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Merge remote-tracking branch 'origin/Multiple_paires' into Multiple_paires

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Jérôme Delacotte
2026-03-28 20:48:10 +01:00
parent 5b1e8bede4 1bc5a38796
commit 2a7dd0beb4
3 changed files with 56 additions and 20 deletions
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8
Empty5m.json
View File

@@ -17,13 +17,13 @@
"max_open_trades": 20
},
"sell": {
"b30_indicateur": "sma24",
"baisse": 0.2,
"sell_force_sell": -0.121,
"b30_indicateur": "sma36",
"baisse": 0.3,
"sell_force_sell": -0.03,
"sell_indicator": "sma36"
},
"protection": {
"drop_from_last_entry": 0.0,
"drop_from_last_entry": -0.01,
"mises_bull": 4
}
},

62
Empty5m.py
View File

@@ -19,6 +19,8 @@ from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalP
import ta
import talib.abstract as talib
import freqtrade.vendor.qtpylib.indicators as qtpylib
from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
from ta.momentum import RSIIndicator, StochasticOscillator
from functools import reduce
from random import shuffle
@@ -103,6 +105,7 @@ class Empty5m(IStrategy):
position_adjustment_enable = True
use_custom_stoploss = True
can_short = True
#max_open_trades = 3
# Optimal ticker interval for the strategy.
@@ -559,14 +562,14 @@ class Empty5m(IStrategy):
self.pairs[pair]['force_sell'] = True
return 'smaBF'
else:
if current_profit < self.sell_force_sell.value \
if current_profit < self.sell_force_sell.value and self.wallets.get_available_stake_amount() < 50 \
and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
self.pairs[pair]['force_sell'] = True
return 'sma'
if current_profit > 0.00 and \
if current_profit > 0.01 and \
(baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
and last_candle['hapercent'] <0 :
and last_candle['hapercent'] < 0:
self.pairs[pair]['force_sell'] = True
return 'B30'
@@ -694,12 +697,14 @@ class Empty5m(IStrategy):
dataframe["volume_mean"] = dataframe["volume"].rolling(20).mean()
dataframe["volume_ratio"] = dataframe["volume"] / dataframe["volume_mean"]
dataframe["market_state"] = 0
# dataframe["market_state"] = 0
#
# dataframe.loc[dataframe["percent"] < -0.005, "market_state"] = -1
# dataframe.loc[(dataframe["percent3"] < -0.015) & (dataframe["volume_ratio"] > 2), "market_state"] = -2
# dataframe.loc[(dataframe["percent"] > 0.003) & (dataframe["volume_ratio"] > 1.5), "market_state"] = 1
# dataframe["velocity"] = dataframe["percent"] - dataframe["percent3"]
dataframe.loc[dataframe["percent"] < -0.005, "market_state"] = -1
dataframe.loc[(dataframe["percent3"] < -0.015) & (dataframe["volume_ratio"] > 2), "market_state"] = -2
dataframe.loc[(dataframe["percent"] > 0.003) & (dataframe["volume_ratio"] > 1.5), "market_state"] = 1
dataframe["velocity"] = dataframe["percent"] - dataframe["percent3"]
dataframe = self.calculateMarketState(dataframe, metadata)
# ######################################################################################################
################### INFORMATIVE 1h
@@ -746,6 +751,7 @@ class Empty5m(IStrategy):
informative['stop_buying'] = latched
informative = self.calculateDownAndUp(informative, limit=0.0001)
informative = self.calculateMarketState(informative, metadata)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
# ######################################################################################################
@@ -789,6 +795,7 @@ class Empty5m(IStrategy):
else:
latched[i] = latched[i - 1]
informative['stop_buying'] = latched
informative = self.calculateMarketState(informative, metadata)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
# ######################################################################################################
@@ -981,17 +988,23 @@ class Empty5m(IStrategy):
conditions = list()
# conditions.append(dataframe['mid_regression'].shift(2) > dataframe['mid_regression'].shift(1))
# conditions.append(dataframe['mid_regression'].shift(1) < dataframe['mid_regression'])
conditions.append(dataframe['close'] <= dataframe['min12_1h'])
conditions.append(dataframe['min60'] == dataframe['min60'].shift(5))
conditions.append(dataframe['close'] <= dataframe['min3_1h'])
# conditions.append(dataframe['min60'] == dataframe['min60'].shift(5))
conditions.append(dataframe['has_cross_min_6'] == 1)
conditions.append(dataframe['down_count'] <= 5)
conditions.append(dataframe['down_count'] <= 5)
# conditions.append(dataframe['down_count'] <= 5)
# conditions.append(dataframe['sma12_deriv1'] >= 0)
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
['enter_long', 'enter_tag']
] = (1, 'Mid')
dataframe.loc[
(
(dataframe['rsi'] > 70) # surachat
),
'enter_short'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -1413,4 +1426,27 @@ class Empty5m(IStrategy):
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
return down_pct_values
def calculateMarketState(self, dataframe, metadata):
dataframe['ema50'] = EMAIndicator(dataframe['close'], window=50).ema_indicator()
dataframe['ema200'] = EMAIndicator(dataframe['close'], window=200).ema_indicator()
# RSI
dataframe['rsi'] = RSIIndicator(dataframe['close'], window=14).rsi()
dataframe["market_state"] = 0
cond_bull = (
(dataframe["rsi"] > 55) &
(dataframe["ema50"] > dataframe["ema200"])
)
cond_bear = (
(dataframe["rsi"] < 45) &
(dataframe["ema50"] < dataframe["ema200"])
)
dataframe.loc[cond_bull, "market_state"] = 1
dataframe.loc[cond_bear, "market_state"] = -1
return dataframe

6
FrictradeLearning.py
View File

@@ -1267,7 +1267,7 @@ class FrictradeLearning(IStrategy):
# hours_since_first_price = (current_time - trade.open_date_utc).seconds / 3600.0
# days_since_first_price = (current_time - trade.open_date_utc).days
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
# minutes = (current_time - trade.date_last_filled_utc).total_seconds() / 60.0
minutes = (current_time - trade.date_last_filled_utc).total_seconds() / 60.0
count_of_buys = trade.nr_of_successful_entries
# current_time_utc = current_time.astimezone(timezone.utc)
@@ -1331,8 +1331,8 @@ class FrictradeLearning(IStrategy):
increase = - decline
# FIN ########################## ALGO ATH
force = hours > 12 #self.hours_force.value and last_candle[self.indic_1h_force_buy.value] > 0
condition = last_candle['percent'] > 0 \
force = False #self.hours_force.value and last_candle[self.indic_1h_force_buy.value] > 0
condition = minutes > 5 and last_candle['percent'] > 0 \
and ((count_of_buys <= 4 and last_candle['sma24_deriv1'] > 0) or (count_of_buys > 4 and last_candle['sma60_deriv1'] > 0))\
and last_candle['close'] < self.pairs[pair]['first_price']