NASMAO

from freqtrade.strategy.interface import IStrategy
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 datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter

# NASMAO
# source: -


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 NASMAO(IStrategy):
    INTERFACE_VERSION = 2

    # Buy hyperspace params:
    buy_params = {
        "base_nb_candles_buy": 42,
        "ewo_high": 8.642,
        "ewo_high_2": 0.968,
        "ewo_low": -8.197,
        "low_offset": 0.984,
        "low_offset_2": 0.986,
        "rsi_buy": 46,
    }

    # Sell hyperspace params:
    sell_params = {
        "base_nb_candles_sell": 79,
        "high_offset": 1.002,
        "high_offset_2": 1.038,
    }

    # ROI table:
    minimal_roi = {"0": 10}

    # Stoploss:
    stoploss = -0.04

    # SMAOffset
    base_nb_candles_buy = IntParameter(
        5, 80, default=buy_params["base_nb_candles_buy"], space="buy", optimize=True
    )
    base_nb_candles_sell = IntParameter(
        5, 80, 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=True
    )
    low_offset_2 = DecimalParameter(
        0.9, 0.99, default=buy_params["low_offset_2"], space="buy", optimize=True
    )
    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
    ewo_low = DecimalParameter(
        -20.0, -8.0, default=buy_params["ewo_low"], space="buy", optimize=True
    )
    ewo_high = DecimalParameter(
        2.0, 12.0, default=buy_params["ewo_high"], space="buy", optimize=True
    )
    ewo_high_2 = DecimalParameter(
        -6.0, 12.0, default=buy_params["ewo_high_2"], space="buy", optimize=True
    )
    rsi_buy = IntParameter(
        30, 70, default=buy_params["rsi_buy"], space="buy", optimize=True
    )

    # Trailing stop:
    trailing_stop = False
    # trailing_stop_positive = 0.005
    # trailing_stop_positive_offset = 0.03
    # trailing_only_offset_is_reached = True

    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    # Optimal timeframe for the strategy
    timeframe = "5m"
    inf_1h = "1h"

    process_only_new_candles = True
    startup_candle_count = 200

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

    buy_signals = {}

    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 populate_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.copy()

    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