diff --git a/Zeus_8_3_2_B_4_2.py b/Zeus_8_3_2_B_4_2.py index 7c6881d..b052325 100644 --- a/Zeus_8_3_2_B_4_2.py +++ b/Zeus_8_3_2_B_4_2.py @@ -130,81 +130,10 @@ class Zeus_8_3_2_B_4_2(IStrategy): 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) - # pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', optimize=False, load=True) - # # profit threshold 1, trigger point, SL_1 is used - # pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=True, load=True) - # pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=True, load=True) - # - # # profit threshold 2, SL_2 is used - # pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=True, load=True) - # pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=True, load=True) - 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()) @@ -269,7 +198,7 @@ class Zeus_8_3_2_B_4_2(IStrategy): if allow_to_sell: self.trades = list() - self.pairs[pair]['last_count_of_buys'] = self.pairs[pair]['count_of_buys'] + self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries #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 @@ -329,7 +258,8 @@ class Zeus_8_3_2_B_4_2(IStrategy): 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): + and ((last_candle['rsi'] >= 75) or before_last_candle['rsi'] >= 75)\ + and (count_of_buys < 5): self.trades = list() return 'rsi_' + str(count_of_buys) @@ -350,6 +280,32 @@ class Zeus_8_3_2_B_4_2(IStrategy): return informative_pairs + from typing import List + + def multi_step_interpolate(self, pct: float, thresholds: List[float], factors: List[float]) -> float: + if pct <= thresholds[0]: + return factors[0] + if pct >= thresholds[-1]: + return factors[-1] + + for i in range(1, len(thresholds)): + if pct <= thresholds[i]: + # interpolation linéaire entre thresholds[i-1] et thresholds[i] + return factors[i - 1] + (pct - thresholds[i - 1]) * (factors[i] - factors[i - 1]) / ( + thresholds[i] - thresholds[i - 1]) + + # Juste au cas où (devrait jamais arriver) + return factors[-1] + + def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30, + start_factor: float = 1.0, end_factor: float = 2.0) -> float: + if pct <= start_pct: + return start_factor + if pct >= end_pct: + return end_factor + # interpolation linéaire + return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct) + 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 @@ -763,12 +719,14 @@ class Zeus_8_3_2_B_4_2(IStrategy): max_stake: float, **kwargs): # ne rien faire si ordre deja en cours if trade.has_open_orders: + print("skip 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() + last_candle_3 = dataframe.iloc[-4].squeeze() # prépare les données current_time = current_time.astimezone(timezone.utc) open_date = trade.open_date.astimezone(timezone.utc) @@ -776,9 +734,12 @@ class Zeus_8_3_2_B_4_2(IStrategy): hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0 if (len(dataframe) < 1): + print("skip dataframe") + return None pair = trade.pair - if pair not in ('BTC/USDT', 'DOGE/USDT', 'ETH/USDT'): + if pair not in ('BTC/USDT', 'BTC/USDC'): + print(f"skip pair {pair}") return None count_of_buys = trade.nr_of_successful_entries @@ -792,19 +753,37 @@ class Zeus_8_3_2_B_4_2(IStrategy): 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_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) - pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 4) + pct_first = 0 + if self.pairs[pair]['first_buy']: + pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) pct = 0.012 - stake_amount = min(self.wallets.get_available_stake_amount(), self.adjust_stake_amount(pair, last_candle) - 20 * pct_first / pct) #min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys]) + if count_of_buys == 1: + pct_max = current_profit + else: + if self.pairs[trade.pair]['last_buy']: + pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 4) + else: + pct_max = - pct + + lim = - pct - (count_of_buys * 0.001) + # print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_max={pct_max:.3f} lim={lim:.3f} rsi_diff_1f={last_candle['rsi_diff_1h']}") # if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1): limit_buy = 20 if (count_of_buys < limit_buy) \ - and ((last_candle['enter_long'] == 1) or last_candle['percent48'] < - 0.03) \ - and (last_candle['enter_long'] == 1) \ - and (pct_max < - pct - (count_of_buys * 0.001)): + and ((last_candle['enter_long'] == 1) + or (last_candle['percent48'] < - 0.03) + or ((last_candle['min50'] == last_candle_3['min50']) and (last_candle['low'] <= last_candle['min50'])) + ) \ + and (last_candle['rsi_diff_1h'] >= -5) \ + and ((pct_max < lim)): try: + # print(self.adjust_stake_amount(pair, last_candle)) + # print(pct_first) + # print(pct) + stake_amount = min(self.wallets.get_available_stake_amount(), + self.adjust_stake_amount(pair, last_candle) - 10 * pct_first / pct) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys]) trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48' self.log_trade( @@ -851,15 +830,39 @@ class Zeus_8_3_2_B_4_2(IStrategy): # 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)) + first_price = self.pairs[pair]['first_buy'] + + last_max = current_price + if self.pairs[pair]['last_max'] > 0: + last_max = self.pairs[pair]['last_max'] + last_count = self.pairs[pair]['last_count_of_buys'] + # factor = 1 + # + # if last_max > 0: + # pct = 100 * (last_max - first_price) / last_max + # + # if pct >= 20: + # factor = 2 + # else: + # if pct >= 15: + # factor = 1.5 + pct = 5 + if last_max > 0: + pct = 100 * (last_max - first_price) / last_max + thresholds = [2, 10, 20, 30] + factors = [0.5, 1.0, 1.5, 2.0] + + factor = self.multi_step_interpolate(pct, thresholds, factors) + # factor = self.interpolate_factor(pct, start_pct=5, end_pct=50, start_factor=0.8, end_factor=3.0) + + adjusted_stake_amount = base_stake_amount * factor #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} factor={factor} 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