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Zeus_11 clean

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Jérôme Delacotte
2025-04-17 13:52:29 +02:00
parent 53beffc6d7
commit 7e169a9fe6
3 changed files with 227 additions and 190 deletions
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Zeus_11.py
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@@ -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']