NASOSv4

# for live trailing_stop = False and use_custom_stoploss = True
# for backtest trailing_stop = True and use_custom_stoploss = False

# --- Do not remove these libs ---
from logging import FATAL
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

# @Rallipanos
# @pluxury

# Buy hyperspace params:
buy_params = {
    "base_nb_candles_buy": 8,
    "ewo_high": 2.403,
    "ewo_high_2": -5.585,
    "ewo_low": -14.378,
    "lookback_candles": 3,
    "low_offset": 0.984,
    "low_offset_2": 0.942,
    "profit_threshold": 1.008,
    "rsi_buy": 72,
}

# Sell hyperspace params:
sell_params = {
    "base_nb_candles_sell": 16,
    "high_offset": 1.084,
    "high_offset_2": 1.401,
    "pHSL": -0.15,
    "pPF_1": 0.016,
    "pPF_2": 0.024,
    "pSL_1": 0.014,
    "pSL_2": 0.022,
}


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 NASOSv4(IStrategy):
    INTERFACE_VERSION = 2

    # ROI table:
    minimal_roi = {
        # "0": 0.283,
        # "40": 0.086,
        # "99": 0.036,
        "0": 10
    }

    # Stoploss:
    stoploss = -0.15

    # SMAOffset
    base_nb_candles_buy = IntParameter(
        2, 20, default=buy_params["base_nb_candles_buy"], space="buy", optimize=True
    )
    base_nb_candles_sell = IntParameter(
        2, 25, default=sell_params["base_nb_candles_sell"], space="sell", optimize=True
    )
    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=True
    )
    high_offset_2 = DecimalParameter(
        0.99, 1.5, default=sell_params["high_offset_2"], space="sell", optimize=True
    )

    # Protection
    fast_ewo = 50
    slow_ewo = 200

    lookback_candles = IntParameter(
        1, 24, default=buy_params["lookback_candles"], space="buy", optimize=True
    )

    profit_threshold = DecimalParameter(
        1.0, 1.03, default=buy_params["profit_threshold"], space="buy", optimize=True
    )

    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(
        50, 100, default=buy_params["rsi_buy"], space="buy", optimize=False
    )

    # trailing stoploss hyperopt parameters
    # hard stoploss profit
    pHSL = DecimalParameter(
        -0.200,
        -0.040,
        default=-0.15,
        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=False, load=True
    )
    pSL_1 = DecimalParameter(
        0.008, 0.020, default=0.014, decimals=3, space="sell", optimize=False, load=True
    )

    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(
        0.040, 0.100, default=0.024, decimals=3, space="sell", optimize=False, load=True
    )
    pSL_2 = DecimalParameter(
        0.020, 0.070, default=0.022, decimals=3, space="sell", optimize=False, load=True
    )

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.016
    trailing_only_offset_is_reached = True

    # Sell signal
    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.01
    ignore_roi_if_buy_signal = False

    # Optional order time in force.
    order_time_in_force = {"buy": "gtc", "sell": "ioc"}

    # Optimal timeframe for the strategy
    timeframe = "5m"
    inf_1h = "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}

    # Custom Trailing Stoploss by Perkmeister

    def custom_stoploss(
        self,
        pair: str,
        trade: "Trade",
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> float:

        # # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value

        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.

        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
        else:
            sl_profit = HSL

        # if current_profit < 0.001 and current_time - timedelta(minutes=600) > trade.open_date_utc:
        #     return -0.005

        return stoploss_from_open(sl_profit, current_profit)

    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

        # slippage
        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 informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, "1h") for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(
        self, dataframe: DataFrame, metadata: dict
    ) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(
            pair=metadata["pair"], timeframe=self.inf_1h
        )
        # EMA
        # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # # RSI
        # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)

        # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        # informative_1h['bb_lowerband'] = bollinger['lower']
        # informative_1h['bb_middleband'] = bollinger['mid']
        # informative_1h['bb_upperband'] = bollinger['upper']

        return informative_1h

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

        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe[f"ma_buy_{val}"] = ta.EMA(dataframe, timeperiod=val)

        # Calculate all ma_sell values
        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)
        # Elliot
        dataframe["EWO"] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        # RSI
        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_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True
        )

        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

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

        dont_buy_conditions = []

        dont_buy_conditions.append(
            (
                # don't buy if there isn't 3% profit to be made
                (
                    dataframe["close_1h"].rolling(self.lookback_candles.value).max()
                    < (dataframe["close"] * self.profit_threshold.value)
                )
            )
        )

        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")

        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, "buy"] = 0

        return dataframe

    def populate_sell_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