ERMD_Strategy_No_Exits

# --- Do not remove these libs ---
from datetime import datetime
from functools import reduce
from typing import Optional, Union
from freqtrade.persistence import Trade
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy
import pandas as pd
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import warnings

warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)

DEBUG = False

class ERMD_Strategy_No_Exits(IStrategy):
    INTERFACE_VERSION: int = 3

    can_short = True  # enable short direction
    timeframe = "1d"
    startup_candle_count = 100  # how many candles we need to skip to start receiving robust signals
    risk = 0.01
    min_pos_pct = 0.01      # minimum position size, percent of current portfolio value
    max_pos_pct = DecimalParameter(0.01, 0.5, default=0.48, decimals=2, optimize=True, space="buy")         # maximum position size, percent of current portfolio value
    minimal_roi = {"0": 0.8380000000000001}       # optimized tp

    # Parameters: signal
    buy_ema_enabled = BooleanParameter(default=False, optimize=True)
    buy_ema1_length = IntParameter(3, 100, default=5, optimize=True)
    buy_ema2_length = IntParameter(3, 100, default=10, optimize=True)
    buy_ema3_length = IntParameter(3, 100, default=20, optimize=True)
    buy_rsi_enabled = BooleanParameter(default=False, optimize=True)
    buy_rsi_length = IntParameter(3, 100, default=14, optimize=True)
    buy_rsi_threshold = IntParameter(50, 100, default=60, optimize=True)
    buy_macd_enabled = BooleanParameter(default=True, optimize=True)
    buy_macd_fast = IntParameter(3, 50, default=12, optimize=True)
    buy_macd_slow = IntParameter(20, 200, default=26, optimize=True)
    buy_macd_smooth = IntParameter(1, 30, default=9, optimize=True)
    buy_candlestick_enabled = BooleanParameter(default=False, optimize=True)

    sell_ema_enabled = BooleanParameter(default=True, optimize=True)
    sell_ema1_length = IntParameter(3, 100, default=5, optimize=True)
    sell_ema2_length = IntParameter(3, 100, default=10, optimize=True)
    sell_ema3_length = IntParameter(3, 100, default=20, optimize=True)
    sell_rsi_enabled = BooleanParameter(default=True, optimize=True)
    sell_rsi_length = IntParameter(3, 100, default=14, optimize=True)
    sell_rsi_threshold = IntParameter(0, 50, default=40, optimize=True)
    sell_macd_enabled = BooleanParameter(default=True, optimize=True)
    sell_macd_fast = IntParameter(3, 50, default=12, optimize=True)
    sell_macd_slow = IntParameter(20, 200, default=26, optimize=True)
    sell_macd_smooth = IntParameter(1, 30, default=9, optimize=True)
    sell_candlestick_enabled = BooleanParameter(default=False, optimize=True)

    # signals
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """ adds indicators to dataframe """

        # BUY
        # EMAs
        for val in self.buy_ema1_length.range:
            dataframe[f"buy_ema1_{val}"] = ta.EMA(dataframe, val)
        for val in self.buy_ema2_length.range:
            dataframe[f"buy_ema2_{val}"] = ta.EMA(dataframe, val)
        for val in self.buy_ema3_length.range:
            dataframe[f"buy_ema3_{val}"] = ta.EMA(dataframe, val)

        # RSI
        for val in self.buy_rsi_length.range:
            dataframe[f"buy_rsi_{val}"] = ta.RSI(dataframe, val)

        # MACD
        for fast_val in self.buy_macd_fast.range:
            for slow_val in self.buy_macd_slow.range:
                for smooth_val in self.buy_macd_smooth.range:
                    macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
                    dataframe[f'buy_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
                    dataframe[f'buy_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']

        # SELL
        # EMAs
        for val in self.sell_ema1_length.range:
            dataframe[f"sell_ema1_{val}"] = ta.EMA(dataframe, val)
        for val in self.sell_ema2_length.range:
            dataframe[f"sell_ema2_{val}"] = ta.EMA(dataframe, val)
        for val in self.sell_ema3_length.range:
            dataframe[f"sell_ema3_{val}"] = ta.EMA(dataframe, val)

        # RSI
        for val in self.sell_rsi_length.range:
            dataframe[f"sell_rsi_{val}"] = ta.RSI(dataframe, val)

        # MACD
        for fast_val in self.sell_macd_fast.range:
            for slow_val in self.sell_macd_slow.range:
                for smooth_val in self.sell_macd_smooth.range:
                    macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
                    dataframe[f'sell_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
                    dataframe[f'sell_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']

        # DOJI candlestick pattern
        doji = ta.CDLDOJI(dataframe)
        doji_dir = (dataframe["high"] - dataframe["close"]) / (dataframe["close"] - dataframe["low"])
        dataframe.loc[((doji == 100) & (doji_dir > 1), "doji")] = dataframe["high"]
        dataframe.loc[((doji == 100) & (doji_dir < 1), "doji")] = dataframe["low"]

        # MARUBOZU candlestick pattern
        body = (dataframe["close"] - dataframe["open"]).abs()
        wick_top = dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
        wick_bottom = dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
        marubozu = (wick_top/body < 0.25) & (wick_bottom/body < 0.25)
        dataframe.loc[(marubozu & (body > 0), 'marubozu')] = dataframe["high"]
        dataframe.loc[(marubozu & (body < 0), 'marubozu')] = dataframe["low"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """ generate signals """

