AlphaMA

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame

import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, \
    CategoricalParameter
import technical.indicators as ftt


















































buy_params = {
    "base_nb_candles_buy": 8,
    "ewo_high": 4.179,
    "ewo_low": -16.917,
    "ewo_high_2": -2.609,  # value loaded from strategy
    "low_offset": 0.986,  # value loaded from strategy
    "low_offset_2": 0.944,  # value loaded from strategy
    "rsi_buy": 58,  # value loaded from strategy
}

sell_params = {
    "base_nb_candles_sell": 16,
    "high_offset": 1.054,
    "high_offset_2": 1.018,  # value loaded from strategy
}


def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif


class AlphaMA(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {



        "0": 10
    }

    stoploss = -0.3

    base_nb_candles_buy = IntParameter(
        2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(
        10, 40, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
    low_offset = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False)
    low_offset_2 = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False)
    high_offset = DecimalParameter(
        0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False)
    high_offset_2 = DecimalParameter(
        0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False)

    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0,
                               default=buy_params['ewo_low'], space='buy', optimize=False)
    ewo_high = DecimalParameter(
        2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False)

    ewo_high_2 = DecimalParameter(
        -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False)

    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)

    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.025
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'ioc'
    }




    timeframe = '5m'
    informative_timeframe = '1h'

    process_only_new_candles = True
    startup_candle_count = 200
    use_custom_stoploss = False

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
        },
    }

    slippage_protection = {
        'retries': 3,
        'max_slippage': -0.02
    }

    buy_signals = {}

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str,
                           current_time: datetime, **kwargs) -> bool:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]

        if (last_candle is not None):
            if (sell_reason in ['sell_signal']):
                if (last_candle['hma_50'] * 1.149 > last_candle['ema_100']) and (
                        last_candle['close'] < last_candle['ema_100'] * 0.951):  # *1.2
                    return False

        try:
            state = self.slippage_protection['__pair_retries']
        except KeyError:
            state = self.slippage_protection['__pair_retries'] = {}

        candle = dataframe.iloc[-1].squeeze()

        slippage = (rate / candle['close']) - 1
        if slippage < self.slippage_protection['max_slippage']:
            pair_retries = state.get(pair, 0)
            if pair_retries < self.slippage_protection['retries']:
                state[pair] = pair_retries + 1
                return False

        state[pair] = 0

        return True

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)

        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)

        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)

        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)

        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                    (dataframe['rsi_fast'] < 35) &
                    (dataframe['close'] < (
                                dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                    (dataframe['EWO'] > self.ewo_high.value) &
                    (dataframe['rsi'] < self.rsi_buy.value) &
                    (dataframe['volume'] > 0) &
                    (dataframe['close'] < (
                            dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
            ),
            ['buy', 'buy_tag']] = (1, 'ewo1')

        dataframe.loc[
            (
                    (dataframe['rsi_fast'] < 35) &
                    (dataframe['close'] < (
                                dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) &
                    (dataframe['EWO'] > self.ewo_high_2.value) &
                    (dataframe['rsi'] < self.rsi_buy.value) &
                    (dataframe['volume'] > 0) &
                    (dataframe['close'] < (
                                dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                    (dataframe['rsi'] < 25)
            ),
            ['buy', 'buy_tag']] = (1, 'ewo2')

        dataframe.loc[
            (
                    (dataframe['rsi_fast'] < 35) &
                    (dataframe['close'] < (
                                dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                    (dataframe['EWO'] < self.ewo_low.value) &
                    (dataframe['volume'] > 0) &
                    (dataframe['close'] < (
                            dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
            ),
            ['buy', 'buy_tag']] = (1, 'ewolow')

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            ((dataframe['close'] > dataframe['sma_9']) &
             (dataframe['close'] > (
                         dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
             (dataframe['rsi'] > 50) &
             (dataframe['volume'] > 0) &
             (dataframe['rsi_fast'] > dataframe['rsi_slow'])
             )
            |
            (
                    (dataframe['close'] < dataframe['hma_50']) &
                    (dataframe['close'] > (
                                dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                    (dataframe['volume'] > 0) &
                    (dataframe['rsi_fast'] > dataframe['rsi_slow'])
            )

        )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'sell'
            ] = 1

        return dataframe