NASOS7

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

# NASOSv7
# source: https://strat.ninja/overview.php?strategy=NASOSv7


class NASOS7(IStrategy):
    INTERFACE_VERSION = 3

    # 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,
    }

    # ROI table:
    minimal_roi = {"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 = False
    # trailing_stop_positive = 0.001
    # trailing_stop_positive_offset = 0.016
    # 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

    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
        )

        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_entry_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
                    )
                )
            ),
            ["enter_long", "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)
            ),
            ["enter_long", "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
                    )
                )
            ),
            ["enter_long", "buy_tag"],
        ] = (1, "ewolow")

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

        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), "exit_long"] = 1

        return dataframe


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