# Zeus Strategy: First Generation of GodStra Strategy with maximum # AVG/MID profit in USDT # Author: @Mablue (Masoud Azizi) # github: https://github.com/mablue/ # IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta) # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus # --- Do not remove these libs --- from datetime import timedelta, datetime from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open, IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute) import pandas as pd import numpy as np from pandas import DataFrame from typing import Optional, Union, Tuple from scipy.special import binom import logging import configparser from technical import pivots_points # -------------------------------- # Add your lib to import here test git import ta import talib.abstract as talib import freqtrade.vendor.qtpylib.indicators as qtpylib import requests from datetime import timezone, timedelta logger = logging.getLogger(__name__) from tabulate import tabulate def pprint_df(dframe): print(tabulate(dframe, headers='keys', tablefmt='psql', showindex=False)) def normalize(df): df = (df - df.min()) / (df.max() - df.min()) return df class Zeus_11(IStrategy): levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20] # ROI table: minimal_roi = { "0": 0.564, "567": 0.273, "2814": 0.12, "7675": 0 } # Stoploss: stoploss = -1 # 0.256 # Custom stoploss use_custom_stoploss = False # Buy hypers timeframe = '5m' columns_logged = False # DCA config position_adjustment_enable = True plot_config = { "main_plot": { "min200": { "color": "#86c932" }, "max50": { "color": "white" }, "max200": { "color": "yellow" }, "bb_lowerband": { "color": "#da59a6"}, "bb_upperband": { "color": "#da59a6", } }, "subplots": { "Rsi": { "rsi": { "color": "pink" } }, "Percent": { "max_min": { "color": "#74effc" } } } } # 20 20 40 60 100 160 260 420 # 50 50 100 300 500 # fibo = [1, 1, 2, 3, 5, 8, 13, 21] # my fibo # 50 50 50 100 100 150 200 250 350 450 600 1050 fibo = [1, 1, 1, 2, 2, 3, 4, 5, 7, 9, 12, 16, 21] baisse = [1, 2, 3, 5, 7, 10, 14, 19, 26, 35, 47, 63, 84] # Ma suite 1 1 1 2 2 3 4 5 7 9 12 16 21 # Mise 50 50 50 100 100 150 200 250 350 450 600 800 1050 # Somme Mises 50 100 150 250 350 500 700 950 1300 1750 2350 3150 4200 # baisse 1 2 3 5 7 10 14 19 26 35 47 63 84 trades = list() max_profit_pairs = {} profit_b_no_change = BooleanParameter(default=True, space="sell") profit_b_quick_lost = BooleanParameter(default=True, space="sell") profit_b_sma5 = BooleanParameter(default=True, space="sell") profit_b_sma10 = BooleanParameter(default=True, space="sell") profit_b_sma20 = BooleanParameter(default=True, space="sell") profit_b_quick_gain = BooleanParameter(default=True, space="sell") profit_b_quick_gain_3 = BooleanParameter(default=True, space="sell") profit_b_old_sma10 = BooleanParameter(default=True, space="sell") profit_b_very_old_sma10 = BooleanParameter(default=True, space="sell") profit_b_over_rsi = BooleanParameter(default=True, space="sell") profit_b_short_loss = BooleanParameter(default=True, space="sell") sell_b_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') sell_b_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') sell_b_candels = IntParameter(0, 48, default=12, space='sell') sell_b_too_old_day = IntParameter(0, 10, default=300, space='sell') sell_b_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') sell_b_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell') sell_b_profit_percent12 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell') sell_b_RSI = IntParameter(70, 98, default=88, space='sell') sell_b_RSI2 = IntParameter(70, 98, default=88, space='sell') sell_b_RSI3 = IntParameter(70, 98, default=80, space='sell') sell_b_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') # sell_b_expected_profit = DecimalParameter(0, 0.01, decimals=3, default=0.01, space='sell') profit_h_no_change = BooleanParameter(default=True, space="sell") profit_h_quick_lost = BooleanParameter(default=True, space="sell") profit_h_sma5 = BooleanParameter(default=True, space="sell") profit_h_sma10 = BooleanParameter(default=True, space="sell") profit_h_sma20 = BooleanParameter(default=True, space="sell") profit_h_quick_gain = BooleanParameter(default=True, space="sell") profit_h_quick_gain_3 = BooleanParameter(default=True, space="sell") profit_h_old_sma10 = BooleanParameter(default=True, space="sell") profit_h_very_old_sma10 = BooleanParameter(default=True, space="sell") profit_h_over_rsi = BooleanParameter(default=True, space="sell") profit_h_short_loss = BooleanParameter(default=True, space="sell") sell_h_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') sell_h_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') sell_h_candels = IntParameter(0, 48, default=12, space='sell') sell_h_too_old_day = IntParameter(0, 10, default=300, space='sell') sell_h_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') sell_h_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell') sell_h_profit_percent12 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell') sell_h_RSI = IntParameter(70, 98, default=88, space='sell') sell_h_RSI2 = IntParameter(70, 98, default=88, space='sell') sell_h_RSI3 = IntParameter(70, 98, default=80, space='sell') sell_h_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell') protection_percent_buy_lost = IntParameter(1, 10, default=5, space='protection') # protection_nb_buy_lost = IntParameter(1, 2, default=2, space='protection') protection_fibo = IntParameter(1, 10, default=2, space='protection') # trailing stoploss hyperopt parameters # hard stoploss profit sell_allow_decrease = DecimalParameter(0.005, 0.02, default=0.2, decimals=2, space='sell', optimize=True, load=True) pairs = { pair: { "first_buy": 0, "last_max": 0, "trade_info": {}, "max_touch": 0.0, "last_sell": 0.0, "last_buy": 0.0, 'count_of_buys': 0, 'current_profit': 0, 'expected_profit': 0, "last_candle": {}, "last_trade": None, "last_count_of_buys": 0, 'base_stake_amount': 0, 'stop_buy': False } for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC", "BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"] } def min_max_scaling(self, series: pd.Series) -> pd.Series: """Normaliser les données en les ramenant entre 0 et 100.""" return 100 * (series - series.min()) / (series.max() - series.min()) def z_score_scaling(self, series: pd.Series) -> pd.Series: """Normaliser les données en utilisant Z-Score Scaling.""" return (series - series.mean()) / series.std() def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool: # count_buys = 0 # trade = self.getTrade(pair) # if trade: # filled_buys = trade.select_filled_orders('buy') # count_buys = len(filled_buys) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # last_candle_12 = dataframe.iloc[-13].squeeze() # allow_to_buy = True #(not self.stop_all) #& (not self.all_down) allow_to_buy = True # (rate <= float(limit)) | (entry_tag == 'force_entry') self.trades = list() dispo = round(self.wallets.get_available_stake_amount()) self.pairs[pair]['first_buy'] = rate self.pairs[pair]['last_buy'] = rate self.pairs[pair]['max_touch'] = last_candle['close'] self.pairs[pair]['last_candle'] = last_candle self.pairs[pair]['count_of_buys'] = 1 self.pairs[pair]['current_profit'] = 0 print( f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|" ) stake_amount = self.adjust_stake_amount(pair, last_candle) self.log_trade( last_candle=last_candle, date=current_time, action="START BUY", pair=pair, rate=rate, dispo=dispo, profit=0, trade_type=entry_tag, buys=1, stake=round(stake_amount, 2) ) return allow_to_buy 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, **kwargs, ) -> bool: # allow_to_sell = (minutes > 30) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() allow_to_sell = (last_candle['percent'] < 0) if allow_to_sell: self.pairs[pair]['last_count_of_buys'] = self.pairs[pair]['count_of_buys'] self.pairs[pair]['last_sell'] = rate self.pairs[pair]['last_trade'] = trade self.pairs[pair]['last_candle'] = last_candle self.trades = list() dispo= round(self.wallets.get_available_stake_amount()) # print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}") self.log_trade( last_candle=last_candle, date=current_time, action="Sell", pair=pair, trade_type=exit_reason, rate=last_candle['close'], dispo=dispo, profit=round(trade.calc_profit(rate, amount), 2) ) self.pairs[pair]['max_touch'] = 0 self.pairs[pair]['last_buy'] = 0 # else: # print('Cancel Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair) return (allow_to_sell) | (exit_reason == 'force_exit') def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() adjusted_stake_amount = self.adjust_stake_amount(pair, current_candle) # print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}") # Use default stake amount. return adjusted_stake_amount def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() before_last_candle = dataframe.iloc[-2].squeeze() count_of_buys = trade.nr_of_successful_entries max_touch_before = self.pairs[pair]['max_touch'] self.pairs[pair]['last_max'] = max(last_candle['haclose'], self.pairs[pair]['last_max']) last_lost = (last_candle['close'] - max_touch_before) / max_touch_before count_of_buys = trade.nr_of_successful_entries self.pairs[pair]['count_of_buys'] = count_of_buys self.pairs[pair]['current_profit'] = current_profit expected_profit = self.expectedProfit(pair, last_candle) if (last_candle['percent3'] < 0.0) & (current_profit > last_candle['min_max200'] / 3): self.trades = list() return 'min_max200_' + str(count_of_buys) if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit): self.trades = list() return 'profit_' + str(count_of_buys) if (current_profit >= expected_profit) & (last_candle['percent'] < 0.0) \ and ((last_candle['rsi'] >= 75) or before_last_candle['rsi'] >= 75): self.trades = list() return 'rsi_' + str(count_of_buys) def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1d') for pair in pairs] informative_pairs += [(pair, '1h') for pair in pairs] return informative_pairs def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None, last_candle=None): # Afficher les colonnes une seule fois if self.config.get('runmode') == 'hyperopt': return if self.columns_logged % 30 == 0: # print( # f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|" # ) print( f"| {'Date':<16} | {'Action':<10} | {'Pair':<10} | {'Trade Type':<18} | {'Rate':>12} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>5} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>12} | {'Buys':>5} | {'Stake':>10} |" ) print( f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|" ) self.columns_logged += 1 date = str(date)[:16] if date else "-" limit = None # if buys is not None: # limit = round(last_rate * (1 - self.fibo[buys] / 100), 4) rsi = '' rsi_pct = '' # if last_candle is not None: # if (not np.isnan(last_candle['rsi_1d'])) and (not np.isnan(last_candle['rsi_1h'])): # rsi = str(int(last_candle['rsi_1d'])) + " " + str(int(last_candle['rsi_1h'])) # if (not np.isnan(last_candle['rsi_pct_1d'])) and (not np.isnan(last_candle['rsi_pct_1h'])): # rsi_pct = str(int(10000 * last_candle['bb_mid_pct_1d'])) + " " + str( # int(last_candle['rsi_pct_1d'])) + " " + str(int(last_candle['rsi_pct_1h'])) # first_rate = self.percent_threshold.value # last_rate = self.threshold.value # action = self.color_line(action, action) sma5_1d = '' sma5_1h = '' sma5 = str(sma5_1d) + ' ' + str(sma5_1h) last_lost = round((last_candle['haclose'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3) max_touch = '' #round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1) pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) # round(100 * self.pairs[pair]['current_profit'], 1) if trade_type is not None: trade_type = trade_type \ + " " + str(round(100 * last_candle['sma5_pct_1d'], 0)) # + " " + str(round(last_candle['sma5_diff_1h'], 1)) print( f"| {date:<16} | {action:<10} | {pair:<10} | {trade_type or '-':<18} | {rate or '-':>12} | {dispo or '-':>6} | {profit or '-':>8} | {pct_max or '-':>5} | {max_touch or '-':>11} | {last_lost or '-':>12} | {round(self.pairs[pair]['last_max'], 2) or '-':>12} | {buys or '-':>5} | {stake or '-':>10} |" ) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Add all ta features pair = metadata['pair'] heikinashi = qtpylib.heikinashi(dataframe) dataframe['haopen'] = heikinashi['open'] dataframe['haclose'] = heikinashi['close'] dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose'] dataframe['close_02'] = dataframe['haclose'] * 1.02 