diff --git a/Zeus_11.json b/Zeus_11.json index d788d0b..50102f1 100644 --- a/Zeus_11.json +++ b/Zeus_11.json @@ -16,63 +16,7 @@ "max_open_trades": { "max_open_trades": 3 }, - "buy": { - "buy_rsi_1d": 45, - "buy_rsi_1h": 49, - "buy_sum_rsi_1d": 17.9, - "buy_sum_rsi_1h": 11.5 - }, - "sell": { - "pHSL": -0.99, - "pPF_1": 0.022, - "pSL_1": 0.015, - "pPF_2": 0.05, - "pSL_2": 0.03, - "profit_b_no_change": false, - "profit_b_old_sma10": false, - "profit_b_over_rsi": true, - "profit_b_quick_gain": false, - "profit_b_quick_gain_3": true, - "profit_b_quick_lost": true, - "profit_b_short_loss": false, - "profit_b_sma10": true, - "profit_b_sma20": false, - "profit_b_sma5": true, - "profit_b_very_old_sma10": false, - "profit_h_no_change": false, - "profit_h_old_sma10": false, - "profit_h_over_rsi": true, - "profit_h_quick_gain": true, - "profit_h_quick_gain_3": false, - "profit_h_quick_lost": true, - "profit_h_short_loss": true, - "profit_h_sma10": true, - "profit_h_sma20": true, - "profit_h_sma5": true, - "profit_h_very_old_sma10": false, - "sell_b_RSI": 87, - "sell_b_RSI2": 82, - "sell_b_RSI2_percent": 0.007, - "sell_b_RSI3": 75, - "sell_b_candels": 23, - "sell_b_percent": 0.014, - "sell_b_percent3": 0.018, - "sell_b_profit_no_change": 0.003, - "sell_b_profit_percent10": 0.0011, - "sell_b_too_old_day": 10, - "sell_b_too_old_percent": 0.013, - "sell_h_RSI": 82, - "sell_h_RSI2": 75, - "sell_h_RSI2_percent": 0.011, - "sell_h_RSI3": 97, - "sell_h_candels": 6, - "sell_h_percent": 0.009, - "sell_h_percent3": 0.016, - "sell_h_profit_no_change": 0.017, - "sell_h_profit_percent10": 0.0014, - "sell_h_too_old_day": 300, - "sell_h_too_old_percent": 0.004 - }, + "protection": { "protection_fibo": 9, "protection_percent_buy_lost": 3 diff --git a/Zeus_11.py b/Zeus_11.py index c315124..d74298d 100644 --- a/Zeus_11.py +++ b/Zeus_11.py @@ -28,6 +28,11 @@ import talib.abstract as talib import freqtrade.vendor.qtpylib.indicators as qtpylib import requests from datetime import timezone, timedelta +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import PolynomialFeatures +from sklearn.pipeline import make_pipeline logger = logging.getLogger(__name__) @@ -47,10 +52,7 @@ class Zeus_11(IStrategy): # ROI table: minimal_roi = { - "0": 0.564, - "567": 0.273, - "2814": 0.12, - "7675": 0 + "0": 10 } # Stoploss: @@ -112,72 +114,6 @@ class Zeus_11(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) - pairs = { pair: { "first_buy": 0, @@ -199,13 +135,13 @@ class Zeus_11(IStrategy): "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 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: @@ -232,7 +168,7 @@ class Zeus_11(IStrategy): self.pairs[pair]['current_profit'] = 0 print( - f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|" + f"|{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|" ) stake_amount = self.adjust_stake_amount(pair, last_candle) @@ -305,7 +241,7 @@ class Zeus_11(IStrategy): last_candle = dataframe.iloc[-1].squeeze() before_last_candle = dataframe.iloc[-2].squeeze() - count_of_buys = trade.nr_of_successful_entries + #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']) @@ -317,16 +253,18 @@ class Zeus_11(IStrategy): 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) + if (last_candle['rsi_1d'] > 50) & (last_candle['percent12'] < 0.0): + if (last_candle['percent3'] < 0.0) & (current_profit > last_candle['min_max200'] / 3): + self.trades = list() + return 'mx_' + 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) + self.pairs[pair]['max_touch'] = max(last_candle['haclose'], self.pairs[pair]['max_touch']) def informative_pairs(self): # get access to all pairs available in whitelist. @@ -346,10 +284,10 @@ class Zeus_11(IStrategy): # 