# source: https://raw.githubusercontent.com/obseries/freqtrade-cqs/e4a94f6e69b862fe1728b86047d8c082c36b6ffc/CQSStrategy.py import logging import json from datetime import datetime from typing import Optional import numpy as np # noqa import pandas as pd # noqa import requests from pandas import DataFrame import os.path from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) class github_obseries_freqtrade_cqs__CQSStrategy__20220805_150054(IStrategy): logger = logging.getLogger(__name__) cqs_multiplier_loop = 8 cqs_current_loop_number = 8 cqs_json_file = '' cqs_trades = [] # 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 # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "10080": 0.005, # dopo 7 giorni va chiuso se in guadagno "4320": 0.15, # dopo 3 giorni "0": 100 } position_adjustment_enable = True max_entry_position_adjustment = 1 # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.20 use_custom_stoploss = True # Trailing stoploss # trailing_stop = True # trailing_stop_positive = 0.02 # trailing_stop_positive_offset = 0.0 # trailing_only_offset_is_reached = True # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = '1m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 1 # Optional order type mapping. order_types = { # 'entry': 'limit', 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } entry_pricing = { 'price_side': 'other' } # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } def version(self) -> str: """ Returns version of the strategy. """ return "0.1" def bot_start(self, **kwargs) -> None: """ Called only once after bot instantiation. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ self.cqs_json_file = str(self.config['user_data_dir']) + '/database/cqs.json' if self.config['runmode'].value in ('live', 'dry_run'): # Assign this to the class by using self.* # can then be used by populate_* methods # cerca se esiste il file cqs.json file_exists = os.path.exists(self.cqs_json_file) if not file_exists: # se non esiste: inizializzo self.save_cqs_trade() # se esiste parsifico il file if file_exists: with open(self.cqs_json_file) as json_file: self.cqs_trades = json.load(json_file) def bot_loop_start(self, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ if self.config['runmode'].value in ('live', 'dry_run'): self.logger.debug("called bot_loop_start") # call remote service every if self.cqs_current_loop_number % self.cqs_multiplier_loop == 0: self.logger.debug("Call CQS Service %s", self.cqs_current_loop_number) self.cqs_current_loop_number = 0 remote_data = requests.get('https://api.cryptoqualitysignals.com/v1/getSignal/?api_key=FREE&interval=15') # remote_data = requests.get( # 'https://44dbadcb-bff3-4063-9910-6609c617b9d8.mock.pstmn.io/v1/getSignal/?api_key=FREE&interval=15') data = json.loads(remote_data.text) self.logger.info(f"Called service message: '{data['message']}' count: '{data['count']}'") for signal in data['signals']: self.logger.info(f"id '{signal['id']}' coin: '{signal['coin']}' currency: '{signal['currency']}' ") # normalizzo la currency su USDT currency = signal['currency'] if currency == 'BUSD' or currency == 'USD': currency = 'USDT' pair = signal['coin'] + '/' + currency # verifico che sia nella lista delle pair trattate has_to_add = False for available_pair in self.dp.available_pairs: if pair == available_pair[0]: # e' nella lista dei pair trattati has_to_add = True # ora verifico di non avere ancora questo trade in lista for trade in self.cqs_trades: if trade['id'] == signal['id']: has_to_add = False if has_to_add: signal['pair'] = pair self.cqs_trades.append(signal) self.save_cqs_trade() self.cqs_current_loop_number = self.cqs_current_loop_number + 1 ## TODO cancellare i trade che dopo 15 minuti non sono stati aperti 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"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ self.logger.debug("called populate_indicators %s", metadata) cqstrade = self.get_cqs_trade_by_pair(metadata['pair']) # e' vuoto se: cqstrade == {} if cqstrade == {}: return dataframe dataframe['buy_start'] = float(cqstrade['buy_start']) dataframe['buy_end'] = float(cqstrade['buy_end']) dataframe['target1'] = float(cqstrade['target1']) dataframe['target2'] = float(cqstrade['target2']) dataframe['target3'] = float(cqstrade['target3']) dataframe['stop_loss'] = float(cqstrade['stop_loss']) # Prendi esempi da https://raw.githubusercontent.com/freqtrade/freqtrade/develop/freqtrade/templates/sample_strategy.py # 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] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ cqstrade = self.get_cqs_trade_by_pair(metadata['pair']) # e' vuoto se: cqstrade == {} if cqstrade == {}: # Deactivated enter long signal to allow the strategy to work correctly dataframe.loc[:, 'enter_long'] = 0 if cqstrade != {}: dataframe.loc[ ( (dataframe['best_ask'] <= dataframe['buy_end']) & (dataframe['best_ask'] >= dataframe['buy_start']) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ cqstrade = self.get_cqs_trade_by_pair(metadata['pair']) # e' vuoto se: cqstrade == {} if cqstrade == {}: # Deactivated exit long signal to allow the strategy to work correctly dataframe.loc[:, 'exit_long'] = 0 if cqstrade != {}: dataframe.loc[ ( dataframe['best_bid'] >= dataframe['target3'] ), 'exit_long'] = 1 return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ Custom stoploss logic, returning the new distance relative to current_rate (as ratio). e.g. returning -0.05 would create a stoploss 5% below current_rate. The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns the initial stoploss value Only called when use_custom_stoploss is set to True. :param pair: Pair that's currently analyzed :param trade: trade object. :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param current_profit: Current profit (as ratio), calculated based on current_rate. