abbas

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, Series
# --------------------------------
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, BooleanParameter
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real


# Protection hyperspace params:
protection_params = {
    "cooldown_stop_duration_candles": 1,
    "lowprofit2_lookback_period_candles": 107,
    "lowprofit2_required_profit": 0.026,
    "lowprofit2_stop_duration_candles": 8,
    "lowprofit2_trade_limit": 6,
    "lowprofit_lookback_period_candles": 34,
    "lowprofit_required_profit": 0.027,
    "lowprofit_stop_duration_candles": 126,
    "lowprofit_trade_limit": 36,
    "maxdrawdown_lookback_period_candles": 36,
    "maxdrawdown_max_allowed_drawdown": 0.34,
    "maxdrawdown_stop_duration_candles": 54,
    "maxdrawdown_trade_limit": 17,
    "stoplossguard_lookback_period_candles": 3,
    "stoplossguard_stop_duration_candles": 8,
    "stoplossguard_trade_limit": 11,
}

# Buy hyperspace params:
buy_params = {
    "base_nb_candles_buy": 17,
    "ewo_high": 2.182,
    "ewo_high_2": -3.44,
    "ewo_low": -10.26,
    "low_offset": 1.066,
    "low_offset_2": 0.961,
    "rsi_buy": 68,
    "min_profit": 1.03
}

# Sell hyperspace params:
sell_params = {
    "base_nb_candles_sell": 9,
    "high_offset": 1.01,
    "high_offset_2": 1.233,
    "high_offset_ema": 0.931
}

class abbas(IStrategy):

    INTERFACE_VERSION = 2

    cooldown_stop_duration_candles = IntParameter(0, 20, default=protection_params["cooldown_stop_duration_candles"], space="protection", optimize=True)

    maxdrawdown_optimize = True
    maxdrawdown_lookback_period_candles = IntParameter(5, 40, default=protection_params["maxdrawdown_lookback_period_candles"], space="protection", optimize=maxdrawdown_optimize)
    maxdrawdown_trade_limit = IntParameter(1, 20, default=protection_params["maxdrawdown_trade_limit"], space="protection", optimize=maxdrawdown_optimize)
    maxdrawdown_stop_duration_candles = IntParameter(10, 60, default=protection_params["maxdrawdown_stop_duration_candles"], space="protection", optimize=maxdrawdown_optimize)
    maxdrawdown_max_allowed_drawdown = DecimalParameter(0.05, 0.40, default=protection_params["maxdrawdown_max_allowed_drawdown"], space="protection", decimals=2, optimize=maxdrawdown_optimize)

    stoplossguard_optimize = True
    stoplossguard_lookback_period_candles = IntParameter(1, 300, default=protection_params["stoplossguard_lookback_period_candles"], space="protection", optimize=stoplossguard_optimize)
    stoplossguard_trade_limit = IntParameter(1, 20, default=protection_params["stoplossguard_trade_limit"], space="protection", optimize=stoplossguard_optimize)
    stoplossguard_stop_duration_candles = IntParameter(1, 10, default=protection_params["stoplossguard_stop_duration_candles"], space="protection", optimize=stoplossguard_optimize)

    lowprofit_optimize = True
    lowprofit_lookback_period_candles = IntParameter(10, 60, default=protection_params["lowprofit_lookback_period_candles"], space="protection", optimize=lowprofit_optimize)
    lowprofit_trade_limit = IntParameter(1, 50, default=protection_params["lowprofit_trade_limit"], space="protection", optimize=lowprofit_optimize)
    lowprofit_stop_duration_candles = IntParameter(10, 200, default=protection_params["lowprofit_stop_duration_candles"], space="protection", optimize=lowprofit_optimize)
    lowprofit_required_profit = DecimalParameter(0.000, 0.050, default=protection_params["lowprofit_required_profit"], space="protection", decimals=3, optimize=lowprofit_optimize)

    lowprofit2_optimize = True
    lowprofit2_lookback_period_candles = IntParameter(10, 300, default=protection_params["lowprofit2_lookback_period_candles"], space="protection", optimize=lowprofit2_optimize)
    lowprofit2_trade_limit = IntParameter(1, 70, default=protection_params["lowprofit2_trade_limit"], space="protection", optimize=lowprofit2_optimize)
    lowprofit2_stop_duration_candles = IntParameter(1, 40, default=protection_params["lowprofit2_stop_duration_candles"], space="protection", optimize=lowprofit2_optimize)
    lowprofit2_required_profit = DecimalParameter(0, 0.030, default=protection_params["lowprofit2_required_profit"], space="protection", decimals=3, optimize=lowprofit2_optimize)

    @property
    def protections(self):
        prot = []

        prot.append({
            "method": "CooldownPeriod",
            "stop_duration_candles": self.cooldown_stop_duration_candles.value
        })
        prot.append({
            "method": "MaxDrawdown",
            "lookback_period_candles": self.maxdrawdown_lookback_period_candles.value,
            "trade_limit": self.maxdrawdown_trade_limit.value,
            "stop_duration_candles": self.maxdrawdown_stop_duration_candles.value,
            "max_allowed_drawdown": self.maxdrawdown_max_allowed_drawdown.value
        })
        prot.append({
            "method": "StoplossGuard",
            "lookback_period_candles": self.stoplossguard_lookback_period_candles.value,
            "trade_limit": self.stoplossguard_trade_limit.value,
            "stop_duration_candles": self.stoplossguard_stop_duration_candles.value,
            "only_per_pair": False
        })
        prot.append({
            "method": "LowProfitPairs",
            "lookback_period_candles": self.lowprofit_lookback_period_candles.value,
            "trade_limit": self.lowprofit_trade_limit.value,
            "stop_duration_candles": self.lowprofit_stop_duration_candles.value,
            "required_profit": self.lowprofit_required_profit.value
        })
        prot.append({
            "method": "LowProfitPairs",
            "lookback_period_candles": self.lowprofit2_lookback_period_candles.value,
            "trade_limit": self.lowprofit2_trade_limit.value,
            "stop_duration_candles": self.lowprofit2_stop_duration_candles.value,
            "required_profit": self.lowprofit2_required_profit.value
        })

        return prot

    class HyperOpt:
        # Define a custom stoploss space.
        def stoploss_space():
            return [SKDecimal(-0.080, -0.030, decimals=3, name="stoploss")]

