# source: https://raw.githubusercontent.com/minulislam/freqtrade-strategies/749324e245f1d051eb0babdb642faa68f2350e89/user_data/strategies/Elliot.py
# --- Do not remove these libs ---
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

# Buy hyperspace params:
buy_params = {
    "base_nb_candles_buy": 14,
    "ewo_high": 2.327,
    "ewo_low": -19.988,
    "low_offset": 0.975,
    "rsi_buy": 69,
}

# Sell hyperspace params:
sell_params = {"base_nb_candles_sell": 24, "high_offset": 0.991, "high_offset_2": 0.997}


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["close"] * 100
    return emadif


class github_minulislam_freqtrade_strategies__Elliot__20211227_174936(IStrategy):
    INTERFACE_VERSION = 2

    # ROI table:
    minimal_roi = {"0": 0.051, "10": 0.031, "22": 0.018, "66": 0}

    # Stoploss:
    stoploss = -0.32

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

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.03
    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": "gtc"}

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

    process_only_new_candles = True
    startup_candle_count = 39

    plot_config = {
        "main_plot": {
             "sma_9": {"color": "#736581"},
             "hma_50": {"color": "#a9d"},
            "ma_buy": {"color": "bule"},
            "ma_sell": {"color": "orange"},
            "ma_sell_2": {"color": "red"},         
        },

        'subplots': {
             "EWO": {
               'EWO': {'color': 'blue'},
              },  
             "RSI": { 
               'rsi': {'color': 'blue'},       
               'rsi_fast': {'color': 'red'},
               'rsi_slow': {'color': 'orange'},
              },
              
             "TREND": { 
                'uptrend_1h': {'color': 'blue'},          
              },  
              
         }

    }

    use_custom_stoploss = False

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

        if (
            current_profit < -0.1
            and current_time - timedelta(minutes=720) > trade.open_date_utc
        ):
            return -0.01

        return -0.99

    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["ema_fast"] = ta.EMA(informative_1h, timeperiod=20)
        informative_1h["ema_slow"] = ta.EMA(informative_1h, timeperiod=25)

        informative_1h["uptrend"] = (
            (informative_1h["ema_fast"] > informative_1h["ema_slow"])
        ).astype("int")
        informative_1h["rsi_100"] = ta.RSI(informative_1h, timeperiod=100)
        return informative_1h

    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
        )
        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe[f"ma_buy_{val}"] = ta.EMA(dataframe, timeperiod=val)
        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2
        )
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_lowerband"] = bollinger["lower"]
        # 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['hma_50']=hmao
        dataframe["sma_9"] = ta.SMA(dataframe, timeperiod=9)
        # github_minulislam_freqtrade_strategies__Elliot__20211227_174936
        dataframe["EWO"] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["rsi_fast"] = ta.RSI(dataframe, timeperiod=4)
        dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20)
        dataframe["rsi_100"] = ta.RSI(dataframe, timeperiod=100)

        dataframe["ma_buy"] = ta.EMA(dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
        dataframe["ma_sell"] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
        dataframe["ma_sell_2"] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset_2.value




        return dataframe

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

        dataframe.loc[
            (
                (dataframe["uptrend_1h"] > 0)
                & (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, "buy_signal_1")

        dataframe.loc[
            (
                (dataframe["uptrend_1h"] > 0)
                & (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, "buy_signal_2")

        # if conditions:
        # dataframe.loc[reduce(lambda x, y: x | y, conditions), "buy"] = 1

        return dataframe

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

        conditions.append(
            (
                (dataframe["sma_9"] > dataframe["hma_50"])
                & (
                    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"])
            )
        )

        conditions.append(
            (
                (dataframe["sma_9"] < 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