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 from freqtrade.persistence import Trade from technical.indicators import RMI from statistics import mean from cachetools import TTLCache """ TODO: """ class Schism_v1_4(IStrategy): """ Strategy Configuration Items """ timeframe = '5m' inf_timeframe = '1h' buy_params = { 'inf-rsi': 58, 'mp': 26, 'rmi-fast': 11, 'rmi-slow': 55, 'xinf-stake-rmi': 25, 'xtf-fiat-rsi': 64, 'xtf-stake-rsi': 90 } sell_params = { 'rmi-high': 50, 'rmi-low': 10 } buy_params_FOO = {} sell_params_FOO = {} minimal_roi = { "0": 0.05, "10": 0.025, "20": 0.015, "30": 0.01, "720": 0.005, "1440": 0 } stoploss = -0.30 use_sell_signal = False sell_profit_only = False ignore_roi_if_buy_signal = True startup_candle_count: int = 72 custom_trade_info = {} custom_fiat = "USD" custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300) # 5 minutes """ Informative Pair Definitions """ def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_timeframe) for pair in pairs] 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: self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair']) dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5) dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4) dataframe['roc'] = ta.ROC(dataframe, timeperiod=6) dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6) 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) 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}" 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) 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}"] = RMI(stake_fiat_inf_tf, length=21, mom=5) informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe) informative['rsi'] = ta.RSI(informative, timeperiod=14) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True) return dataframe """ Buy Trigger Signals """ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.get_pair_params(metadata['pair'], 'buy') trade_data = self.custom_trade_info[metadata['pair']] conditions = [] if trade_data['active_trade']: profit_factor = (1 - (dataframe['rmi-slow'].iloc[-1] / 400)) rmi_grow = self.linear_growth(30, 70, 180, 720, trade_data['open_minutes']) conditions.append(dataframe['rmi-up-trend'] == 1) conditions.append(trade_data['current_profit'] > (trade_data['peak_profit'] * profit_factor)) conditions.append(dataframe['rmi-slow'] >= rmi_grow) else: conditions.append( (dataframe[f"rsi_{self.inf_timeframe}"] <= params['inf-rsi']) & (dataframe['rmi-up-trend'] == 1) & (dataframe['rmi-slow'] <= params['rmi-slow']) & (dataframe['rmi-fast'] >= params['rmi-fast']) & (dataframe['mp'] >= params['mp']) ) if self.config['stake_currency'] in ('BTC', 'ETH'): conditions.append( (dataframe[f"{self.config['stake_currency']}_rsi"] < params['xtf-stake-rsi']) | (dataframe[f"{self.custom_fiat}_rsi"] > params['xtf-fiat-rsi']) ) conditions.append(dataframe[f"{self.config['stake_currency']}_rmi_{self.inf_timeframe}"] < params['xinf-stake-rmi']) conditions.append(dataframe['volume'].gt(0)) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe """ Sell Trigger Signals: In this strategy all sells for profit happen according to ROI This sell signal is designed only as a "dynamic stoploss" """ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.get_pair_params(metadata['pair'], 'sell') trade_data = self.custom_trade_info[metadata['pair']] conditions = [] if trade_data['active_trade']: loss_cutoff = self.linear_growth(-0.03, 0, 0, 300, trade_data['open_minutes']) conditions.append( (trade_data['current_profit'] < loss_cutoff) & (trade_data['current_profit'] > self.stoploss) & (dataframe['rmi-dn-trend'] == 1) & (dataframe['volume'].gt(0)) ) if trade_data['peak_profit'] > 0: conditions.append(qtpylib.crossed_below(dataframe['rmi-slow'], params['rmi-high'])) else: conditions.append(qtpylib.crossed_below(dataframe['rmi-slow'], params['rmi-low'])) if trade_data['other_trades']: if trade_data['free_slots'] > 0: """ Less free slots, more willing to sell 1 / free_slots * x = 1 slot = 1/1 * -0.04 = -0.04 -> only allow sells if avg_other_proift above -0.04 4 slot = 1/4 * -0.04 = -0.01 -> only allow sells is avg_other_profit above -0.01 """ max_market_down = -0.04 hold_pct = (1/trade_data['free_slots']) * max_market_down conditions.append(trade_data['avg_other_profit'] >= hold_pct) else: conditions.append(trade_data['biggest_loser'] == True) 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 populate_trades(self, pair: str) -> 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 self.custom_trade_info['meta'] = {} 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 # floor 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()) 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: 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 """ Simple linear growth function: Starts at X and grows to Y after A minutes (starting after B miniutes) f(t) = X + (rate * t), where rate = (Y - X) / (B - A) """ def linear_growth(self, start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float: time = max(0, trade_time - start_time) rate = (end - start) / (end_time - start_time) return min(end, start + (rate * time)) """ Allow for buy/sell override parameters per pair. Testing, might remove. TODO: If good: make this more robust so you never have to edit this method. Consider: per-pair ROI if it seems worthwhile? """ def get_pair_params(self, pair: str, side: str) -> Dict: buy_params = self.buy_params sell_params = self.sell_params if pair in ('ABC/XYZ', 'DEF/XYZ'): buy_params = self.buy_params_GROUP1 sell_params = self.sell_params_GROUP1 elif pair in ('QRD/WTF'): buy_params = self.buy_params_QRD sell_params = self.sell_params_QRD if side == 'sell': return sell_params return buy_params """ 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: 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 """ Sub-strategy overrides Anything not explicity defined here will follow the settings in the base strategy """ class Schism4_BTC(Schism_v1_4): 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 } use_sell_signal = False class Schism4_ETH(Schism_v1_4): 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 } use_sell_signal = False