# source: https://raw.githubusercontent.com/XQuant42/ft_pantheon/9cc88e804b9f553d3e9fe90539030b18cb8c0b2e/user_data/strategies/SolipsisMM.py import numpy as np import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # #import arrow from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair from typing import Dict, List, Optional, Tuple from pandas import DataFrame, Series from functools import reduce from datetime import datetime, timedelta from freqtrade.persistence import Trade from statistics import mean from cachetools import TTLCache from scipy.signal import argrelextrema # Get rid of pandas warnings during backtesting import pandas as pd pd.options.mode.chained_assignment = None # default='warn' # Strategy specific imports, files must reside in same folder as strategy import sys from pathlib import Path sys.path.append(str(Path(__file__).parent)) import custom_indicators as cta """ TODO: - Continue to hunt for a better all around buy signal. - Prevent buys when potential for strong downward trend and not just a dip? - ADR guards are helping but need to reduce losing trade counts. - Need to reduce drawdown. - Tweak ROI Trend Ride - Maybe use free_slots as a factor in how eager we are to sell? - Maybe further exploit the ROI table itself using trade data? - Per-pair automatic ROI points based on ADR/ATR or something similar? - Tweak sell signal - Continue to evaluate good circumstances to bail and sell vs hold on for recovery - Further enchance and optimize custom stop loss - Figure out a way to directly feed a daily hyperopt output into a running strategy and reload it? STRATEGY NOTES: - It is recommended to configure protections *if/as* you will use them in live and run hyperopt/backtest with --enable-protections as this strategy will hit a lot of stoplosses so the stoploss protection is helpful to test. - Keep in mind the sell signal (dynamic bailout) does not function in backtest and this strategy should be validated and tested in dry-run before live. If you do not want to use the sell and only rely on the bits of the strategy that can be backtested be sure to turn use_sell_signal = False. - If running backtest/hyperopt around the portion of the sell signal that is testable, keep in mind in live/dry it will not sell nearly as frequently due to the profit guard and other_profit / free_slot guards. - Keep in mind that due to the dynamic ROI trend ride this strategy implements that most sells for ROI will actually sell for more profit than the ROI table dictates and you can assume that your average profit from ROI based sells will be higher than the backtest shows. - It is *not* worthwhile to hyperopt for roi as it does not take into account the roi trend ride we are doing and having ROI points that are too high will sort of be counter-productive to how this strategy works even if it seems to produce better results in backtesting. - It is *probably not* worthwhile to hyperopt the stoploss as we can't hyperopt any of the parameters in the custom stoploss so hyperopting the stoploss is only looking at one aspect of it. - It is *not* recommended to use freqtrades built-in trailing stop, nor to hyperopt for that. - It is *highly* recommended to backtest with use_sell_signal = False because it will not behave remotely the same in dry/live - It is *highly* recommended to hyperopt this with '--spaces buy' only or 'buy sell' and at least 5000 total epochs, but a preliminary set of sane defaults is: buy_params = { 'base-fib-level': 0.618, 'base-mp': 30, 'base-pmax': 'up', 'base-pmax-enable': False, 'base-rmi-fast': 50, 'base-rmi-slow': 20, 'base-trend': 'down', 'inf-fib-level': 0.236, 'inf-guard': 'upper', 'inf-pct-adr-bot': 0.05, 'inf-pct-adr-top': 0.80, 'inf-rsi': 20, 'xtra-base-fiat-rsi': 30, 'xtra-base-stake-rsi': 60, 'xtra-inf-stake-rmi': 20 } sell_params = { 'sell-rmi-drop': 0.50, 'sell-pmax-enable': False } """ class Github_XQuant42_ft_pantheon__SolipsisMM__20251123_060325(IStrategy): # Recommended