# source: https://raw.githubusercontent.com/devsmitra/trading/a318dd6846d309977bd1753a10503dcbd299b1d2/user_data/strategies/Candlestick.py
# --- Do not remove these libs ---
from datetime import datetime
from typing import Any, Optional
from freqtrade.strategy import IStrategy, informative
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade

from support import identify_df_trends

# --------------------------------


class github_devsmitra_trading__Candlestick__20220819_173947(IStrategy):
    cache: Any = {}

    INTERFACE_VERSION: int = 3
    process_only_new_candles: bool = False
    # Optimal timeframe for the strategy
    timeframe = '15m'

    minimal_roi = {
        "0": 1
    }

    # Optimal stoploss designed for the strategy
    stoploss = -0.05
    use_custom_stoploss = True

    # @informative('5m')
    # def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    #     dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
    #     self.get_trend(dataframe, metadata)
    #     return dataframe

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        if current_profit > 0.075:
            return -.025
        if current_profit > 0.05:
            return -.05
        return -.1

    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 get_trend(self, dataframe: DataFrame, metadata: dict):
        pair = metadata['pair']
        prev = self.cache.get(pair,  {'date': dataframe.iloc[-2]['date'], 'Trend': 0})
        date = dataframe.iloc[-1]['date']
        if (date != prev['date']):
            df = identify_df_trends(dataframe, 'close', window_size=3)
            self.cache[pair] = {'date': date, 'Trend': df['Trend']}
        else:
            dataframe['Trend'] = prev['Trend']

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        self.get_trend(dataframe, metadata)
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        crossed = False
        for i in range(0, 2):
            crossed = crossed | (qtpylib.crossed_above(dataframe.shift(i)['Trend'], 0) & (dataframe.shift(i)['adx'] > 20))

        dataframe.loc[
            crossed,
            'enter_long'
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_below(dataframe['adx'], 25) & (dataframe['Trend'] == -1)) |
                (qtpylib.crossed_below(dataframe['Trend'], 0) & (dataframe['adx'] < 25)) |
                (qtpylib.crossed_below(dataframe.shift()['Trend'], 0) & (dataframe['adx'] < 25))
            ),
            'exit_long'] = 1
        return dataframe

    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') | (exit_reason == 'exit_signal')) and (profit < 0)):
            return False
        return True