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} |" + f"| {'Date':<16} | {'Action':<10} | {'Pair':<5} | {'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}|" + f"|{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|" ) self.columns_logged += 1 date = str(date)[:16] if date else "-" @@ -380,12 +318,17 @@ class Zeus_11(IStrategy): 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: + if np.isnan(last_candle['rsi_1d']): + string = ' ' + else: + string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_diff_1d'])) trade_type = trade_type \ - + " " + str(round(100 * last_candle['sma5_pct_1d'], 0)) - # + " " + str(round(last_candle['sma5_diff_1h'], 1)) + + " " + string \ + + " " + str(int(last_candle['rsi_1h'])) \ + + " " + str(int(last_candle['rsi_diff_1h'])) 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} |" + f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {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: @@ -415,6 +358,7 @@ class Zeus_11(IStrategy): dataframe['max50_diff'] = (dataframe['max50'] - dataframe['close']) / dataframe['close'] dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5) + dataframe['sma5_pct'] = (dataframe['sma5'] - dataframe['sma5']) / dataframe['sma5'] dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10) dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20) dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"] @@ -484,7 +428,7 @@ class Zeus_11(IStrategy): # 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") + 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 @@ -492,10 +436,17 @@ class Zeus_11(IStrategy): # 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['rsi'] = talib.RSI(informative['close'], length=7) + informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1) + + informative['sma5'] = talib.SMA(informative, timeperiod=5) + informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5'] + 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['rsi'] = talib.RSI(informative['close'], length=7) + informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1) 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() @@ -548,6 +499,7 @@ class Zeus_11(IStrategy): # 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 + self.pairs[trade.pair]['last_buy'] = buy.price print(buy) count = count + 1 amount += buy.price * buy.filled @@ -610,11 +562,12 @@ class Zeus_11(IStrategy): # ************** - # 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 - ) + # # 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 @@ -639,12 +592,154 @@ class Zeus_11(IStrategy): 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['rsi_1h'] < 70) + & (dataframe['rsi_diff_1h'] > -5) + # (dataframe['down_count'].shift(1) < - 6) + # & (dataframe['down_count'] == 0) + # & (dataframe['down_pct'].shift(1) <= -0.5) + ), ['enter_long', 'enter_tag']] = (1, 'down') dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan) + # for i in range(len(dataframe) - 48): + # last_candle = dataframe.iloc[i] + # if last_candle['enter_long'] is not None: + # if last_candle['enter_long'] == 1: + # futur_candle = dataframe.iloc[i + 48] + # sma5pct_1h = last_candle['sma5_pct_1h'] + # sma5pct_1d = last_candle['sma5_pct_1d'] + # i = i + 48 + # print(f"{i} ===> ;{sma5pct_1d:.2f};{sma5pct_1h:.2f};{100 * futur_candle['percent48']:.1f}") + + # print(dataframe.columns) + # + # colonnes = [ + # 'hapercent', 'close_02', 'pct_change', 'max200_diff', + # 'max50_diff', 'sma5_pct', 'percent', 'percent3', + # 'percent5', 'percent12', 'percent24', 'percent48', 'rsi', + # 'bb_percent', 'down_count', + # 'up_count', 'down_pct', 'up_pct', 'volume_1h', 'rsi_1h', + # 'sma5_pct_1h', 'volume_1d', 'rsi_1d', 'sma5_pct_1d', + # 'pct_min_max_1d'] + # + # exclude_cols = ['date', 'enter_tag', 'close', 'open', 'low', 'high', 'haclose', 'haopen', 'halow', 'hahigh' + # , 'date_1h', 'close_1h', 'open_1h', 'low_1h', 'high_1h', 'haclose_1h', 'haopen_1h', 'halow_1h', 'hahigh_1h' + # , 'date_1d', 'close_1d', 'open_1d', 'low_1d', 'high_1d', 'haclose_1d', 'haopen_1d', 'halow_1d', 'hahigh_1d'] + # for column in colonnes: + # for column2 in colonnes: + # print('===============================================') + # print(f"Colonne 1: {column} Colonne 2: {column2}") + # list_1 = [] + # list_2 = [] + # data = [] + # key_1 = column + # key_2 = column2 + # futur = 'percent48' + # + # for i in range(200, len(dataframe) - 48): + # last_candle = dataframe.iloc[i] + # if last_candle['enter_long'] is not None and last_candle['enter_long'] == 1: + # futur_candle = dataframe.iloc[i + 48] + # val_1 = last_candle[key_1] + # val_2 = last_candle[key_2] + # if not np.isnan(val_1) and not np.isnan(val_2): + # value = 100 * futur_candle[futur] + # list_1.append(val_2) + # list_2.append(val_1) + # data.append(value) + # i += 48 # skip to avoid overlapping trades + # + # # Tes données sous forme de listes + # x = np.array(list_1) # axe X + # y = np.array(list_2) # axe Y + # z = np.array(data) # valeur à afficher (performance future) + # # print(len(list_2), len(list_2), len(data)) + # # print(f"Min/max H1: {min(list_1):.5f}, {max(list_1):.5f}") + # # print(f"Min/max 1D: {min(list_2):.5f}, {max(list_2):.5f}") + # # print(f"Min/max Data: {min(data):.5f}, {max(data):.5f}") + # # Fusionner X et Y comme variables indépendantes + # XY = np.column_stack((x, y)) + # # Modèle + # model = LinearRegression() + # model.fit(XY, z) + # # Coefficients + # a, b = model.coef_ + # c = model.intercept_ + # r_squared = model.score(XY, z) + # print(f"Coefficient de détermination R² : {r_squared:.4f}") + # print(f"Équation estimée : Z = {a:.4f} * X + {b:.4f} * Y + {c:.4f}") + + # degree = 2 # Pour inclure X², Y², XY + # poly_model = make_pipeline(PolynomialFeatures(degree), LinearRegression()) + # poly_model.fit(XY, z) + # + # # Pour afficher les coefficients : + # linreg = poly_model.named_steps['linearregression'] + # print("Coefficients:", linreg.coef_) + # print("Intercept:", linreg.intercept_) + # + # + # # Données factices + # # x = np.random.uniform(-2, 2, 500) + # # y = np.random.uniform(-2, 2, 500) + # # z = np.sin(x) * np.cos(y) * 10 # variation factice + # + # # Discrétisation (binning) + # xbins = np.linspace(min(x), max(x), 20) + # ybins = np.linspace(min(y), max(y), 20) + # + # # Création des bins 2D + # H, xedges, yedges = np.histogram2d(x, y, bins=[xbins, ybins], weights=z) + # counts, _, _ = np.histogram2d(x, y, bins=[xbins, ybins]) # pour normaliser + # + # # Moyenne dans chaque bin (évite division par 0) + # H_avg = np.divide(H, counts, out=np.zeros_like(H), where=counts != 0) + # + # # Préparer coordonnées pour le graphique + # xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1], indexing="ij") + # xpos = xpos.ravel() + # ypos = ypos.ravel() + # zpos = np.zeros_like(xpos) + # + # dx = dy = (xedges[1] - xedges[0]) * 0.9 + # dz = H_avg.ravel() + # + # # Affichage + # fig = plt.figure(figsize=(12, 8)) + # ax = fig.add_subplot(111, projection='3d') + # colors = plt.cm.RdYlGn((dz - dz.min()) / (dz.max() - dz.min() + 1e-5)) # Normalisation + # + # ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=colors, shade=True) + # + # ax.set_xlabel(f"{key_1}") + # ax.set_ylabel(f"{key_2}") + # ax.set_zlabel('Perf. moyenne sur 48 bougies') + # ax.set_title('Performance 48 bougies (%)') + # plt.show() + + # plt.figure(figsize=(10, 8)) + # scatter = plt.scatter( + # list_1, + # list_2, + # c=data, # La couleur selon la performance future + # cmap='RdYlGn', # Dégradé rouge -> jaune -> vert + # alpha=0.8, + # edgecolors='k' + # ) + # plt.xlabel(f"{key_1}") + # plt.ylabel(f"{key_2}") + # plt.title(f"Performance future") + # plt.colorbar(scatter, label="Performance 48 bougies (%)") + # plt.grid(True) + # plt.show() + + # plt.figure(figsize=(10, 6)) + # plt.scatter(list_1, data, c='blue', alpha=0.6) + # plt.xlabel("SMA5 % sur 1 jour") + # plt.ylabel("Variation du prix après 48 bougies (%)") + # plt.title("Lien entre variation SMA5 1j et performance 48h") + # plt.grid(True) + # plt.show() + return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: @@ -652,15 +747,17 @@ class Zeus_11(IStrategy): 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 + max_stake: float, **kwargs): # ne rien faire si ordre deja en cours if trade.has_open_orders: + #print("has open orders : true") return None if (self.wallets.get_available_stake_amount() < 50): # or trade.stake_amount >= max_stake: + #print("wallet too low") 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 count_of_buys = trade.nr_of_successful_entries current_time = current_time.astimezone(timezone.utc) @@ -669,15 +766,18 @@ class Zeus_11(IStrategy): hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0 if (len(dataframe) < 1): + #print("dataframe empty") return None pair = trade.pair if pair not in ('BTC/USDC', 'XRP/USDC', 'BTC/USDT', 'XRP/USDT'): + print(f"{pair} not in allowed pairs list") return None max_buys = 20 - filled_buys = trade.select_filled_orders('buy') - count_of_buys = len(filled_buys) + # filled_buys = trade.select_filled_orders('buy') + # count_of_buys = len(filled_buys) if count_of_buys >= max_buys: + #print(f"count_of_buys {count_of_buys} > {max_buys} max buys") return None # if 'buy' in last_candle: @@ -685,16 +785,20 @@ class Zeus_11(IStrategy): # else: # condition = False # self.protection_nb_buy_lost.value - limit = last_candle['limit'] + # 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) + # 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'], 4) # 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) \ + if ( + ( + last_candle['enter_long'] == 1) + or (last_candle['percent48'] < - 0.03 and last_candle['rsi_diff_1h'] > -5) + ) \ and (pct_max < -0.012 - (count_of_buys * 0.001)): try: @@ -702,7 +806,7 @@ class Zeus_11(IStrategy): # 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}") - + trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48' self.log_trade( last_candle=last_candle, date=current_time, @@ -710,7 +814,7 @@ class Zeus_11(IStrategy): dispo=dispo, pair=trade.pair, rate=current_rate, - trade_type=last_candle['enter_tag'], + trade_type=trade_type, profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2), buys=trade.nr_of_successful_entries + 1, stake=round(stake_amount, 2) @@ -722,9 +826,13 @@ class Zeus_11(IStrategy): except Exception as exception: print(exception) return None + pcte=-0.012 - (count_of_buys * 0.001) + + if not self.dp.runmode.value in ('backtest', 'hyperopt'): + logger.error(f"adjust_trade_position {trade.pair} tag={last_candle['enter_long']} pct48={last_candle['percent48']:.1f} pctmax={pct_max:.4f} pcte={pcte:.4f}") return None - def adjust_stake_amount(self, pair: str, dataframe: DataFrame): + def adjust_stake_amount(self, pair: str, dataframe: DataFrame): # Calculer le minimum des 14 derniers jours current_price = dataframe['close'] diff --git a/Zeus_11.txt b/Zeus_11.txt deleted file mode 100644 index 5df2352..0000000 --- a/Zeus_11.txt +++ /dev/null @@ -1,15 +0,0 @@ -[Achats] -BTC/USDT=63400 -ETH/USDT=2570 -ETC/USDT=10 -DOGE/USDT=0.106 -SOL/USDT=150 -XRP/USDT=0.584 - -[Ventes] -BTC/USDT=63979 -ETH/USDT=2542 -ETC/USDT=70 -DOGE/USDT=0.122 -SOL/USDT=150.24 -XRP/USDT=0.6