for USD/USDT/etc. timeframe = '5m' inf_timeframe = '1h' buy_params = { 'base-fib-level': 0.618, 'base-mp': 84, 'base-pmax': 'up', 'base-pmax-enable': True, 'base-rmi-fast': 44, 'base-rmi-slow': 27, 'base-trend': 'down', 'inf-fib-level': 0.0, 'inf-guard': 'both', 'inf-pct-adr-bot': 0.05725, 'inf-pct-adr-top': 0.98309, 'inf-rsi': 22, 'xtra-base-fiat-rsi': 70, 'xtra-base-stake-rsi': 50, 'xtra-inf-stake-rmi': 47 } # Sell hyperspace params: sell_params = { 'sell-pmax-enable': True, 'sell-rmi-drop': 0.89661 } # Custom buy/sell parameters per pair custom_pair_params = [] # Recommended on 5m timeframe minimal_roi = { "0": 0.05, "30": 0.025, "120": 0.01, "360": 0.01, "720": 0.005, "1440": 0 } # Optional dynamic_roi = { 'dynamic_roi_enabled': True, 'dynamic_roi_type': 'connect' } stoploss = -0.30 # Optional use_custom_stoploss = True custom_stop = { 'decay-time': 1080, # minutes to reach end 'decay-delay': 0, # minutes to wait before decay starts 'decay-start': -0.30, # starting value: should be the same as initial stoploss 'decay-end': -0.03, # ending value 'pos-trail': False, # enable trailing once positive 'pos-threshold': 0.005, # trail after how far positive 'pos-trail-dist': 0.015 # how far behind to place the trail } # Recommended use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True # Required startup_candle_count: int = 72 process_only_new_candles = False # Strategy Specific Variable Storage custom_trade_info = {} custom_fiat = "USD" # Only relevant if stake is BTC or ETH custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300) # Parameters dealing with the ROI ride the sell signal behind active_trade custom_active_trade = { # linear decay on RMI trend threshold 'roi-pft-factor': 400, 'roi-rmi-start': 30, 'roi-rmi-end': 70, 'roi-delay': 180, 'roi-time': 720, # loss thresholds 'sell-mkt-down': -0.04, 'loss-start': -0.03, 'loss-end': 0, 'loss-delay': 0, 'loss-time': 240 } """ Informative Pair Definitions """ def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_timeframe) for pair in pairs] # add extra informative pairs if the stake is BTC or ETH if self.config['stake_currency'] in ('BTC', 'ETH'): for pair in pairs: coin, stake = pair.split('/') coin_fiat = f"{coin}/{self.custom_fiat}" informative_pairs += [(coin_fiat, self.timeframe)] stake_fiat = f"{self.config['stake_currency']}/{self.custom_fiat}" informative_pairs += [(stake_fiat, self.timeframe)] informative_pairs += [(stake_fiat, self.inf_timeframe)] return informative_pairs """ Indicator Definitions """ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: buy_params = self.get_pair_params(metadata['pair'], 'buy') sell_params = self.get_pair_params(metadata['pair'], 'sell') self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair']) # Base timeframe indicators dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5) dataframe['rmi-fast'] = cta.RMI(dataframe, length=8, mom=4) # Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/ dataframe['roc'] = ta.ROC(dataframe, timeperiod=6) dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6) # RMI Trends and Peaks dataframe['rmi-up'] = np.where(dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(),1,0) dataframe['rmi-dn'] = np.where(dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(),1,0) dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2,1,0) dataframe['rmi-dn-trend'] = np.where(dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2,1,0) dataframe['rmi-max'] = dataframe['rmi-slow'].rolling(10, min_periods=1).max() # Fibonacci Retracements dataframe['fib-ret'] = cta.fib_ret(dataframe) # PMAX # matype = ema, src = hl2 # pmax is really slow, only enable it if its actually being used. if buy_params['base-pmax-enable'] == True or sell_params['sell-pmax-enable'] == True: atr, ma, pmax, pm_loc = cta.PMAX(dataframe, period=10, multiplier=3, length=12, MAtype=1, src=2) dataframe['pmax-trend'] = pm_loc else: dataframe['pmax-trend'] = False # Other stake specific