closePos_h2']) dataframe['max_high_h1'] = dataframe['kalman'].rolling(self.h1).max() dataframe['min_low_h1'] = dataframe['kalman'].rolling(self.h1).min() dataframe['closePos_h1'] = (dataframe['kalman'] - dataframe['min_low_h1']) / (dataframe['max_high_h1'] - dataframe['min_low_h1']) dataframe['closePos_h1_cod'] = detect_change_of_direction(dataframe['closePos_h1']) dataframe['max_high_h0'] = dataframe['kalman'].rolling(self.h0).max() dataframe['min_low_h0'] = dataframe['kalman'].rolling(self.h0).min() dataframe['closePos_h0'] = (dataframe['kalman'] - dataframe['min_low_h0']) / (dataframe['max_high_h0'] - dataframe['min_low_h0']) dataframe['closePos_h0_cod'] = detect_change_of_direction(dataframe['closePos_h0']) dataframe['max_high_cp'] = dataframe['kalman'].rolling(self.cp).max() dataframe['min_low_cp'] = dataframe['kalman'].rolling(self.cp).min() dataframe['closePos_cp'] = (dataframe['kalman'] - dataframe['min_low_cp']) / (dataframe['max_high_cp'] - dataframe['min_low_cp']) dataframe['closePos_cp_cod'] = detect_change_of_direction(dataframe['closePos_cp']) dataframe['closePos_cod_sum'] = dataframe['closePos_h2_cod'] + dataframe['closePos_h1_cod'] + dataframe['closePos_h0_cod'] + dataframe['closePos_cp_cod'] dataframe['closePosSum'] = (dataframe['closePos_cp'] + dataframe['closePos_h0'] + dataframe['closePos_h1'] + dataframe['closePos_h2']) / 4 dataframe['closePosSumSmoo'] = ta.SMA(dataframe['closePosSum'], self.h2) dataframe['closePosSumSmooDir'] = np.where(dataframe['closePosSumSmoo'] > dataframe['closePosSumSmoo'].shift(), self.sell_cls.value, self.buy_cls.value) dataframe['closePosDiff'] = abs(dataframe['closePosSum'] - dataframe['closePos_h2']) dataframe['closePosDiffMeanHalf'] = (dataframe['closePosDiff'].rolling(self.cp).mean()) / 2 dataframe['closePosDiffMean'] = (dataframe['closePosDiff'].rolling(self.cp).mean()) dataframe['closePosDiffMeanFast'] = (dataframe['closePosDiff'].rolling(self.h2).mean()) dataframe['closePosMarket'] = np.where(dataframe['closePosDiff'] > 0, 0.1, -0.1) dataframe['closePosV'] = np.where(dataframe['closePosSum'] > dataframe['closePosSumSmoo'] , 0.15, -0.15) dataframe['candle_size'] = abs((dataframe['high'] - dataframe['low']) / dataframe['low']) dataframe['candle_size_sum'] = dataframe['candle_size'].rolling(self.cp).sum() dataframe['candle_size_lower'] = dataframe['candle_size'].rolling(self.cp).mean() dataframe['candle_size_upper'] = dataframe['candle_size_lower'] * 2 dataframe['BOP'] = (ta.SMA(dataframe['close'], self.cp) - ta.SMA(dataframe['open'], self.cp)) / (ta.SMA(dataframe['high'], self.cp) - ta.SMA(dataframe['low'], self.cp)) dataframe['BOP_SMA'] = ta.SMA(dataframe['BOP'], self.h0) dataframe['BOP_SMA_2'] = ta.SMA(dataframe['BOP'], self.h2) dataframe['zero'] = 0 h = self.h2 r = 8.0 x_0 = self.cp smoothColors = False lag = 0 nadaraya_watson(dataframe, h, r, x_0, smoothColors, lag, mult = 2.5) dataframe['nw_width'] = dataframe['nw_up'] - dataframe['nw_down'] dataframe['nw_width_mean'] = dataframe['nw_width'].rolling(self.h2).mean() dataframe['signal_UP'] = np.where(dataframe['nw_width'] > 0, dataframe['nw_width'], 0) dataframe['signal_DN'] = np.where(dataframe['nw_width'] < 0, dataframe['nw_width'], 