# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/flawless_lambo.py import logging import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from sqlalchemy.orm.base import RELATED_OBJECT_OK from sqlalchemy.sql.elements import or_ import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.exchange import timeframe_to_minutes from freqtrade.persistence import Trade from technical import indicators from datetime import datetime, timezone from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, RealParameter, IStrategy, IntParameter, merge_informative_pair class Github_DerSalvador_freqtrade_helm_chart__flawless_lambo__20260115_122204(IStrategy): # Add some logging logger = logging.getLogger(__name__) # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 @property def protections(self): return [{'method': 'MaxDrawdown', 'lookback_period': 360, 'trade_limit': 1, 'stop_duration': 720, 'max_allowed_drawdown': 0.05}, {'method': 'StoplossGuard', 'lookback_period': 4320, 'trade_limit': 1, 'stop_duration': 10080, 'only_per_pair': True}, {'method': 'LowProfitPairs', 'lookback_period': 1440, 'trade_limit': 1, 'stop_duration': 1440, 'required_profit': 0.003}] # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # ROI1 table: minimal_roi = {'120': 0.30135315985130107, '125': 0.29907620817843866, '130': 0.2967992565055762, '135': 0.29452230483271374, '140': 0.2922453531598513, '145': 0.2899684014869888, '150': 0.28769144981412637, '155': 0.2854144981412639, '160': 0.2831375464684015, '165': 0.28086059479553904, '170': 0.2785836431226766, '175': 0.2763066914498141, '180': 0.27402973977695166, '185': 0.2717527881040892, '190': 0.26947583643122675, '195': 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0.0021414100486223664, '3605': 0.0020705024311183165, '3610': 0.0019995948136142597, '3615': 0.0019286871961102096, '3620': 0.00185777957860616, '3625': 0.00178687196110211, '3630': 0.0017159643435980532, '3635': 0.0016450567260940033, '3640': 0.0015741491085899534, '3645': 0.0015032414910858966, '3650': 0.0014323338735818469, '3655': 0.0013614262560777968, '3660': 0.0012905186385737469, '3665': 0.00121961102106969, '3670': 0.0011487034035656402, '3675': 0.0010777957860615905, '3680': 0.0010068881685575334, '3685': 0.0009359805510534837, '3690': 0.0008650729335494337, '3695': 0.0007941653160453838, '3700': 0.0007232576985413269, '3705': 0.000652350081037277, '3710': 0.0005814424635332271, '3715': 0.0005105348460291703, '3720': 0.0004396272285251204, '3725': 0.00036871961102107054, '3730': 0.00029781199351702065, '3735': 0.00022690437601296384, '3740': 0.00015599675850891392, '3745': 8.508914100486403e-05, '3750': 1.4181523500814142e-05, '3755': 0, '3900': -0.01, '3960': -0.02, '4020': -0.03} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". # use_custom_stoploss = True stoploss = -1 #-0.10 # Trailing stop: trailing_stop = False trailing_stop_positive = 0.006 trailing_stop_positive_offset = 0.019 trailing_only_offset_is_reached = False process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 #30 # Optimal timeframe for the strategy. timeframe = '15m' # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False # exit_profit_offset = 0.019 ignore_roi_if_entry_signal = False # hyperopt params exit_rsi = DecimalParameter(60, 100, default=70) exit_williams = DecimalParameter(-30, 0, default=-10) # trailing exit (borrowed from UziChanTB2) custom_info_trail_exit = dict() trailing_exit_order_enabled = True # trailing_expire_seconds = 1800 #NOTE 5m timeframe # trailing_expire_seconds = 1800/5 #NOTE 1m timeframe trailing_expire_seconds = 1800 * 3 #NOTE 15m timeframe # trailing_expire_seconds = 1800*6 trailing_exit_uptrend_enabled = True trailing_expire_seconds_uptrend = 300 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_exit_max_stop = 0.01 # stop trailing exit if current_price < starting_price * (1+trailing_entry_max_stop) trailing_exit_max_exit = 0.0 # exit if price between downlimit (=max of serie (current_price * (1 + trailing_exit_offset())) and (start_price * 1+trailing_exit_max_exit)) abort_trailing_when_exit_signal_triggered = False init_trailing_exit_dict = {'trailing_exit_order_started': False, 'trailing_exit_order_downlimit': 0, 'start_trailing_exit_price': 0, 