informative indicators # e.g if stake is BTC and current coin is XLM (pair: XLM/BTC) # TODO: Re-evaluate the value of these informative indicators if self.config['stake_currency'] in ('BTC', 'ETH'): coin, stake = metadata['pair'].split('/') fiat = self.custom_fiat coin_fiat = f"{coin}/{fiat}" stake_fiat = f"{stake}/{fiat}" # Informative COIN/FIAT e.g. XLM/USD coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat, timeframe=self.timeframe) dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14) # Informative STAKE/FIAT e.g. BTC/USD stake_fiat_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.timeframe) stake_fiat_inf_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.inf_timeframe) dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14) dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = cta.RMI(stake_fiat_inf_tf, length=21, mom=5) # Base pair informative timeframe indicators informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe) informative['rsi'] = ta.RSI(informative, timeperiod=14) # Get the "average day range" between the 1d high and 3d low to set up guards informative['1d_high'] = informative['close'].rolling(24).max() informative['3d_low'] = informative['close'].rolling(72).min() informative['adr'] = informative['1d_high'] - informative['3d_low'] informative['fib-ret'] = cta.fib_ret(informative) min_peaks = argrelextrema(dataframe['close'].values, np.less, order=100) for mp in min_peaks[0]: dataframe.at[mp, 'buy_signal'] = True dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True) return dataframe """ Buy Signal """ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.get_pair_params(metadata['pair'], 'buy') at = self.custom_active_trade trade_data = self.custom_trade_info[metadata['pair']] conditions = [] """ The primary "secret sauce" of Solipsis is to take advantage of the ignore_roi_if_buy_signal setting. Ideally, the ROI table is very tight and aggressive allowing for quick exits on ROI without reliance on a sell signal. However, if certain criteria are met for an open trade, we stimulate a sticking buy signal on purpose to prevent the bot from selling to the ROI in the midst of an upward trend. This is not currently able to be backtested or hyperopted so all settings here must be tested and validated in dry-run or live. It is safe to assume that when backtesting, all sells for reason roi will have likely been for higher profits in live trading than in backtest. """ # If active trade, look at trend to persist a buy signal for ignore_roi_if_buy_signal if trade_data['active_trade']: profit_factor = (1 - (dataframe['rmi-slow'].iloc[-1] / at['roi-pft-factor'])) rmi_grow = cta.linear_growth(at['roi-rmi-start'], at['roi-rmi-end'], at['roi-delay'], at['roi-time'], trade_data['open_minutes']) conditions.append(trade_data['current_profit'] > (trade_data['peak_profit'] * profit_factor)) # TODO: Experiment with replacing or augmenting this with PMAX conditions.append( ( (params['base-pmax-enable'] == True) & (dataframe['pmax-trend'] == 'up') ) | ( (dataframe['rmi-up-trend'] == 1) & (dataframe['rmi-slow'] >= rmi_grow) ) ) # Standard signals for entering new trades else: # Guards on informative timeframe if params['inf-guard'] == 'upper' or params['inf-guard'] == 'both': conditions.append( (dataframe['close'] <= dataframe[f"3d_low_{self.inf_timeframe}"] + (params['inf-pct-adr-top'] * dataframe[f"adr_{self.inf_timeframe}"])) ) if params['inf-guard'] == 'lower' or params['inf-guard'] == 'both': conditions.append( (dataframe['close'] >= dataframe[f"3d_low_{self.inf_timeframe}"] + (params['inf-pct-adr-bot'] * dataframe[f"adr_{self.inf_timeframe}"])) ) # Informative Timeframe conditions.append( (dataframe[f"rsi_{self.inf_timeframe}"] >= params['inf-rsi']) & (dataframe[f"fib-ret_{self.inf_timeframe}"] >= params['inf-fib-level']) ) if params['base-pmax-enable'] == True: conditions.append((dataframe['pmax-trend'] == params['base-pmax'])) # # Base Timeframe # Look for