0) dataframe['signal_UP'] = dataframe['signal_UP'].ffill() dataframe['signal_DN'] = dataframe['signal_DN'].ffill() dataframe['nw_width_up'] = dataframe['signal_UP'].rolling(self.h2).mean() dataframe['nw_width_dn'] = dataframe['signal_DN'].rolling(self.h2).mean() dataframe['channellowermid'] = dataframe['yhat1'] * (1 - (dataframe['h2_move_mean']/2)) dataframe['channeluppermid'] = dataframe['yhat1'] * (1 + (dataframe['h2_move_mean']/2)) dataframe['channellower'] = dataframe['yhat1'] * (1 - dataframe['h2_move_mean']) dataframe['channelupper'] = dataframe['yhat1'] * (1 + dataframe['h2_move_mean']) dataframe = detect_all_bullish_patterns(dataframe) dataframe['slope'] = dataframe['yhat1'].diff() dataframe['angle_degrees'] = dataframe['slope'].apply(lambda x: float(np.degrees(np.arctan(float(x)))) if x is not None else None) if not self.dp.runmode.value in ("backtest", "plot", "hyperopt"): logger.info(f'{pair} - DC: {self.cp:.2f} | 1/2: {self.h0:.2f} | 1/3: {self.h1:.2f} | 1/4: {self.h2:.2f}') return dataframe ### ENTRY CONDITIONS ### def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[ ( (self.use0.value == True) & (df['closePos_h2'] > df['closePos_h2'].shift()) & (df['closePos_h2'] < self.buy_cl1.value) & (df['closePos_h1'] < self.buy_cl2.value) & (df['closePos_h0'] < self.buy_cl3.value) & (df['closePosDiffMeanHalf'] > df['closePosDiff']) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'WT123') df.loc[ ( (self.use1.value == True)& (df['closePos_h2'] < self.buy_cl1.value) & (df['closePos_h2'] > df['closePos_h2'].shift()) & (df['closePosDiffMeanHalf'] > df['closePosDiff']) & (df['closePosDiffMeanFast'] < df['closePosDiffMean']) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'WT1') df.loc[ ( (self.use2.value == True)& (df['closePos_h2'] < self.buy_cl1.value) & (df['closePos_h1'] < self.buy_cl2.value) & (df['closePos_h2'] > df['closePos_h2'].shift()) & (df['closePosDiffMeanHalf'] > df['closePosDiff']) & (df['closePosDiffMeanFast'] < df['closePosDiffMean']) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'WT12') df.loc[ ( (self.use3.value == True) & # (df['kalman'] < df['ma_hi']) & (df['closePos_h1'] < self.buy_cl2.value) & (df['closePos_h0'] < self.buy_cl3.value) & (df['closePos_h2'] > df['closePos_h2'].shift()) & (df['closePosDiffMeanHalf'] > df['closePosDiff']) & (df['closePosDiffMeanFast'] < df['closePosDiffMean']) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'W23') df.loc[ ( (self.use4.value == True) & (df['closePosSum'] < df['closePosSumSmooDir']) & (df['closePosSumSmoo'] < df['closePosSumSmooDir']) & (self.buy_cls.value > df['closePosSumSmoo']) & (qtpylib.crossed_above(df['closePosSum'], df['closePosSumSmoo'])) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'cp x') df.loc[ ( (self.use5.value == True) & # (df['kalman'] < df['yhat1']) & (df['closePos_h1'] < self.buy_cl2.value) & (df['BOP_SMA'] < 0) & (qtpylib.crossed_above(df['BOP'], df['BOP_SMA'])) & (df['closePosDiffMeanHalf'] > df['closePosDiff']) & (df['closePosDiffMeanFast'] < df['closePosDiffMean']) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'BOP X') df.loc[ ( (self.use6.value == True) & (df['closePosSum'] < self.buy_cls.value) & (df['closePosSum'] < df['closePosSumSmoo']) & (df['closePos_h2'] > df['closePos_h2'].shift()) & (df['closePosDiffMeanHalf'] > df['closePosDiff']) & (df['closePosDiffMeanFast'] < df['closePosDiffMean']) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'Sum') return