'exit_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_exit_trailing': False} # Optional order type mapping. order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False} # Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} @property def plot_config(self): return {'main_plot': {'bb.lower': {'color': '#9c6edc', 'type': 'line'}, 'bb.upper': {'color': '#9c6edc', 'type': 'line'}, 'vwma': {'color': '#4f9f02', 'type': 'line'}}, 'subplots': {'obv': {'OBV': {'color': '#1b61ab', 'type': 'line'}, 'OBVSlope': {'color': '#f18b7a', 'type': 'line'}}, 'vpci': {'vpci': {'color': '#d59a7a', 'type': 'line'}}, 'macd': {'macd': {'color': '#1c3d6a', 'type': 'line'}, 'macdsignal': {'color': '#873480', 'type': 'line'}, 'macdhist': {'color': '#478a87', 'type': 'bar'}}, 'wiliams': {'williamspercent': {'color': '#10f551', 'type': 'line'}}, 'stoch + rsi': {'rsi': {'color': '#d7affd', 'type': 'line'}, 'slowd': {'color': '#d7cc5c', 'type': 'line'}, 'fastk': {'color': '#186f86', 'type': 'line'}}, 'adx': {'adx': {'color': '#c392cd', 'type': 'line'}, 'plus.di': {'color': '#bcd6c5', 'type': 'line'}, 'plus.di.slope': {'color': '#ffffff', 'type': 'line'}, 'minus.di': {'color': '#eb044c', 'type': 'line'}}}} def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.timeframe) for pair in pairs] return informative_pairs def trailing_exit(self, pair, reinit=False): # returns trailing exit info for pair (init if necessary) if not pair in self.custom_info_trail_exit: self.custom_info_trail_exit[pair] = dict() if reinit or not 'trailing_exit' in self.custom_info_trail_exit[pair]: self.custom_info_trail_exit[pair]['trailing_exit'] = self.init_trailing_exit_dict.copy() return self.custom_info_trail_exit[pair]['trailing_exit'] def trailing_exit_info(self, pair: str, current_price: float): # current_time live, dry run current_time = datetime.now(timezone.utc) if not self.debug_mode: return trailing_exit = self.trailing_exit(pair) duration = 0 try: duration = current_time - trailing_exit['start_trailing_time'] except TypeError: duration = 0 finally: self.logger.info(f"'\x1b[36m'SELL: pair: {pair} : start: {trailing_exit['start_trailing_exit_price']:.4f}, duration: {duration}, current: {current_price:.4f}, downlimit: {trailing_exit['trailing_exit_order_downlimit']:.4f}, profit: {self.current_trailing_exit_profit_ratio(pair, current_price) * 100:.2f}%, offset: {trailing_exit['offset']}") def current_trailing_exit_profit_ratio(self, pair: str, current_price: float) -> float: trailing_exit = self.trailing_exit(pair) if trailing_exit['trailing_exit_order_started']: return (current_price - trailing_exit['start_trailing_exit_price']) / trailing_exit['start_trailing_exit_price'] else: #return 0-((trailing_exit['start_trailing_exit_price'] - current_price) / trailing_exit['start_trailing_exit_price']) return 0 def trailing_exit_offset(self, dataframe, pair: str, current_price: float): current_trailing_exit_profit_ratio = self.current_trailing_exit_profit_ratio(pair, current_price) last_candle = dataframe.iloc[-1] adapt = last_candle['perc_norm'].round(5) default_offset = 0.003 * (1 + adapt) #NOTE: default_offset 0.003 <--> 0.006 trailing_exit = self.trailing_exit(pair) if not trailing_exit['trailing_exit_order_started']: return default_offset # example with duration and indicators # dry run, live only last_candle = dataframe.iloc[-1] current_time = datetime.now(timezone.utc) trailing_duration = current_time - trailing_exit['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if current_trailing_exit_profit_ratio > 0 and last_candle['exit'] != 0: # more than 1h, price over first signal, exit signal still active -> exit return 'forceexit' else: # wait for next signal return None elif self.trailing_exit_uptrend_enabled and trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend and (current_trailing_exit_profit_ratio < -1 * self.min_uptrend_trailing_profit): # less than 90s and price is falling, exit return 'forceexit' if current_trailing_exit_profit_ratio > 0: # current price is lower than initial price return default_offset # 0.06: 0.02, # 0.03: 0.01, trailing_exit_offset = {0.1: default_offset} for key in trailing_exit_offset: if current_trailing_exit_profit_ratio < key: return trailing_exit_offset[key] return default_offset def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Retrieve