        # BUY signal
        # only trade if there is volume
        conditions = [(dataframe['volume'] > 0)]

        # Close > EMA1 > EMA2 > EMA3
        if self.buy_ema_enabled.value:
            conditions.append((dataframe["close"] > dataframe[f"buy_ema1_{self.buy_ema1_length.value}"]) &
                              (dataframe[f"buy_ema1_{self.buy_ema1_length.value}"] > dataframe[f"buy_ema2_{self.buy_ema2_length.value}"]) &
                              (dataframe[f"buy_ema2_{self.buy_ema2_length.value}"] > dataframe[f"buy_ema3_{self.buy_ema3_length.value}"]))

        # RSI > RSI_Threshold_Long
        if self.buy_rsi_enabled:
            conditions.append(dataframe[f"buy_rsi_{self.buy_rsi_length.value}"] > self.buy_rsi_threshold.value)

        # MACD cross MACDs above or MACD > 0
        if self.buy_macd_enabled.value:
            macd = dataframe[f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
            macds = dataframe[f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
            conditions.append(qtpylib.crossed_above(macd, macds) | (macd > 0))

        # Candlestick patterns: any DOJI and green MARUBOZU
        if self.buy_candlestick_enabled.value:
            doji = dataframe['doji'] == dataframe["high"]
            marubozu = dataframe['marubozu'] == dataframe["high"]
            conditions.append(doji | marubozu)

        # combine conditions and return
        buy_conditions = reduce(lambda x, y: x & y, conditions)
        dataframe.loc[buy_conditions, 'enter_long'] = 1

        # SELL signal
        # only trade if there is volume
        conditions = [(dataframe['volume'] > 0)]

        # Close < EMA1 < EMA2 < EMA3
        if self.sell_ema_enabled.value:
            conditions.append((dataframe["close"] < dataframe[f"sell_ema1_{self.sell_ema1_length.value}"]) &
                              (dataframe[f"sell_ema1_{self.sell_ema1_length.value}"] < dataframe[f"sell_ema2_{self.sell_ema2_length.value}"]) &
                              (dataframe[f"sell_ema2_{self.sell_ema2_length.value}"] < dataframe[f"sell_ema3_{self.sell_ema3_length.value}"]))

        # RSI < RSI_Threshold_Long
        if self.sell_rsi_enabled:
            conditions.append(dataframe[f"sell_rsi_{self.sell_rsi_length.value}"] < self.sell_rsi_threshold.value)

        # MACD cross MACDs below or MACD < 0
        if self.sell_macd_enabled.value:
            macd = dataframe[f'sell_macd_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
            macds = dataframe[f'sell_macds_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
            conditions.append(qtpylib.crossed_below(macd, macds) | (macd < 0))

        # Candlestick patterns, any DOJI and red MARUBOZU
        if self.sell_candlestick_enabled.value:
            doji = dataframe['doji'] == dataframe['low']
            marubozu = dataframe['marubozu'] == dataframe['low']
            conditions.append(doji | marubozu)

        # combine conditions and return
        sell_conditions = reduce(lambda x, y: x & y, conditions)
        dataframe.loc[sell_conditions, 'enter_short'] = 1

        return dataframe

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

    stop_info = {}

    # Stop Loss
    stoploss = -1.00    # disable built-in stop-loss with 100% level

    # Position Size
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
                            leverage: float, entry_tag: Optional[str], side: str,
                            **kwargs) -> float:
        """ custom position size """

        if self.wallets:
            total = self.wallets.get_total("USDT")
        else:
            if DEBUG:
                print("Wallets are not available, using max_stake instead of portfolio value to calculate position size!")
            total = max_stake

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        if side == "long":
            level = last_candle["low"]
            stop_distance = current_rate / level - 1
            self.stop_info[pair] = level
        else:
            level = last_candle["high"]
            stop_distance = level / current_rate - 1
            self.stop_info[pair] = level

        result = total * self.risk / stop_distance

        # apply maximum/minimum position size
        min_pos = self.min_pos_pct * total
        max_pos = self.max_pos_pct.value * total
        if result < min_pos:
            if DEBUG:
                print(f"Calculated position ({round(result, 2)}) is less than minimum position size ({round(min_pos)})! Skipping...")
            return 0
        if result > max_pos:
            if DEBUG:
                print(f"Calculated position ({round(result, 2)}) is greater than maximum position size ({round(min_pos)})! Reduced!")
            result = max_pos

        # check min/max stake
        if result < min_stake:
            if DEBUG:
                print(f"Calculated stake ({round(result,2)}) is less than minimum stake ({min_stake})! Skipping trade...")
            return 0
        elif result > max_stake:
            if DEBUG:
                print(f"Calculated stake ({round(result,2)}) is greater than maximum stake ({max_stake})! Reducing to maximum!")
            result = max_stake

        return result

    # set up plotting
    @property
    def plot_config(self):
        plot_config = {
            'main_plot': {
                f"buy_ema1_{self.buy_ema1_length.value}": {},
                f"buy_ema2_{self.buy_ema2_length.value}": {},
                f"buy_ema3_{self.buy_ema3_length.value}": {},
                f"doji": {"type": "scatter"},
                f"marubozu": {"type": "scatter"}
            },
            'subplots': {
                "MACD": {
                    f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
                    f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
                },
                "RSI": {
                    f'buy_rsi_{self.buy_rsi_length.value}': {}
                }
            }}

        return plot_config