dataframe['pct_change'] = dataframe['close'].pct_change(5) dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=200) dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12) dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50) dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200) dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50) dataframe['min_max50'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50'] dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200) dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200'] dataframe['max200_diff'] = (dataframe['max200'] - dataframe['close']) / dataframe['close'] dataframe['max50_diff'] = (dataframe['max50'] - dataframe['close']) / dataframe['close'] dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5) dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10) dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20) dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"] dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3) dataframe["percent5"] = (dataframe["close"] - dataframe["open"].shift(5)) / dataframe["open"].shift(5) dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12) dataframe["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24) dataframe["percent48"] = (dataframe["close"] - dataframe["open"].shift(48)) / dataframe["open"].shift(48) # print(metadata['pair']) dataframe['rsi'] = talib.RSI(dataframe['close'], length=14) # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) # Normalization dataframe['average_line'] = dataframe['close'].mean() dataframe['average_line_50'] = talib.MIDPOINT(dataframe['close'], timeperiod=50) dataframe['average_line_288'] = talib.MIDPOINT(dataframe['close'], timeperiod=288) # Sort the close prices to find the 4 lowest values sorted_close_prices = dataframe['close'].tail(576).sort_values() lowest_4 = sorted_close_prices.head(20) dataframe['lowest_4_average'] = lowest_4.mean() # Propagate this mean value across the entire dataframe # dataframe['lowest_4_average'] = dataframe['lowest_4_average'].iloc[0] # # Sort the close prices to find the 4 highest values sorted_close_prices = dataframe['close'].tail(288).sort_values(ascending=False) highest_4 = sorted_close_prices.head(20) # # Calculate the mean of the 4 highest values dataframe['highest_4_average'] = highest_4.mean() # # Propagate this mean value across the entire dataframe # dataframe['highest_4_average'] = dataframe['highest_4_average'].iloc[0] # dataframe['pct_average'] = (dataframe['highest_4_average'] - dataframe['close']) / dataframe['lowest_4_average'] # dataframe['highest_4_average_1'] = dataframe['highest_4_average'] * 0.99 # dataframe['highest_4_average_2'] = dataframe['highest_4_average'] * 0.98 # dataframe['highest_4_average_3'] = dataframe['highest_4_average'] * 0.97 # dataframe['highest_4_average_4'] = dataframe['highest_4_average'] * 0.96 # dataframe['highest_4_average_5'] = dataframe['highest_4_average'] * 0.95 # Compter les baisses consécutives dataframe['down'] = dataframe['hapercent'] <= 0.001 dataframe['up'] = dataframe['hapercent'] >= -0.001 dataframe['down_count'] = - dataframe['down'].astype(int) * ( dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1) dataframe['up_count'] = dataframe['up'].astype(int) * ( dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1) dataframe['down_tag'] = (dataframe['down_count'] < -7) dataframe['up_tag'] = (dataframe['up_count'] > 7) # Créer une colonne vide dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count') dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count') # Normaliser les données de 'close' # normalized_close = self.min_max_scaling(dataframe['close']) ################### INFORMATIVE 1h # informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h") # x_percent = 0.01 # n_hours = 6 # n_candles = n_hours * 60 # metadata["timeframe"] # Convertir en bougies # # informative["max_profit"] = dataframe["informative"].rolling(n_candles).max() # informative["profit_hit"] = dataframe["informative"] >= informative["close"] * (1 + x_percent) # # dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True) ################### INFORMATIVE 1d informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d") informative['sma5'] = talib.SMA(informative, timeperiod=5) informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5'] sorted_close_prices = informative['close'].tail(365).sort_values() lowest_4 = sorted_close_prices.head(4) informative['lowest_4'] = lowest_4.mean() sorted_close_prices = informative['close'].tail(365).sort_values(ascending=False) highest_4 = sorted_close_prices.head(4) informative['highest_4'] = highest_4.mean() last_14_days = informative.tail(14) # Récupérer le minimum et le maximum de la colonne 'close' des 14 derniers jours min_14_days = last_14_days['close'].min() max_14_days = last_14_days['close'].max() informative['lowest'] = min_14_days informative['highest'] = max_14_days informative['pct_min_max'] = (max_14_days - min_14_days) / min_14_days informative['mid_min_max'] = min_14_days + (max_14_days - min_14_days) / 2 informative['middle'] = informative['lowest_4'] + (informative['highest_4'] - informative['lowest_4']) / 2 informative['mid_min_max_0.98'] = informative['mid_min_max'] * 0.98 dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True) dataframe['count_buys'] = 0 dataframe['last_price'] = dataframe['close'] dataframe['first_price'] = dataframe['close'] dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2 dataframe['close01'] = dataframe.iloc[-1]['close'] * 1.01 dataframe['amount'] = 0 dataframe['limit'] = dataframe['close'] count_buys = 0 if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): self.getOpenTrades() for trade in self.trades: if trade.pair != pair: continue print(trade) filled_buys = trade.select_filled_orders('buy') dataframe['count_buys'] = len(filled_buys) count = 0 amount = 0 for buy in filled_buys: if count == 0: dataframe['first_price'] = buy.price dataframe['close01'] = buy.price * 1.01 # Order(id=2396, trade=1019, order_id=29870026652, side=buy, filled=0.00078, price=63921.01, # status=closed, date=2024-08-26 02:20:11) dataframe['last_price'] = buy.price print(buy) count = count + 1 amount += buy.price * buy.filled dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2 count_buys = count dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100) dataframe['amount'] = amount print(f"amount= {amount}") # # trades = Trade.get_trades([Trade.is_open is False]).all() # trades = Trade.get_trades_proxy(is_open=False, pair=metadata['pair']) # if trades: # trade = trades[-1] # print('closed trade pair is : ') # print(trade) # dataframe['expected_profit'] = (1 + self.expectedProfit(pair, dataframe.iloc[-1])) * dataframe[ # 'last_price'] # dataframe['lbp'] = dataframe['last_price'] # dataframe['lbp_3'] = dataframe['lbp'] * 0.97 # 3 # dataframe['lbp_6'] = dataframe['lbp'] * 0.94 # 6 # dataframe['lbp_9'] = dataframe['lbp'] * 0.90 # 10 # dataframe['lbp_12'] = dataframe['lbp'] * 0.85 # 15 # dataframe['lbp_20'] = dataframe['lbp'] * 0.8 # 20 # dataframe['fbp'] = trade.open_rate # # else: # # last_trade = self.get_trades(pair=pair).order_by('-close_date').first() # # filled_buys = last_trade.select_filled_orders('buy') # # print(last_trade) # # for buy in filled_buys: # # print(filled_buys) #dataframe['buy_level'] = dataframe['lowest_4_average'] * (1 - self.levels[count_buys] / 100) dataframe['buy_level'] = dataframe['max50'] * 0.99 #(1 - self.levels[count_buys] / 100) # ---------------------------------------------------------- # Calcul de la variation entre deux bougies successives dataframe['price_change'] = dataframe['close'].diff() # Marquer les bougies en baisse dataframe['is_down'] = dataframe['price_change'] < 0 # Identifier les blocs consécutifs de baisses # dataframe['drop_id'] = (dataframe['is_down'] != dataframe['is_down'].shift(1)).cumsum() dataframe['drop_id'] = np.where(dataframe['is_down'], (dataframe['is_down'] != dataframe['is_down'].shift(12)).cumsum(), np.nan) # Identifier uniquement les blocs de baisse dataframe['drop_id'] = dataframe['drop_id'].where(dataframe['is_down']) # # Grouper par les chutes détectées # drop_info = dataframe.groupby('drop_id').agg( # start=('close', 'first'), # Prix au début de la chute # end=('close', 'last'), # Prix à la fin de la chute # start_index=('close', 'idxmin'), # Début de la chute (index) # end_index=('close', 'idxmax'), # Fin de la chute (index) # ) # # # Calcul de l'ampleur de la chute en % # drop_info['drop_amplitude_pct'] = ((drop_info['end'] - drop_info['start']) / drop_info['start']) * 100 # # Filtrer les chutes avec une amplitude supérieure à 3% # drop_info = drop_info[drop_info['drop_amplitude_pct'] < -3] # ************** # Identifier le prix de début et de fin de chaque chute drop_stats = dataframe.groupby('drop_id').agg( start_price=('close', 'first'), # Prix au début de la chute end_price=('close', 'last'), # Prix à la fin de la chute ) return dataframe def getOpenTrades(self): # if len(self.trades) == 0: print('search open trades') self.trades = Trade.get_open_trades() return self.trades def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair = metadata['pair'] #expected_profit = self.expectedProfit(pair, dataframe.iloc[-1]) #last_candle = dataframe.iloc[-1].squeeze() # print("---------------" + pair + "----------------") # print('adjust stake amount ' + str(self.adjust_stake_amount(pair, dataframe.iloc[-1]))) # # print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1]))) # print('calcul expected_profit ' + str(expected_profit)) # buy_level = dataframe['buy_level'] # self.get_buy_level(pair, dataframe) dataframe.loc[ ( (dataframe['down_count'].shift(1) < - 6) & (dataframe['down_count'] == 0) & (dataframe['down_pct'].shift(1) <= -0.5) ), ['enter_long', 'enter_tag']] = (1, 'buy_hapercent') dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): # ne rien faire si ordre deja en cours if trade.has_open_orders: return None if (self.wallets.get_available_stake_amount() < 50): # or trade.stake_amount >= max_stake: return 0 dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # prépare les données count_of_buys = trade.nr_of_successful_entries current_time = current_time.astimezone(timezone.utc) open_date = trade.open_date.astimezone(timezone.utc) dispo = round(self.wallets.get_available_stake_amount()) hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0 if (len(dataframe) < 1): return None pair = trade.pair if pair not in ('BTC/USDC', 'XRP/USDC', 'BTC/USDT', 'XRP/USDT'): return None max_buys = 20 filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) if count_of_buys >= max_buys: return None # if 'buy' in last_candle: # condition = (last_candle['buy'] == 1) # else: # condition = False # self.protection_nb_buy_lost.value limit = last_candle['limit'] stake_amount = self.config['stake_amount'] + 50 * self.fibo[count_of_buys] current_time_utc = current_time.astimezone(timezone.utc) open_date = trade.open_date.astimezone(timezone.utc) days_since_open = (current_time_utc - open_date).days pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 3) # if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1): if ((last_candle['enter_long'] == 1) or last_candle['percent48'] < - 0.03) \ and (pct_max < -0.012 - (count_of_buys * 0.001)): try: # This then calculates current safety order size # stake_amount = stake_amount * pow(1.5, count_of_buys) # print( # f"Adjust {current_time} price={trade.pair} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}") self.log_trade( last_candle=last_candle, date=current_time, action="Loss -", dispo=dispo, pair=trade.pair, rate=current_rate, trade_type=last_candle['enter_tag'], profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2), buys=trade.nr_of_successful_entries + 1, stake=round(stake_amount, 2) ) self.pairs[trade.pair]['last_buy'] = current_rate self.pairs[trade.pair]['max_touch'] = last_candle['close'] self.pairs[trade.pair]['last_candle'] = last_candle return stake_amount except Exception as exception: print(exception) return None return None def adjust_stake_amount(self, pair: str, dataframe: DataFrame): # Calculer le minimum des 14 derniers jours current_price = dataframe['close'] # trade = self.getTrade(pair) # if trade: # current_price = trade.open_rate base_stake_amount = self.config['stake_amount'] #.get('stake_amount', 50) # Montant de base configuré # Calculer le max des 14 derniers jours min_14_days_4 = dataframe['lowest_4_1d'] max_14_days_4 = dataframe['highest_4_1d'] percent_4 = 1 - (current_price - min_14_days_4) / (max_14_days_4 - min_14_days_4) factor_4 = 1 / ((current_price - min_14_days_4) / (max_14_days_4 - min_14_days_4)) max_min_4 = max_14_days_4 / min_14_days_4 # min_14_days = dataframe['lowest_1d'] # max_14_days = dataframe['highest_1d'] # percent = 1 - (current_price - min_14_days) / (max_14_days - min_14_days) # factor = 1 / ((current_price - min_14_days) / (max_14_days - min_14_days)) # max_min = max_14_days / min_14_days # Stack amount ajusté price=2473.47 min_max=0.15058074985054215 percent=0.8379141364642171 amount=20.0 adjusted_stake_amount = max(base_stake_amount, min(100, base_stake_amount * percent_4)) # if pair in ('BTC/USDT', 'ETH/USDT'): # if percent_4 > 0.5: # adjusted_stake_amount = 300 # adjusted_stake_amount_2 = max(base_stake_amount / 2.5, min(75, base_stake_amount * percent)) # print( # f"Stack amount ajusté