best bid and best ask from the orderbook # ------------------------------------ # first check if dataprovider is available # if self.dp: # if self.dp.runmode.value in ('live', 'dry_run'): # ob = self.dp.orderbook(metadata['pair'], 1) # dataframe['best_bid'] = ob['bids'][0][0] # dataframe['best_ask'] = ob['asks'][0][0] # Bollinger! bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb.lower'] = bollinger['lower'] dataframe['bb.middle'] = bollinger['mid'] dataframe['bb.upper'] = bollinger['upper'] # Added PCB Style OBV dataframe['OBV'] = ta.OBV(dataframe) dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV']) # VWMA # vwma_period = 13 # dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() / # dataframe['volume'].rolling(vwma_period).sum()) # VWAP # vwap_period = 20 # dataframe['vwap'] = qtpylib.rolling_vwap(dataframe, window=vwap_period) # VPCI dataframe['vpci'] = indicators.vpci(dataframe, period_long=14) #williamsR dataframe['williamspercent'] = indicators.williams_percent(dataframe) # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['plus.di'] = ta.PLUS_DI(dataframe) dataframe['minus.di'] = ta.MINUS_DI(dataframe) dataframe['plus.di.slope'] = pta.momentum.slope(dataframe['plus.di']) # RSI dataframe['rsi'] = ta.RSI(dataframe) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stochastic Slow stoch_slow = ta.STOCH(dataframe) dataframe['slowd'] = stoch_slow['slowd'] dataframe['slowk'] = stoch_slow['slowk'] # Perc dataframe['perc'] = (dataframe['high'] - dataframe['low']) / dataframe['low'] * 100 dataframe['avg3_perc'] = ta.EMA(dataframe['perc'], 3) dataframe['perc_norm'] = (dataframe['perc'] - dataframe['perc'].rolling(50).min()) / (dataframe['perc'].rolling(50).max() - dataframe['perc'].rolling(50).min()) self.trailing_exit(metadata['pair']) return dataframe def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Bollinger! bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb.lower'] = bollinger['lower'] dataframe['bb.middle'] = bollinger['mid'] dataframe['bb.upper'] = bollinger['upper'] # Added PCB Style OBV dataframe['OBV'] = ta.OBV(dataframe) dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV']) # VWMA # vwma_period = 13 # dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() / # dataframe['volume'].rolling(vwma_period).sum()) # VWAP # vwap_period = 20 # dataframe['vwap'] = qtpylib.rolling_vwap(dataframe, window=vwap_period) # VPCI dataframe['vpci'] = indicators.vpci(dataframe, period_long=14) #williamsR dataframe['williamspercent'] = indicators.williams_percent(dataframe) # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['plus.di'] = ta.PLUS_DI(dataframe) dataframe['minus.di'] = ta.MINUS_DI(dataframe) dataframe['plus.di.slope'] = pta.momentum.slope(dataframe['plus.di']) # RSI dataframe['rsi'] = ta.RSI(dataframe) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stochastic Slow stoch_slow = ta.STOCH(dataframe) dataframe['slowd'] = stoch_slow['slowd'] dataframe['slowk'] = stoch_slow['slowk'] # Perc dataframe['perc'] = (dataframe['high'] - dataframe['low']) / dataframe['low'] * 100 dataframe['avg3_perc'] = ta.EMA(dataframe['perc'], 3) dataframe['perc_norm'] = (dataframe['perc'] - dataframe['perc'].rolling(50).min()) / (dataframe['perc'].rolling(50).max() - dataframe['perc'].rolling(50).min()) self.trailing_exit(metadata['pair']) return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool: val = super().confirm_trade_exit(pair, trade, order_type, amount, rate, time_in_force, exit_reason, **kwargs) if val: if self.trailing_exit_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): val = False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) >= 1: last_candle = dataframe.iloc[-1].squeeze() current_price = rate trailing_exit = self.trailing_exit(pair) trailing_exit_offset = self.trailing_exit_offset(dataframe, pair, current_price) if trailing_exit['allow_exit_trailing']: if not trailing_exit['trailing_exit_order_started'] and last_candle['exit'] != 0: trailing_exit['trailing_exit_order_started'] = True trailing_exit['trailing_exit_order_downlimit'] = last_candle['close'] trailing_exit['start_trailing_exit_price'] = last_candle['close'] trailing_exit['exit_tag'] = last_candle['exit_tag'] trailing_exit['start_trailing_time'] = datetime.now(timezone.utc) trailing_exit['offset'] = 