bottom of downward trend and expect bounce if params['base-trend'] == 'down': conditions.append( (dataframe['fib-ret'] <= params['base-fib-level']) & (dataframe['rmi-dn-trend'] == 1) & (dataframe['rmi-slow'] >= params['base-rmi-slow']) & (dataframe['rmi-fast'] <= params['base-rmi-fast']) & (dataframe['mp'] <= params['base-mp']) ) # Look for upward trend elif params['base-trend'] == 'up': conditions.append( (dataframe['fib-ret'] >= params['base-fib-level']) & (dataframe['rmi-up-trend'] == 1) & (dataframe['rmi-slow'] <= params['base-rmi-slow']) & (dataframe['rmi-fast'] >= params['base-rmi-fast']) & (dataframe['mp'] >= params['base-mp']) ) # Extra conditions for BTC/ETH stakes on additional informative pairs if self.config['stake_currency'] in ('BTC', 'ETH'): conditions.append( (dataframe[f"{self.config['stake_currency']}_rsi"] < params['xtra-base-stake-rsi']) | (dataframe[f"{self.custom_fiat}_rsi"] > params['xtra-base-fiat-rsi']) ) conditions.append(dataframe[f"{self.config['stake_currency']}_rmi_{self.inf_timeframe}"] < params['xtra-inf-stake-rmi']) # Applies to active or new trades conditions.append(dataframe['buy_signal']) conditions.append(dataframe['volume'].gt(0)) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe """ Sell Signal """ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.get_pair_params(metadata['pair'], 'sell') at = self.custom_active_trade trade_data = self.custom_trade_info[metadata['pair']] conditions = [] """ Previous Schism sell implementation has been *partially* replaced by custom_stoploss. Doing it there makes it able to be backtested more realistically but some of the functionality such as the ability to use other trades or the # of slots in our decision need to remain here. The current sell is meant to be a pre-stoploss bailout but most sells at a loss should happen due to the stoploss in live/dry. ** Only part of this signal functions in backtest/hyperopt. Can only be optimized in live/dry. ** Results in backtest/hyperopts will sell far more readily than in live due to the profit guards. """ # If we are in an active trade (which is the only way a sell can occur...) # This is needed to block backtest/hyperopt from hitting the profit stuff and erroring out. if trade_data['active_trade']: # Decay a loss cutoff point where we allow a sell to occur idea is this allows # a trade to go into the negative a little bit before we react to sell. loss_cutoff = cta.linear_growth(at['loss-start'], at['loss-end'], at['loss-delay'], at['loss-time'], trade_data['open_minutes']) conditions.append((trade_data['current_profit'] < loss_cutoff)) # Examine the state of our other trades and free_slots to inform the decision if trade_data['other_trades']: if trade_data['free_slots'] > 0: # If the average of all our other trades is below a certain threshold based # on free slots available, hold and wait for market recovery. hold_pct = (1/trade_data['free_slots']) * at['sell-mkt-down'] conditions.append(trade_data['avg_other_profit'] >= hold_pct) else: # If we are out of free slots disregard the above and allow the biggest loser to sell. conditions.append(trade_data['biggest_loser'] == True) # Primary sell trigger rmi_drop = dataframe['rmi-max'] - (dataframe['rmi-max'] * params['sell-rmi-drop']) conditions.append( (dataframe['rmi-dn-trend'] == 1) & (qtpylib.crossed_below(dataframe['rmi-slow'], rmi_drop)) & (dataframe['volume'].gt(0)) ) if params['sell-pmax-enable'] == True: conditions.append((dataframe['pmax-trend'] == 'down')) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe """ Custom Stoploss """ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: start = self.custom_stop['decay-start'] end = self.custom_stop['decay-end'] time = self.custom_stop['decay-time'] delay = self.custom_stop['decay-delay'] pos_trail = self.custom_stop['pos-trail'] pos_threshold = self.custom_stop['pos-threshold'] pos_trail_dist = self.custom_stop['pos-trail-dist'] open_minutes: int = (current_time - trade.open_date).total_seconds() // 60 decay_stoploss = cta.linear_growth(start, end, delay, time, open_minutes) min_profit = trade.calc_profit_ratio(trade.min_rate) max_profit = trade.calc_profit_ratio(trade.max_rate) profit_diff = current_profit - min_profit if pos_trail == True: # if trade is positive above threshold, immediately move the stoploss up to x% behind it if current_profit > pos_threshold: return current_profit - pos_trail_dist # if we might be on a rebound, move the stoploss to the low point or keep it where it was if current_profit > min_profit: if profit_diff > 0.015: return min_profit return -1 # otherwise return the time decaying stoploss return decay_stoploss """ Trade Timeout Overloads """ def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: bid_strategy = self.config.get('bid_strategy', {}) ob = self.dp.orderbook(pair, 1) current_price = ob[f"{bid_strategy['price_side']}s"][0][0] if current_price > order['price'] * 1.01: return True return False def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: ask_strategy = self.config.get('ask_strategy', {}) ob = self.dp.orderbook(pair, 1) current_price = ob[f"{ask_strategy['price_side']}s"][0][0] if current_price < order['price'] * 0.99: return True return False def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: bid_strategy = self.config.get('bid_strategy', {}) ob = self.dp.orderbook(pair, 1) current_price = ob[f"{bid_strategy['price_side']}s"][0][0] if current_price > rate * 1.01: return False return True """ Freqtrade ROI Overload for dynamic ROI functionality """ def min_roi_reached_dynamic(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]: dynamic_roi = self.dynamic_roi minimal_roi = self.minimal_roi if not dynamic_roi: return None, None if 'dynamic_roi_type' in dynamic_roi and dynamic_roi['dynamic_roi_type'] \ in ['linear', 'exponential', 'connect']: roi_type = dynamic_roi['dynamic_roi_type'] # linear decay: f(t) = start - (rate * t) if roi_type == 'linear': if 'dynamic_roi_start' in dynamic_roi and 'dynamic_roi_end' in dynamic_roi and \ 'dynamic_roi_time' in dynamic_roi: start = dynamic_roi['dynamic_roi_start'] end = dynamic_roi['dynamic_roi_end'] time = dynamic_roi['dynamic_roi_time'] rate = (start - end) / time min_roi = max(end, start - (rate * trade_dur)) else: return None, None # exponential decay: f(t) = start * e^(-rate*t) elif roi_type == 'exponential': if 'dynamic_roi_start' in dynamic_roi and 'dynamic_roi_end' in dynamic_roi and \ 'dynamic_roi_rate' in dynamic_roi: start = dynamic_roi['dynamic_roi_start'] end = dynamic_roi['dynamic_roi_end'] rate = dynamic_roi['dynamic_roi_rate'] min_roi = max(end, start * np.exp(-rate*trade_dur)) else: return None, None # "connect the dots" between the points on the minima_roi table elif roi_type == 'connect': if not minimal_roi: return None, None # figure out where we are in the defined roi table past_roi = list(filter(lambda x: x <= trade_dur, minimal_roi.keys())) next_roi = list(filter(lambda x: x > trade_dur, minimal_roi.keys())) # if we are past the final point in the table, use that key/vaule pair if not past_roi: return None, None current_entry = max(past_roi) if not next_roi: return current_entry, minimal_roi[current_entry] next_entry = min(next_roi) # use the slope-intercept formula between the two points # y = mx + b x1, y1 = current_entry, minimal_roi[current_entry] x2, y2 = next_entry, minimal_roi[next_entry] m = (y1-y2)/(x1-x2) b = (x1*y2 - x2*y1)/(x1-x2) min_roi = (m * trade_dur) + b else: return None, None return trade_dur, min_roi def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool: trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) if self.dynamic_roi and 'dynamic_roi_enabled' in self.dynamic_roi \ and self.dynamic_roi['dynamic_roi_enabled']: _, roi = self.min_roi_reached_dynamic(trade_dur) else: _, roi = self.min_roi_reached_entry(trade_dur) if roi is None: return False else: return current_profit > roi """ Custom Methods for Strategy