souti/Freqtrade
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Zeus_11 logs / adjust change /

Browse Source
This commit is contained in:
Jérôme Delacotte
2025-03-31 21:47:58 +02:00
parent a484cf5ecc
commit 4ef36babbd
1 changed files with 153 additions and 47 deletions
Download Patch File Download Diff File Expand all files Collapse all files

200
Zeus_11.py
View File

@@ -56,13 +56,12 @@ class Zeus_11(IStrategy):
# Stoploss:
stoploss = -1 # 0.256
# Custom stoploss
use_custom_stoploss = True
use_custom_stoploss = False
# Buy hypers
timeframe = '5m'
max_open_trades = 5
max_amount = 40
columns_logged = False
# DCA config
position_adjustment_enable = True
@@ -179,14 +178,25 @@ class Zeus_11(IStrategy):
# 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)
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,
'base_stake_amount': 0,
'stop_buy': False
}
for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "DASH/USDC", "XRP/USDC", "SOL/USDC",
"BTC/USDT", "ETH/USDT", "DOGE/USDT", "DASH/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."""
@@ -212,7 +222,32 @@ class Zeus_11(IStrategy):
allow_to_buy = True # (rate <= float(limit)) | (entry_tag == 'force_entry')
self.trades = list()
dispo = round(self.wallets.get_available_stake_amount())
print(f"BUY {pair} {entry_tag} {current_time} allow_to_buy={allow_to_buy} dispo={dispo}")
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
@@ -226,11 +261,27 @@ class Zeus_11(IStrategy):
allow_to_sell = (last_candle['percent'] < 0)
if allow_to_sell:
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}")
else:
print('Cancel Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair)
# 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,
@@ -253,19 +304,17 @@ class Zeus_11(IStrategy):
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'])
# self.analyze_conditions(pair, dataframe)
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
# print("---------------" + pair + "----------------")
expected_profit = self.expectedProfit(pair, last_candle)
# Calcul du prix cible basé sur l'ATR
atr_take_profit = trade.open_rate + (last_candle['atr'] * 2) # Prendre profit à 2x l'ATR
# print(f"{pair} Custom exit atr_take_profit={atr_take_profit:.4f}")
# if current_rate >= atr_take_profit:
# return 'sell_atr_take_profit'
if (last_candle['percent3'] < 0.0) & (current_profit > last_candle['min_max200'] / 3):
self.trades = list()
return 'min_max200_' + str(count_of_buys)
@@ -284,6 +333,59 @@ class Zeus_11(IStrategy):
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 * self.pairs[pair]['expected_profit'], 1))
# + " " + str(round(last_candle['sma5_diff_1d'], 1)) \
# + " " + 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']
@@ -540,33 +642,21 @@ class Zeus_11(IStrategy):
self.trades = Trade.get_open_trades()
return self.trades
# def getTrade(self, pair):
# trades = self.getOpenTrades()
# trade_for_pair = None
# for trade in trades:
# if trade.pair == pair:
# trade_for_pair = trade
# break
# return trade_for_pair
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pair = metadata['pair']
# self.getOpenTrades()
expected_profit = self.expectedProfit(pair, dataframe.iloc[-1])
# self.getBinanceOrderBook(pair, dataframe)
last_candle = dataframe.iloc[-1].squeeze()
#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))
# 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)
# buy_level = dataframe['buy_level'] # self.get_buy_level(pair, dataframe)
dataframe.loc[
(
# (dataframe['hapercent'] > 0)
(dataframe['down_count'].shift(1) < - 6)
& (dataframe['down_count'] == 0)
& (dataframe['down_pct'].shift(1) <= -0.5)
@@ -599,7 +689,7 @@ class Zeus_11(IStrategy):
if (len(dataframe) < 1):
return None
pair = trade.pair
if pair not in ('BTC/USDC', 'XRP/USDC'):
if pair not in ('BTC/USDC', 'XRP/USDC', 'BTC/USDT', 'XRP/USDT'):
return None
max_buys = 20
@@ -619,17 +709,33 @@ class Zeus_11(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_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 ( hours > 48 or (current_profit < -0.015 * count_of_buys and hours > 6)):
and (pct_max < -0.015):
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}")
# 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)