# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/bot-mssm-04-k8s-namespace/Apollo11.py%20GodStra.py
kubectl --context=gke_vaulted-gift-406223_europe-west1-b_private-cluster-3 -n bot-mssm-04 exec -it pod/freqtrade-bot-mssm-04-6786cbc6b-82ztj -c freqtrade -- cat /freqtrade/user_data/strategies/Github_DerSalvador_freqtrade_helm_chart__Apollo11_pyGodStra__20260115_122204.py GodStra.py
from datetime import datetime
from datetime import timedelta
from functools import reduce
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy
from pandas import DataFrame

def to_minutes(**timdelta_kwargs):
    return int(timedelta(**timdelta_kwargs).total_seconds() / 60)

class Github_DerSalvador_freqtrade_helm_chart__Apollo11_pyGodStra__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '15m'
    # Stoploss
    stoploss = -0.16
    startup_candle_count: int = 480
    trailing_stop = False
    use_custom_stoploss = True
    use_exit_signal = False
    # signal controls
    entry_signal_1 = True
    entry_signal_2 = True
    entry_signal_3 = True
    # ROI table:
    # This is 10000%, which basically disables ROI
    minimal_roi = {'0': 10}
    # Indicator values:
    # Signal 1
    s1_ema_xs = 3
    s1_ema_sm = 5
    s1_ema_md = 10
    s1_ema_xl = 50
    s1_ema_xxl = 240
    # Signal 2
    s2_ema_input = 50
    s2_ema_offset_input = -1
    s2_bb_sma_length = 49
    s2_bb_std_dev_length = 64
    s2_bb_lower_offset = 3
    s2_fib_sma_len = 50
    s2_fib_atr_len = 14
    s2_fib_lower_value = 4.236

    @property
    def protections(self):
        # Don't enter a trade right after exiting a trade.
        # Stop trading if max-drawdown is reached.
        # Considering all pairs that have a minimum of 20 trades
        # If max-drawdown is > 20% this will activate
        # Stop trading if a certain amount of stoploss occurred within a certain time window.
        # Considering all pairs that have a minimum of 4 trades
        # Looks at all pairs
        # Lock pairs with low profits
        # Considering all pairs that have a minimum of 2 trades
        # If profit < 2% this will activate for a pair
        # Lock pairs with low profits
        # Considering all pairs that have a minimum of 4 trades
        # If profit < 1% this will activate for a pair
        return [{'method': 'CooldownPeriod', 'stop_duration': to_minutes(minutes=0)}, {'method': 'MaxDrawdown', 'lookback_period': to_minutes(hours=12), 'trade_limit': 20, 'stop_duration': to_minutes(hours=1), 'max_allowed_drawdown': 0.2}, {'method': 'StoplossGuard', 'lookback_period': to_minutes(hours=6), 'trade_limit': 4, 'stop_duration': to_minutes(minutes=30), 'only_per_pair': False}, {'method': 'LowProfitPairs', 'lookback_period': to_minutes(hours=1, minutes=30), 'trade_limit': 2, 'stop_duration': to_minutes(hours=15), 'required_profit': 0.02}, {'method': 'LowProfitPairs', 'lookback_period': to_minutes(hours=6), 'trade_limit': 4, 'stop_duration': to_minutes(minutes=30), 'required_profit': 0.01}]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Adding EMA's into the dataframe
        dataframe['s1_ema_xs'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xs)
        dataframe['s1_ema_sm'] = ta.EMA(dataframe, timeperiod=self.s1_ema_sm)
        dataframe['s1_ema_md'] = ta.EMA(dataframe, timeperiod=self.s1_ema_md)
        dataframe['s1_ema_xl'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xl)
        dataframe['s1_ema_xxl'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xxl)
        s2_ema_value = ta.EMA(dataframe, timeperiod=self.s2_ema_input)
        s2_ema_xxl_value = ta.EMA(dataframe, timeperiod=200)
        dataframe['s2_ema'] = s2_ema_value - s2_ema_value * self.s2_ema_offset_input
        dataframe['s2_ema_xxl_off'] = s2_ema_xxl_value - s2_ema_xxl_value * self.s2_fib_lower_value
        dataframe['s2_ema_xxl'] = ta.EMA(dataframe, timeperiod=200)
        s2_bb_sma_value = ta.SMA(dataframe, timeperiod=self.s2_bb_sma_length)
        s2_bb_std_dev_value = ta.STDDEV(dataframe, self.s2_bb_std_dev_length)
        dataframe['s2_bb_std_dev_value'] = s2_bb_std_dev_value
        dataframe['s2_bb_lower_band'] = s2_bb_sma_value - s2_bb_std_dev_value * self.s2_bb_lower_offset
        s2_fib_atr_value = ta.ATR(dataframe, timeframe=self.s2_fib_atr_len)
        s2_fib_sma_value = ta.SMA(dataframe, timeperiod=self.s2_fib_sma_len)
        dataframe['s2_fib_lower_band'] = s2_fib_sma_value - s2_fib_atr_value * self.s2_fib_lower_value
        s3_bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['s3_bb_lowerband'] = s3_bollinger['lower']
        # Volume weighted MACD
        dataframe['fastMA'] = ta.EMA(dataframe['volume'] * dataframe['close'], 12) / ta.EMA(dataframe['volume'], 12)
        dataframe['slowMA'] = ta.EMA(dataframe['volume'] * dataframe['close'], 26) / ta.EMA(dataframe['volume'], 26)
        dataframe['vwmacd'] = dataframe['fastMA'] - dataframe['slowMA']
        dataframe['signal'] = ta.EMA(dataframe['vwmacd'], 9)
        dataframe['hist'] = dataframe['vwmacd'] - dataframe['signal']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # basic entry methods to keep the strategy simple
        if self.entry_signal_1:
            conditions = [dataframe['vwmacd'] < dataframe['signal'], dataframe['low'] < dataframe['s1_ema_xxl'], dataframe['close'] > dataframe['s1_ema_xxl'], qtpylib.crossed_above(dataframe['s1_ema_sm'], dataframe['s1_ema_md']), dataframe['s1_ema_xs'] < dataframe['s1_ema_xl'], dataframe['volume'] > 0]
            dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'entry_signal_1')
        if self.entry_signal_2:
            conditions = [qtpylib.crossed_above(dataframe['s2_fib_lower_band'], dataframe['s2_bb_lower_band']), dataframe['close'] < dataframe['s2_ema'], dataframe['volume'] > 0]
            dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'entry_signal_2')
        if self.entry_signal_3:
            conditions = [dataframe['low'] < dataframe['s3_bb_lowerband'], dataframe['low'] > dataframe['s1_ema_xxl'], dataframe['volume'] > 0]
            dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'entry_signal_3')
        if not all([self.entry_signal_1, self.entry_signal_2, self.entry_signal_3]):
            dataframe.loc[(), 'enter_long'] = 0
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # This is essentailly ignored as we're using strict ROI / Stoploss / TTP sale scenarios
        dataframe.loc[(), 'exit_long'] = 0
        return dataframe

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        if current_profit > 0.2:
            return 0.04
        if current_profit > 0.1:
            return 0.03
        if current_profit > 0.06:
            return 0.02
        if current_profit > 0.03:
            return 0.01
        # Let's try to minimize the loss
        if current_profit <= -0.1:
            if trade.open_date_utc + timedelta(hours=60) < current_time:
                # After 60H since entry
                return current_profit / 1.75
        if current_profit <= -0.08:
            if trade.open_date_utc + timedelta(hours=120) < current_time:
                # After 120H since entry
                return current_profit / 1.7
        return -1cat: GodStra.py: No such file or directory