0 self.trailing_exit_info(pair, current_price) self.logger.info(f"start trailing exit for {pair} at {last_candle['close']}") elif trailing_exit['trailing_exit_order_started']: if trailing_exit_offset == 'forceexit': # exit in custom conditions val = True ratio = '%.2f' % (self.current_trailing_exit_profit_ratio(pair, current_price) * 100) self.trailing_exit_info(pair, current_price) self.logger.info(f'FORCESELL for {pair} ({ratio} %, {current_price})') elif trailing_exit_offset is None: # stop trailing exit custom conditions self.trailing_exit(pair, reinit=True) self.logger.info(f'STOP trailing exit for {pair} because "trailing exit offset" returned None') elif current_price > trailing_exit['trailing_exit_order_downlimit']: # update downlimit old_downlimit = trailing_exit['trailing_exit_order_downlimit'] self.custom_info_trail_exit[pair]['trailing_exit']['trailing_exit_order_downlimit'] = max(current_price * (1 - trailing_exit_offset), self.custom_info_trail_exit[pair]['trailing_exit']['trailing_exit_order_downlimit']) self.custom_info_trail_exit[pair]['trailing_exit']['offset'] = trailing_exit_offset self.trailing_exit_info(pair, current_price) self.logger.info(f"update trailing exit for {pair} at {old_downlimit} -> {self.custom_info_trail_exit[pair]['trailing_exit']['trailing_exit_order_downlimit']}") elif current_price > trailing_exit['start_trailing_exit_price'] * (1 - self.trailing_exit_max_exit): # exit! current price < downlimit && higher than starting price val = True ratio = '%.2f' % (self.current_trailing_exit_profit_ratio(pair, current_price) * 100) self.trailing_exit_info(pair, current_price) self.logger.info(f"current price ({current_price}) < downlimit ({trailing_exit['trailing_exit_order_downlimit']}) but higher than starting price ({trailing_exit['start_trailing_exit_price'] * (1 + self.trailing_exit_max_exit)}). OK for {pair} ({ratio} %)") elif current_price < trailing_exit['start_trailing_exit_price'] * (1 - self.trailing_exit_max_stop): # stop trailing, exit fast, price too low val = True self.trailing_exit_info(pair, current_price) self.logger.info(f'STOP trailing exit for {pair} because of the price is much lower than starting price * {1 + self.trailing_exit_max_stop}') else: # uplimit > current_price > max_price, continue trailing and wait for the price to go down self.trailing_exit_info(pair, current_price) self.logger.info(f'price too low for {pair} !') else: self.logger.info(f'Wait for next exit signal for {pair}') if val == True: self.trailing_exit_info(pair, rate) self.trailing_exit(pair, reinit=True) self.logger.info(f'STOP trailing exit for {pair} because I SOLD it') if exit_reason != 'exit_signal': val = True return val def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe['volume'] > 0) & (dataframe['OBVSlope'] > 0) & (dataframe['plus.di.slope'] > 0) & (dataframe['williamspercent'] < -66) & qtpylib.crossed_above(dataframe['close'], dataframe['bb.lower']), 'entry'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe['volume'] > 0) & (dataframe['close'] > dataframe['bb.upper']) & (dataframe['plus.di.slope'] < 0) & (dataframe['williamspercent'] >= self.exit_williams.value) & (dataframe['rsi'] >= self.exit_rsi.value), 'exit'] = 1 if self.trailing_exit_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): last_candle = dataframe.iloc[-1].squeeze() trailing_exit = self.trailing_exit(metadata['pair']) if last_candle['exit'] != 0: if not trailing_exit['trailing_exit_order_started']: open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all() if open_trades: self.logger.info(f"Set 'allow_SELL_trailing' to True for {metadata['pair']} to start *SELL* trailing") # self.custom_info_trail_entry[metadata['pair']]['trailing_entry']['allow_trailing'] = True trailing_exit['allow_exit_trailing'] = True initial_exit_tag = last_candle['exit_tag'] if 'exit_tag' in last_candle else 'exit signal' dataframe.loc[:, 'exit_tag'] = f"{initial_exit_tag} (start trail price {last_candle['close']})" elif trailing_exit['trailing_exit_order_started'] == True: self.logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger exit signal!") dataframe.loc[:, 'exit'] = 1 dataframe.loc[:, 'exit_tag'] = trailing_exit['exit_tag'] return dataframe # "All watched over by machines with loving grace..."