XGBoost_v1_2

# 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