price={current_price} max_min={max_min_4:.4f} min_14={min_14_days_4:.4f} max_14={max_14_days_4:.4f} factor={factor_4:.4f} percent={percent_4:.4f} amount={adjusted_stake_amount:.4f}") # print(f"Stack amount ajusté price={current_price} max_min={max_min:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} factor={factor:.4f} percent={percent:.4f} amount={adjusted_stake_amount_2:.4f}") return adjusted_stake_amount # def adjust_exit_price(self, dataframe: DataFrame): # # Calculer le max des 14 derniers jours # min_14_days = dataframe['lowest_1d'] # max_14_days = dataframe['highest_1d'] # entry_price = dataframe['fbp'] # current_price = dataframe['close'] # percent = 0.5 * (max_14_days - min_14_days) / min_14_days # exit_price = (1 + percent) * entry_price # # print(f"Exit price ajusté price={current_price:.4f} max_14={max_14_days:.4f} exit_price={exit_price:.4f}") # # return exit_price # def adjust_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, # current_rate: float, current_profit: float, **kwargs) -> float: # dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # # print(dataframe) # last_candle = dataframe.iloc[-1].squeeze() # # # Utiliser l'ATR pour ajuster le stoploss # atr_stoploss = current_rate - (last_candle['atr'] * 1.5) # Stoploss à 1.5x l'ATR # # # Retourner le stoploss dynamique en pourcentage du prix actuel # return (atr_stoploss / current_rate) - 1 def expectedProfit(self, pair: str, last_candle): current_price = last_candle['last_price'] # dataframe['close'] # trade = self.getTrade(pair) # if trade: # current_price = trade.open_rate # Calculer le max des 14 derniers jours min_14_days = last_candle['lowest_1d'] max_14_days = last_candle['highest_1d'] percent = (max_14_days - current_price) / (min_14_days) min_max = last_candle['pct_min_max_1d'] # (max_14_days - min_14_days) / min_14_days expected_profit = min(0.1, max(0.01, last_candle['min_max200'] * 0.5 + self.pairs[pair]['count_of_buys'] * 0.0005)) return expected_profit # def adjust_exit_price(self, dataframe: DataFrame): # # Calculer le max des 14 derniers jours # min_14_days = dataframe['lowest_1d'] # max_14_days = dataframe['highest_1d'] # entry_price = dataframe['fbp'] # current_price = dataframe['close'] # percent = 0.5 * (max_14_days - min_14_days) / min_14_days # exit_price = (1 + percent) * entry_price # # print(f"Exit price ajusté price={current_price} max_14={max_14_days} exit_price={exit_price}") # # return exit_price # def adjust_entry_price(self, dataframe: DataFrame): # # Calculer le max des 14 derniers jours # min_14_days = dataframe['lowest_1d'] # max_14_days = dataframe['highest_1d'] # current_price = dataframe['close'] # percent = 0.5 * (max_14_days - min_14_days) / min_14_days # entry_price = (1 + percent) * entry_price # # print(f"Entry price ajusté price={current_price} max_14={max_14_days} exit_price={entry_price}") # # return entry_price # def adjust_stake_amount(self, dataframe: DataFrame): # # Calculer le minimum des 14 derniers jours # middle = dataframe['middle_1d'] # # # Récupérer la dernière cotation actuelle (peut être le dernier point de la série) # current_price = dataframe['close'] # # # Calculer l'écart entre la cotation actuelle et le minimum des 14 derniers jours # difference = middle - current_price # # Ajuster la stake_amount en fonction de l'écart # # Par exemple, augmenter la stake_amount proportionnellement à l'écart # base_stake_amount = self.config.get('stake_amount', 100) # Montant de base configuré # # multiplier = 1 - (difference / current_price) # Exemple de logique d'ajustement # # adjusted_stake_amount = max(base_stake_amount / 2.5, base_stake_amount * multiplier) # # # difference = 346.07000000000016 # # price = 2641.75 # # min_14 = 2295.68 # # amount = 56.5500141951358 # # print(f"Stack amount ajusté difference={difference} price={current_price} middle={middle} multiplier={multiplier} amount={adjusted_stake_amount}") # # return adjusted_stake_amount def calculateUpDownPct(self, dataframe, key): down_pct_values = np.full(len(dataframe), np.nan) # Remplir la colonne avec les bons calculs for i in range(len(dataframe)): shift_value = abs(int(dataframe[key].iloc[i])) # Récupérer le shift actuel if i - shift_value > 1: # Vérifier que le shift ne dépasse pas l'index down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \ dataframe['close'].iloc[i - shift_value] return down_pct_values