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return float: New stoploss value, relative to the current rate """ result = 1 cqstrade = self.get_cqs_trade_by_pair(pair) # e' vuoto se: cqstrade == {} if cqstrade == {}: return result if trade: relative_sl = None if self.dp: # so we need to get analyzed_dataframe from dp dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) # only use .iat[-1] in callback methods, never in "populate_*" methods. # see: https://www.freqtrade.io/en/latest/strategy-customization/#common-mistakes-when-developing-strategies last_candle = dataframe.iloc[-1].squeeze() # imposto lo stop loss ufficiale relative_sl = last_candle['stop_loss'] # se ho superato il target1, imposto al break even if current_rate > last_candle['target1'] and current_profit > 0.04: relative_sl = last_candle['buy_end'] * 1.01 if current_rate > last_candle['target2']: relative_sl = last_candle['target1'] * 1.01 if relative_sl is not None: # #print("custom_stoploss().relative_sl: {}".format(relative_sl)) # calculate new_stoploss relative to current_rate new_stoploss = (current_rate - relative_sl) / current_rate # turn into relative negative offset required by `custom_stoploss` return implementation result = - new_stoploss return result def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, **kwargs) -> Optional[float]: """ Custom trade adjustment logic, returning the stake amount that a trade should be increased. This means extra buy orders with additional fees. Only called when `position_adjustment_enable` is set to True. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns None :param trade: trade object. :param current_time: datetime object, containing the current datetime :param current_rate: Current buy rate. :param current_profit: Current profit (as ratio), calculated based on current_rate. :param min_stake: Minimal stake size allowed by exchange. :param max_stake: Balance available for trading. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return float: Stake amount to adjust your trade """ cqstrade = self.get_cqs_trade_by_pair(trade.pair) if cqstrade == {}: return None filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = trade.nr_of_successful_entries buy_start = float(cqstrade['buy_start']) buy_end = float(cqstrade['buy_end']) # massimo entrata dividendo in tre parti third_entry = ((buy_end-buy_start)/3) + buy_start if buy_start < current_rate < third_entry and count_of_entries == 1: stake_amount = filled_entries[0].cost / 2 if stake_amount < min_stake: stake_amount = min_stake return stake_amount # TODO inserire le exit parziali impostando uno stake amount negativo al raggiungimento dei target # ATTENZIONE # al min_stake, si potrebbe fare che di default imposto uno stake che รจ almeno 3 volte il minimo utilizzando # il metodo custom_stake_amount return None 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: """ Called right before placing a regular exit order. Timing for this function is critical, so avoid doing heavy computations or network requests in this method. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns True (always confirming). :param pair: Pair for trade that's about to be exited. :param trade: trade object. :param order_type: Order type (as configured in order_types). usually limit or market. :param amount: Amount in base currency. :param rate: Rate that's going to be used when using limit orders or current rate for market orders. :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param exit_reason: Exit reason. Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', 'exit_signal', 'force_exit', 'emergency_exit'] :param current_time: datetime object, containing the current datetime :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return bool: When True, then the exit-order is placed on the exchange. False aborts the process """ # TODO verificare che non venga chiamato con le partial exit self.logger.warning("NON DEVE ESSERE CHIAMATO PER LE EXIT PARZIALI: reason: %s", exit_reason) # rimuovere in cqs i trade conclusi self.remove_cqs_trade_by_pair(pair) return True def save_cqs_trade(self): with open(self.cqs_json_file, 'w') as output_file: json.dump(self.cqs_trades, output_file, indent=4) def get_cqs_trade_by_pair(self, pair: str) -> dict: trade = {} for cqstrade in self.cqs_trades: if cqstrade['pair'] == pair: trade = cqstrade if trade == {}: self.logger.warning("ATTENZIONE: pair non trovato nel json %s", pair) return trade def remove_cqs_trade_by_pair(self, pair: str): trade = {} for cqstrade in self.cqs_trades: if cqstrade['pair'] == pair: self.cqs_trades.remove(cqstrade) self.save_cqs_trade()