TD

from pandas import DataFrame
from functools import reduce
import datetime
from typing import Optional
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
                                IStrategy, IntParameter)

#Start Strategy
class TD(IStrategy):

    minimal_roi = {
        "0":  0.10
    }
    stoploss = -0.40
    timeframe = '2h'
    max_entry_position_adjustment = 3
    position_adjustment_enable: True

    # This number is explained a bit further down
    max_dca_multiplier = 5.5

    ### hyper-opt parameters ###
    # entry optizimation
    max_epa = CategoricalParameter([-1, 0, 1, 3, 5, 10], default=1, space="buy", optimize=True)

    # protections
    cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True)
    stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True)
    use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)

    # indicators
    sma_ha_close = IntParameter(3, 15, default=3, space="buy", optimize=True)
    sma_ha_open = IntParameter(5, 15, default=5, space="sell", optimize=True)


    ### entry opt.
    @property
    def max_entry_position_adjustment(self):
        return self.max_epa.value


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

        prot.append({
            "method": "CooldownPeriod",
            "stop_duration_candles": self.cooldown_lookback.value
        })
        if self.use_stop_protection.value:
            prot.append({
                "method": "StoplossGuard",
                "lookback_period_candles": 24 * 3,
                "trade_limit": 4,
                "stop_duration_candles": self.stop_duration.value,
                "only_per_pair": False
            })

        return prot
    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:

        # We need to leave most of the funds for possible further DCA orders
        # This also applies to fixed stakes
        return proposed_stake / self.max_dca_multiplier

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake: Optional[float], max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs) -> Optional[float]:

     
        if current_profit > 0.10 and trade.nr_of_successful_exits == 0:
            # Take half of the profit at +5%
            return -(trade.stake_amount / 2)

        if current_profit > -0.05:
            return None

        # Obtain pair dataframe (just to show how to access it)
        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
        # Only buy when not actively falling price.
        last_candle = dataframe.iloc[-1].squeeze()
        previous_candle = dataframe.iloc[-2].squeeze()
        if last_candle['close'] < previous_candle['close']:
            return None

        filled_entries = trade.select_filled_orders(trade.entry_side)
        count_of_entries = trade.nr_of_successful_entries
        # Allow up to 3 additional increasingly larger buys (4 in total)
        # Initial buy is 1x
        # If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
        # If that falls down to -5% again, we buy 1.5x more
        # If that falls once again down to -5%, we buy 1.75x more
        # Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
        # That is why max_dca_multiplier is 5.5
        # Hope you have a deep wallet!
        try:
            # This returns first order stake size
            stake_amount = filled_entries[0].cost
            # This then calculates current safety order size
            stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
            return stake_amount
        except Exception as exception:
            return None

        return None

    ### indicators ###
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Generate all indicators used by the strategy"""

        # Heikin Ashi Strategy
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']



        # Calculate all ema_long values
        for val in self.sma_ha_close.range:
            dataframe[f'sma_ha_close{val}'] = ta.SMA(dataframe['ha_close'], timeperiod=val)
        for val in self.sma_ha_open.range:
            dataframe[f'sma_ha_open{val}'] = ta.SMA(dataframe['ha_open'], timeperiod=val)

        return dataframe


    ### buy logic ###
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(qtpylib.crossed_above(
                dataframe[f'sma_ha_close{self.sma_ha_close.value}'], dataframe[f'sma_ha_open{self.sma_ha_open.value}']
            ))

        # Check that volume is not 0
        conditions.append(dataframe['volume'] > 0)

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


    ### sell logic ###
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(qtpylib.crossed_above(
                dataframe[f'sma_ha_open{self.sma_ha_open.value}'], dataframe[f'sma_ha_close{self.sma_ha_close.value}']
            ))

        # Check that volume is not 0
        conditions.append(dataframe['volume'] > 0)

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'exit_long'] = 1
        return dataframe