# source: https://raw.githubusercontent.com/devsmitra/trading/9c881a8decfb7c5acabf1edbdf2172b56f2d1a28/user_data/strategies/NadarayaWatson.py
# --- Do not remove these libs ---
from datetime import datetime
from typing import Any, Optional
from freqtrade.strategy import IStrategy, informative, stoploss_from_absolute
from pandas import DataFrame
import talib.abstract as ta
import indicators as indicators
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
# --------------------------------

class github_devsmitra_trading__NadarayaWatson__20230409_183646(IStrategy):
    INTERFACE_VERSION: int = 3

    minimal_roi = { "0":  1 }

    # Optimal stoploss designed for the strategy
    sl = 2.4
    stoploss = -0.1
    use_custom_stoploss = False

    # Optimal timeframe for the strategy
    timeframe = '1h'

    custom_info: dict = {}

    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:
        if self.wallets is None:
            return proposed_stake
        return self.wallets.get_total_stake_amount() * .06

    def populate_indicators(self, df: DataFrame, metadata: dict) -> DataFrame:
        df['kr'] = indicators.kernel_regression(df['close'], loop_back=32)
        df['gr'] = indicators.gaussian_regression(df['close'], loop_back=8)
        # df['lp'] = indicators.locally_periodic(df['close'], loop_back=8)
        df['ema'] = indicators.smma(df, timeperiod=32)
        df['atr'] = ta.ATR(df, timeperiod=14)
        df['gr_atr'] = indicators.gaussian_regression(df['atr'], loop_back=8)
        return df

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        pair = metadata['pair']
        df.loc[
            (
                ((df['close'].diff() / df['close'])  < 0.05) &
                (
                    (
                        (df['close'] > df['ema']) &
                        qtpylib.crossed_above(df['gr'], df['kr'])
                    ) | (
                        qtpylib.crossed_above(df['close'], df['ema']) &
                        (df['kr'] < df['gr'])
                    )
                )

                # (df['atr'] > df['gr_atr']) &
                # (
                #     (
                #         qtpylib.crossed_above(df['gr'], df['gr'].shift(1)) &
                #         (df['kr'] > df['kr'].shift(1))
                #     ) | (
                #         qtpylib.crossed_above(df['kr'], df['kr'].shift(1)) &
                #         (df['gr'] > df['gr'].shift(1))
                #     )
                # )
            ),
            'enter_long'
        ] = 1
        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df.loc[
            (qtpylib.crossed_below(df['kr'], df['kr'].shift(1)) & False),
            'exit_long'
        ] = 1
        return df

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, exit_reason: str,
                           current_time: datetime, **kwargs) -> bool:
        profit = trade.calc_profit_ratio(rate)
        if (((exit_reason == 'force_exit')) and (profit < 0.01)):
            return False
        if pair in self.custom_info:
            del self.custom_info[pair]
        return True

    def trade_candle(self, pair, trade, current_rate):
        if pair not in self.custom_info:
            df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            last_candle = df.iloc[-1].squeeze()
            # ATR stoploss
            # diff = last_candle['atr'] * self.sl

            # Swing Low Stoploss
            diff = (trade.open_rate - df['low'].rolling(14).min().iloc[-1]) * 1.1

            self.custom_info[pair] = {
                'last_candle': last_candle,
                'sl': trade.open_rate - diff,
                'pt': trade.open_rate + (diff * 1.5),
                'diff': diff
            }

        last_candle = self.custom_info[pair]
        return last_candle

    def custom_exit(self, pair: str, trade: Trade, current_time: 'datetime',
                    current_rate: float, current_profit: float, **kwargs):

        candle = self.trade_candle(pair, trade, current_rate)
        pt = candle['pt']
        if (pt < current_rate):
            return 'Profit Booked'

        # break_even = trade.open_rate + candle['diff']
        # if (break_even < current_rate):
        #     candle['sl'] = trade.open_rate + (candle['diff'] * 0.1)

        sl = candle['sl']
        if (sl > current_rate):
            return 'Stop Loss Hit'

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> float:
        candle = self.trade_candle(pair, trade, current_rate)
        def get_stoploss(atr):
            return stoploss_from_absolute(
                current_rate - (candle['atr'] * atr), 
                current_rate, 
                is_short=trade.is_short
            ) * -1

        pt = trade.open_rate + (candle['atr'] * self.sl)
        if (pt < current_rate):
            return get_stoploss(self.sl/2)
        return 1