Timeframe
1d
Direction
Long Only
Stoploss
-100.0%
Trailing Stop
No
ROI
0m: 1000000.0%
Interface Version
3
Startup Candles
15
Indicators
4
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union
from joblib import load
import sys
sys.path.append('/home/matias/freqtrade/dev/crypto_trading/ml_api')
from strategy_funcs import process_dataframe
# additional imports required
from datetime import datetime
from freqtrade.persistence import Trade
from functools import reduce
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import IStrategy, stoploss_from_absolute
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib
import logging
logger = logging.getLogger(__name__)
class XGBoost_v1_2(IStrategy):
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Optimal timeframe for the strategy.
timeframe = '1d'
# Indicators startup period
startup_candle_count = 15
# Can this strategy go short?
can_short: bool = False
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 10000
}
# Establecer el stop loss absoluto
stoploss = -1
trailing_stop = False
use_custom_stoploss = True
trailing_stop_positive: None
trailing_stop_positive_offset: 0.0
trailing_only_offset_is_reached: False
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Optional order time in force.
order_time_in_force = {
'entry': 'GTC',
'exit': 'GTC'
}
@property
def plot_config(self):
return {
# Main plot indicators (Moving averages, ...)
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
}
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['emaFast'] = ta.EMA(dataframe, timeperiod=4)
dataframe['emaSlow'] = ta.EMA(dataframe, timeperiod=8)
dataframe["atr"] = ta.ATR(dataframe,timeperiod=9) # average true range
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Transforma dataframe en ventanas de 14 dias
window_size = 14
data = process_dataframe(dataframe, lags=window_size)
# Calculamos predicciones
clf, _ = load('dev/crypto_trading/modelos/clasificadores/xgb.joblib')
probas = clf.predict_proba(data)[:,1]
preds = probas > 0.53
# Compramos en los dias indicados por las predicciones
dataframe.loc[1+window_size:, 'enter_long'] = preds.astype(int)
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
"""
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> Union[float, None]:
Calculates stop loss based on ATR.
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# Calcular el ATR y obtener el valor del último cierre
tr1 = dataframe['high'].iloc[:-1] - dataframe['low'].iloc[:-1]
tr2 = abs(dataframe['high'].iloc[:-1] - dataframe['close'].iloc[:-1].shift())
tr3 = abs(dataframe['low'].iloc[:-1] - dataframe['close'].iloc[:-1].shift())
tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
atr = tr.rolling(9).mean()
stop_loss = dataframe["close"].iloc[-1] - (atr.iloc[-1] * 3)
# Calcular el porcentaje de pérdida basado en el valor del stoploss y el precio actual (porcentaje no porque esta divido 100)
loss_percent = (current_rate - stop_loss) / current_rate
return loss_percent
"""
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> Union[float, None]:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
## FORMA 2: Esta seria con el stoploss que yo usaba -> Funciona mejor anualmente
# ESTO es con el encoder.
delta = 1e-6 # Minima de cambio: venta
factor = 2
# Cierre anterior menos dos veces el atr
cierre_atr = (dataframe['close'] - factor*dataframe['atr']).shift(1).iloc[-1]
low = current_rate # Ultimo low # con close va bien ->
# Logica simplificada para que salga apenas el sto
if low < cierre_atr: cambio_porcentual = delta # Minima de cambio venta # Se puede cambiar el low por current rate
else: cambio_porcentual = 1 # Alto para que no de señal de venta
return cambio_porcentual
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
stakes = {'BTC/USDT':0.5,'ETH/USDT':0.25,'BNB/USDT':0.05,'DOGE/USDT':0.05,'SOL/USDT':0.05,'MATIC/USDT':0.05,'ADA/USDT':0.05}
#stakes = {'ETH/BTC':0.5, 'BNB/BTC':0.1, 'ADA/BTC':0.1, 'DOGE/BTC':0.1, 'SOL/BTC':0.1, 'MATIC/BTC':0.1}
try:
if self.config['stake_amount'] == 'unlimited' or self.config['stake_amount'] > 0:
# Use entire available wallet during favorable conditions when in compounding mode.
return self.wallets.get_total_stake_amount() * stakes[pair]
else:
# Compound profits during favorable conditions instead of using a static stake.
return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
except:
# Use default stake amount.
return proposed_stake