# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Fakebuy.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair from pandas import DataFrame, Series from functools import reduce from datetime import datetime from freqtrade.persistence import Trade from technical.indicators import RMI from statistics import mean """ TODO: - Better buy signal. - Potentially leverage an external data source? """ def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) class Github_remiotore_freqtrade__Fakebuy__20260111_210550(IStrategy): timeframe = '5m' inf_timeframe = '1h' buy_params = { 'bbdelta-close': 0.01697, 'bbdelta-tail': 0.85522, 'close-bblower': 0.01167, 'closedelta-close': 0.00513, 'rocr-1h': 0.54614, 'volume': 32 } minimal_roi = { "0": 0.15, "5": 0.025, "10": 0.015, "30": 0.005 } stoploss = -0.085 use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True startup_candle_count: int = 168 custom_trade_info = {} def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_timeframe) for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.custom_trade_info[metadata['pair']] = trade_data = {} trade_data['active_trade'] = trade_data['other_trades'] = False if self.config['runmode'].value in ('live', 'dry_run'): active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True),]).all() other_trades = Trade.get_trades([Trade.pair != metadata['pair'], Trade.is_open.is_(True),]).all() if active_trade: current_rate = self.get_current_price(metadata['pair']) active_trade[0].adjust_min_max_rates(current_rate) trade_data['active_trade'] = True trade_data['current_profit'] = active_trade[0].calc_profit_ratio(current_rate) trade_data['peak_profit'] = active_trade[0].calc_profit_ratio(active_trade[0].max_rate) if other_trades: trade_data['other_trades'] = True total_other_profit = tuple(trade.calc_profit_ratio(self.get_current_price(trade.pair)) for trade in other_trades) trade_data['avg_other_profit'] = mean(total_other_profit) self.custom_trade_info[metadata['pair']] = trade_data mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb-lowerband'] = bollinger['lower'] dataframe['bb-middleband'] = bollinger['mid'] dataframe['volume-mean-slow'] = dataframe['volume'].rolling(window=24).mean() dataframe['rmi-slow'] = RMI(dataframe, length=20, mom=5) dataframe['rmi-fast'] = RMI(dataframe, length=9, mom=3) dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28) dataframe['ema-slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['max'] = dataframe['high'].rolling(12).max() dataframe['min'] = dataframe['low'].rolling(12).min() dataframe['upper'] = np.where(dataframe['max'] > dataframe['max'].shift(),1,0) dataframe['lower'] = np.where(dataframe['min'] < dataframe['min'].shift(),1,0) dataframe['up_trend'] = np.where(dataframe['upper'].rolling(3, min_periods=1).sum() != 0,1,0) dataframe['dn_trend'] = np.where(dataframe['lower'].rolling(3, min_periods=1).sum() != 0,1,0) informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe) informative['rocr'] = ta.ROCR(informative, timeperiod=168) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.buy_params trade_data = self.custom_trade_info[metadata['pair']] conditions = [] if trade_data['active_trade']: if (trade_data['peak_profit'] > 0): conditions.append(trade_data['current_profit'] > (trade_data['peak_profit'] * 0.8)) conditions.append(dataframe['rmi-slow'] >= 60) else: dataframe.loc[ ( (dataframe['rocr_1h'] > params['rocr-1h']) ) & (( (dataframe['lower'].shift() > 0) & (dataframe['bbdelta'] > (dataframe['close'] * params['bbdelta-close'])) & (dataframe['closedelta'] > (dataframe['close'] * params['closedelta-close'])) & (dataframe['tail'] < (dataframe['bbdelta'] * params['bbdelta-tail'])) & (dataframe['close'] < dataframe['lower'].shift()) & (dataframe['close'] <= dataframe['close'].shift()) ) | ( (dataframe['close'] < dataframe['ema-slow']) & (dataframe['close'] < params['close-bblower'] * dataframe['bb-lowerband']) & (dataframe['volume'] < (dataframe['volume-mean-slow'].shift(1) * params['volume'])) )), 'fake_buy' ] = 1 conditions.append(dataframe['fake_buy'].shift(1).eq(1)) conditions.append(dataframe['fake_buy'].eq(1)) conditions.append(dataframe['volume'].gt(0)) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: trade_data = self.custom_trade_info[metadata['pair']] conditions = [] if trade_data['active_trade']: conditions.append( (trade_data['current_profit'] < 0) & (trade_data['current_profit'] > self.stoploss) & (dataframe['dn_trend'] == 1) & (dataframe['rmi-fast'] < 50) & (dataframe['volume'].gt(0)) ) if trade_data['other_trades']: conditions.append(trade_data['avg_other_profit'] >= -0.005) else: conditions.append(dataframe['volume'].lt(0)) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe """ Custom methods """ def get_current_price(self, pair: str) -> float: """ side = "asks" if (self.config['ask_strategy']['price_side'] == "bid"): side = "bids" ob = self.dp.orderbook(pair, 1) current_price = ob[side][0][0] """ ticker = self.dp.ticker(pair) current_price = ticker['last'] return current_price """ Price protection on trade entry and timeouts, built-in Freqtrade functionality https://www.freqtrade.io/en/latest/strategy-advanced/ """ def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: ob = self.dp.orderbook(pair, 1) current_price = ob['bids'][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: ob = self.dp.orderbook(pair, 1) current_price = ob['asks'][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: ob = self.dp.orderbook(pair, 1) current_price = ob['asks'][0][0] if current_price > rate * 1.01: return False return True