        # Define custom trailing space
        def trailing_space() -> List[Dimension]:
            return[
                Categorical([True, False], name="trailing_stop"),
                SKDecimal(0.00010, 0.00100, decimals=5, name="trailing_stop_positive"),
                SKDecimal(0.0080, 0.0180, decimals=4, name="trailing_stop_positive_offset_p1"),
                Categorical([True, False], name="trailing_only_offset_is_reached"),
            ]

    # ROI table:
    minimal_roi = {
        "200": 0
    }

    # Stoploss:
    stoploss = -0.04

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.00013
    trailing_stop_positive_offset = 0.00963
    trailing_only_offset_is_reached = True

    # Sell signal
    use_sell_signal = False
    ignore_roi_if_buy_signal = False

    # SMAOffset
    smaoffset_optimize = True
    high_offset_ema = DecimalParameter(0.90, 1.1, default=sell_params["high_offset_ema"], load=True, space="sell", decimals=3, optimize=smaoffset_optimize)
    base_nb_candles_buy = IntParameter(15, 30, default=buy_params["base_nb_candles_buy"], space="buy", optimize=smaoffset_optimize)
    base_nb_candles_sell = IntParameter(5, 30, default=sell_params["base_nb_candles_sell"], space="sell", optimize=smaoffset_optimize)
    low_offset = DecimalParameter(1.0, 1.1, default=buy_params["low_offset"], space="buy", decimals=3, optimize=smaoffset_optimize)
    low_offset_2 = DecimalParameter(0.94, 0.98, default=buy_params["low_offset_2"], space="buy", decimals=3, optimize=smaoffset_optimize)
    high_offset = DecimalParameter(1.0, 1.1, default=sell_params["high_offset"], space="sell", decimals=3, optimize=smaoffset_optimize)
    high_offset_2 = DecimalParameter(1.2, 1.5, default=sell_params["high_offset_2"], space="sell", decimals=3, optimize=smaoffset_optimize)

    # Protection
    fast_ewo = 50
    slow_ewo = 200
    protection_optimize = True
    ewo_low = DecimalParameter(-12.0, -8.0,default=buy_params["ewo_low"], space="buy", decimals=2, optimize=protection_optimize)
    ewo_high = DecimalParameter(1.0, 2.2, default=buy_params["ewo_high"], space="buy", decimals=3, optimize=protection_optimize)
    ewo_high_2 = DecimalParameter(-4.0, -2.0, default=buy_params["ewo_high_2"], space="buy", decimals=2, optimize=protection_optimize)
    rsi_buy = IntParameter(55, 85, default=buy_params["rsi_buy"], space="buy", optimize=protection_optimize)

    min_profit = DecimalParameter(0.70, 1.20, default=buy_params["min_profit"], space="buy", decimals=2, optimize=protection_optimize)

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

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

    process_only_new_candles = True
    startup_candle_count = 200

    plot_config = {
        "main_plot": {
            "bb_upperband28": {"color": "#bc281d","type": "line"},
            "bb_midband28": {"color": "orange", "type": "line"},
            "bb_lowerband28": {"color": "#792bbb","type": "line"}
        },
        "subplots": {
            "RSI": {
                "rsi": {"color": "yellow"},
            }
        }
    }

    slippage_protection = {
        "retries": 3,
        "max_slippage": -0.002
    }

    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)

        # 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):
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        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)

        informative_1h["hma_50"] = qtpylib.hull_moving_average(informative_1h["close"], window=50)
        informative_1h["ema_100"] = ta.EMA(informative_1h, timeperiod=100)
        informative_1h["ema_12"] = ta.EMA(informative_1h, timeperiod=12)
        informative_1h["ema_20"] = ta.EMA(informative_1h, timeperiod=20)
        informative_1h["ema_26"] = ta.EMA(informative_1h, timeperiod=26)
        informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200)
        informative_1h["sma_200"] = ta.SMA(informative_1h, timeperiod=200)
        informative_1h["sma_200_dec"] = informative_1h["sma_200"] < informative_1h["sma_200"].shift(20)
        informative_1h["sma_9"] = ta.SMA(informative_1h, timeperiod=9)

        # Elliot
        informative_1h["EWO"] = EWO(informative_1h, self.fast_ewo, self.slow_ewo)

        # RSI
        informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14)
        informative_1h["rsi_fast"] = ta.RSI(informative_1h, timeperiod=4)
        informative_1h["rsi_slow"] = ta.RSI(informative_1h, timeperiod=20)

        return informative_1h

    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["ema_12"] = ta.EMA(dataframe, timeperiod=12)
        dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
        dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200)
        dataframe["sma_200_dec"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20)
        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)

        # Bollinger bands
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2.8)
        dataframe["bb_lowerband28"] = bollinger2["lower"]
        dataframe["bb_middleband28"] = bollinger2["mid"]
        dataframe["bb_upperband28"] = bollinger2["upper"]

        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        return dataframe

    def populate_buy_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")

        dont_buy_conditions = []

        dont_buy_conditions.append(
            (
                (dataframe["close_1h"].rolling(24).max() < (dataframe["close"] * self.min_profit.value ))
            )
        )

        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:
        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