Parameters, Trade, and Price Data Consider splitting this out into a helper file. """ def populate_trades(self, pair: str) -> dict: """ Query the database and populate the custom_trade_info dict. """ if not pair in self.custom_trade_info: self.custom_trade_info[pair] = {} trade_data = {} trade_data['active_trade'] = trade_data['other_trades'] = trade_data['biggest_loser'] = False if self.config['runmode'].value in ('live', 'dry_run'): active_trade = Trade.get_trades([Trade.pair == pair, Trade.is_open.is_(True),]).all() if active_trade: current_rate = self.get_current_price(pair, True) active_trade[0].adjust_min_max_rates(current_rate) present = arrow.utcnow() trade_start = arrow.get(active_trade[0].open_date) open_minutes = (present - trade_start).total_seconds() // 60 trade_data['active_trade'] = True trade_data['current_profit'] = active_trade[0].calc_profit_ratio(current_rate) trade_data['peak_profit'] = max(0, active_trade[0].calc_profit_ratio(active_trade[0].max_rate)) trade_data['open_minutes'] : int = open_minutes trade_data['open_candles'] : int = (open_minutes // active_trade[0].timeframe) # floor else: trade_data['current_profit'] = trade_data['peak_profit'] = 0.0 trade_data['open_minutes'] = trade_data['open_candles'] = 0 other_trades = Trade.get_trades([Trade.pair != pair, Trade.is_open.is_(True),]).all() if other_trades: trade_data['other_trades'] = True other_profit = tuple(trade.calc_profit_ratio(self.get_current_price(trade.pair, False)) for trade in other_trades) trade_data['avg_other_profit'] = mean(other_profit) if trade_data['current_profit'] < min(other_profit): trade_data['biggest_loser'] = True else: trade_data['avg_other_profit'] = 0 open_trades = len(Trade.get_open_trades()) # this was lazy. trade_data['free_slots'] = max(0, self.config['max_open_trades'] - open_trades) return trade_data def get_current_price(self, pair: str, refresh: bool) -> float: """ Query the exchange for current price information, used in profit calculations. Implements a cache to prevent excessive api calls. """ if not refresh: rate = self.custom_current_price_cache.get(pair) if rate: return rate ask_strategy = self.config.get('ask_strategy', {}) if ask_strategy.get('use_order_book', False): ob = self.dp.orderbook(pair, 1) rate = ob[f"{ask_strategy['price_side']}s"][0][0] else: ticker = self.dp.ticker(pair) rate = ticker['last'] self.custom_current_price_cache[pair] = rate return rate def get_pair_params(self, pair: str, side: str) -> Dict: """ Returns buy/sell params that are specific to pairs or groups of pairs if desired. TODO: - Consider adding stoploss and/or ROI tables to the pair/group specific options """ buy_params = self.buy_params sell_params = self.sell_params if self.custom_pair_params: custom_params = next(item for item in self.custom_pair_params if pair in item['pairs']) if custom_params['buy_params']: buy_params = custom_params['buy_params'] if custom_params['sell_params']: sell_params = custom_params['sell_params'] if side == 'sell': return sell_params return buy_params # Sub-strategy with parameters specific to BTC stake class Github_XQuant42_ft_pantheon__SolipsisMM__20251123_060325_BTC(Github_XQuant42_ft_pantheon__SolipsisMM__20251123_060325): timeframe = '1h' inf_timeframe = '4h' buy_params = { 'inf-rsi': 64, 'mp': 55, 'rmi-fast': 31, 'rmi-slow': 16, 'xinf-stake-rmi': 67, 'xtf-fiat-rsi': 17, 'xtf-stake-rsi': 57 } minimal_roi = { "0": 0.05, "240": 0.025, "1440": 0.01, "4320": 0 } stoploss = -0.30 use_custom_stoploss = False # Sub-strategy with parameters specific to ETH stake class Github_XQuant42_ft_pantheon__SolipsisMM__20251123_060325_ETH(Github_XQuant42_ft_pantheon__SolipsisMM__20251123_060325): timeframe = '1h' inf_timeframe = '4h' buy_params = { 'inf-rsi': 13, 'inf-stake-rmi': 69, 'mp': 40, 'rmi-fast': 42, 'rmi-slow': 17, 'tf-fiat-rsi': 15, 'tf-stake-rsi': 92 } minimal_roi = { "0": 0.05, "240": 0.025, "1440": 0.01, "4320": 0 } stoploss = -0.30 use_custom_stoploss = False