df ### EXIT CONDITIONS ### def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[ ( (self.use10.value == True) & (df['rsi'] > self.sell_rsi.value) & (df['closePos_h2'] > self.sell_cl1.value) & (df['closePos_h1'] > self.sell_cl2.value) & (df['closePos_h0'] > self.sell_cl3.value) & (df['closePos_h2'] < df['closePos_h2'].shift()) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'WT 123') df.loc[ ( # Signal: RSI crosses above 30 (df['rsi'] > self.sell_rsi.value) & (df['closePos_h2'] > self.sell_cl1.value) & (df['closePos_h1'] > self.sell_cl2.value) & (self.use11.value == True) & (df['closePos_h2'] < df['closePos_h2'].shift()) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'WT 12') df.loc[ ( # Signal: RSI crosses above 30 (df['rsi'] > self.sell_rsi.value) & (self.use12.value == True) & (df['closePos_h1'] > self.sell_cl2.value) & (df['closePos_h0'] > self.sell_cl3.value) & (df['closePos_h2'] < df['closePos_h2'].shift()) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'WT 23') df.loc[ ( # Signal: RSI crosses above 30 # (df['rsi'] > self.sell_rsi.value) & (self.use13.value == True) & (df['closePosSum'] > df['closePosSumSmooDir']) & (df['closePosSum'] > df['closePosSumSmoo']) & (df['closePos_h2'] < df['closePos_h2'].shift()) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'cp x') return df 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 kernel_regression(src, size, h, r, x_0): _currentWeight = 0.0 _cumulativeWeight = 0.0000000001 for i in range(len(src)): y = src.iloc[i] w = np.power(1 + (np.power(i, 2) / ((np.power(h, 2) * 2 * r))), -r) _currentWeight += y * w _cumulativeWeight += w if _cumulativeWeight == 0: return 0 return _currentWeight / _cumulativeWeight def nadaraya_watson(df, h, r, x_0, smoothColors, lag, mult=2): src = df['kalman'] size = len(src) yhat1 = [] yhat2 = [] nwe_up = [] nwe_down = [] nwe_entry = [] nwe_exit = [] wasBearish = [] wasBullish = [] isBearish = [] isBullish = [] isBearishChange = [] isBullishChange = [] isBullishCross = [] isBearishCross = [] isBullishSmooth = [] isBearishSmooth = [] colorByCross = [] colorByRate = [] plotColor = [] alertBullish = [] alertBearish = [] for i in range(size): if i >= h: window = src[i - h:i] yhat1_value = kernel_regression(window, h, h, r, x_0) yhat1.append(yhat1_value) # Compute envelopes mae = np.mean(np.abs(src[i - h:i] - yhat1_value)) * mult nwe_up.append(yhat1_value + mae) nwe_down.append(yhat1_value - mae) # Set entry and exit signals nwe_entry.append(1 if src[i] < nwe_down[-1] else 0) nwe_exit.append(1 if src[i] > nwe_up[-1] else 0) # Trend and crossover conditions if i > 1: wasBearish.append(yhat1[i - 2] > yhat1[i - 1]) wasBullish.append(yhat1[i - 2] < yhat1[i - 1]) else: wasBearish.append(False) wasBullish.append(False) if i > 0: isBearish.append(yhat1[i - 1] > yhat1[i]) isBullish.append(yhat1[i - 1] < yhat1[i]) else: isBearish.append(False) isBullish.append(False) isBearishChange.append(isBearish[-1] and wasBullish[-1] if wasBullish else False) isBullishChange.append(isBullish[-1] and wasBearish[-1] if wasBearish else False) if i >= h + lag: window = src[i - h - lag:i - lag] yhat2.append(kernel_regression(window, h, h, r, x_0)) # Crossover conditions with lag if i > 0: isBullishCross.append(yhat2[-1] > yhat1[i]) isBearishCross.append(yhat2[-1] < yhat1[i]) isBullishSmooth.append(yhat2[-1] > yhat1[i]) isBearishSmooth.append(yhat2[-1] < yhat1[i]) else: isBullishCross.append(False) isBearishCross.append(False) isBullishSmooth.append(False) isBearishSmooth.append(False) # Color and alert conditions colorByCross.append(1 if isBullishSmooth[-1] else -1 if isBearishSmooth[-1] else 0) colorByRate.append(1 if isBullish[-1] else -1 if isBearish[-1] else 0) plotColor.append(colorByCross[-1] if smoothColors else colorByRate[-1]) alertBullish.append(1 if isBullishCross[-1] else 0) alertBearish.append(-1 if isBearishCross[-1] else 0) else: yhat2.append(0) isBullishCross.append(False) isBearishCross.append(False) isBullishSmooth.append(False) isBearishSmooth.append(False) colorByCross.append(0) colorByRate.append(0) plotColor.append(0) alertBullish.append(0) alertBearish.append(0) else: yhat1.append(0) yhat2.append(0) nwe_up.append(np.nan) nwe_down.append(np.nan) nwe_entry.append(0) nwe_exit.append(0) wasBearish.append(False) wasBullish.append(False) isBearish.append(False) isBullish.append(False) isBearishChange.append(False) isBullishChange.append(False) isBullishCross.append(False) isBearishCross.append(False) isBullishSmooth.append(False) isBearishSmooth.append(False) colorByCross.append(0) colorByRate.append(0) plotColor.append(0) alertBullish.append(0) alertBearish.append(0) # Append the new columns to the dataframe df['yhat1'] = yhat1 df['yhat2'] = yhat2 df['nw_up'] = nwe_up df['nw_down'] = nwe_down df['nw_entry'] = nwe_entry df['nw_exit'] = nwe_exit df['wasBearish'] = wasBearish df['wasBullish'] = wasBullish df['isBearish'] = isBearish df['isBullish'] = isBullish df['isBearishChange'] = isBearishChange df['isBullishChange'] = isBullishChange df['isBullishCross'] = isBullishCross df['isBearishCross'] = isBearishCross df['isBullishSmooth'] = isBullishSmooth df['isBearishSmooth'] = isBearishSmooth df['colorByCross'] = colorByCross df['colorByRate'] = colorByRate df['plotColor'] = plotColor df['alertBullish'] = alertBullish df['alertBearish'] = alertBearish return df def detect_change_of_direction(indicator_values): """ Detects changes in direction in a trading indicator. Args: indicator_values (list or np.ndarray or pd.Series): The trading indicator values. Returns: np.ndarray: An array of the same length with 1 for upward change, -1 for downward change, and 0 otherwise. """ # Ensure the input is a NumPy array for efficient computation indicator_values = np.asarray(indicator_values) # Compute the difference between consecutive values diff = np.diff(indicator_values) # Create an array to hold the results (same length as input, pad with 0) change_direction = np.zeros_like(indicator_values, dtype=int) # Identify upward changes (1) and downward changes (-1) change_direction[1:] = np.sign(diff) # Detect changes of direction result = np.zeros_like(change_direction, dtype=int) result[1:] = np.where(np.diff(change_direction) != 0, change_direction[1:], 0) return result def calculate_sine_wave(src, hp_period): pi = 2*np.arcsin(1) alpha1 = (np.cos(0.707*2*pi/hp_period) + np.sin(0.707*2*pi/hp_period)-1)/np.cos(0.707*2*pi/hp_period) hp = np.zeros(len(src)) for i in range(2, len(src)): hp[i] = (1-alpha1/2)*(1-alpha1/2)*(src[i]-2*src[i-1]+src[i-2])+2*(1-alpha1)*hp[i-1]-(1-alpha1)*(1-alpha1)*hp[i-2] filt = np.zeros(len(src)) for i in range(6, len(src)): filt[i] = (7*hp[i]+6*hp[i-1]+5*hp[i-2]+4*hp[i-3]+3*hp[i-4]+2*hp[i-5]+hp[i-6])/28 wave = np.zeros(len(src)) for i in range(2, len(src)): wave[i] = (filt[i]+filt[i-1]+filt[i-2])/3 pwr = np.zeros(len(src)) for i in range(2, len(src)): pwr[i] = (filt[i]**2+filt[i-1]**2+filt[i-2]**2)/3 for i in range(2, len(src)): wave[i] = wave[i]/np.sqrt(pwr[i]) return wave def detect_all_bullish_patterns(dataframe: pd.DataFrame) -> pd.DataFrame: """ Detects all bullish candlestick patterns and updates the dataframe with a 'bullish_pattern' column. Parameters: dataframe (pd.DataFrame): Dataframe containing OHLC data with columns ['open', 'high', 'low', 'close']. Returns: pd.DataFrame: Updated dataframe with a new column 'bullish_pattern' indicating bullish candlestick patterns. """ # Ensure necessary columns are present required_columns = ['open', 'high', 'low', 'close'] for col in required_columns: if col not in dataframe.columns: raise ValueError(f"Missing required column: {col}") # List of bullish candlestick patterns bullish_patterns = { 'CDL_ENGULFING': talib.CDLENGULFING, # Bullish Engulfing 'CDL_HAMMER': talib.CDLHAMMER, # Hammer 'CDL_INVERTED_HAMMER': talib.CDLINVERTEDHAMMER, # Inverted Hammer 'CDL_MORNING_STAR': talib.CDLMORNINGSTAR, # Morning Star 'CDL_PIERCING': talib.CDLPIERCING, # Piercing Line 'CDL_THREE_INSIDE_UP': talib.CDL3INSIDE, # Three Inside Up 'CDL_THREE_WHITE_SOLDIERS': talib.CDL3WHITESOLDIERS, # Three White Soldiers 'CDL_KICKING': talib.CDLKICKING, # Kicking } # Initialize the 'bullish_pattern' column to False dataframe['bullish_pattern'] = False # Apply each pattern and update the 'bullish_pattern' column for pattern_name, pattern_func in bullish_patterns.items(): dataframe[pattern_name] = pattern_func( dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'] ) dataframe[pattern_name] = dataframe[pattern_name] > 0 # Convert to boolean (True for bullish) dataframe['bullish_pattern'] |= dataframe[pattern_name] # Combine all bullish patterns # Optionally, drop individual pattern columns # dataframe.drop(columns=bullish_patterns.keys(), inplace=True) return dataframe # Function to implement a simple 1D Kalman Filter def kalman_filter(data, Q=0.001, R=0.1): """ Applies a simple Kalman Filter to a time series. Args: data: numpy array of observations (closing prices) Q: Process noise covariance (higher = more uncertain about dynamics) R: Measurement noise covariance (higher = less trust in observations) Returns: xhat: Array of filtered estimates """ n = len(data) xhat = np.zeros(n) # a posteriori estimate of x P = np.zeros(n) # a posteriori error estimate xhat_pred = np.zeros(n) # a priori estimate of x P_pred = np.zeros(n) # a priori error estimate K = np.zeros(n) # Kalman gain # Initialize xhat[0] = data[0] P[0] = 1.0 for k in range(1, n): # Prediction step xhat_pred[k] = xhat[k - 1] P_pred[k] = P[k - 1] + Q # Update step K[k] = P_pred[k] / (P_pred[k] + R) xhat[k] = xhat_pred[k] + K[k] * (data[k] - xhat_pred[k]) P[k] = (1 - K[k]) * P_pred[k] return xhat