# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-futures-k8s-namespace/SlowPotato.py
# Github_DerSalvador_freqtrade_helm_chart__SlowPotato__20260416_224245 is a slow order strategy based on averages (5 days) executed on 5m interval 
# the premise is to entry once the average low (5 days) is reached or lower and wait for exit trigger once the average high (5 days) is reached or higher 
# If you want to help with this small endevor please reach me via discord jadex#0557
# If you want to show your support donations are always accepted 
# BTC = 13PustEinvinjud3wCARGHqz34j3GAifjC
# ETH (ERC20) = 0x1b2aaceff8e4475f28280186553c07286e7e3e53
## Suggestions and improvements always welcome.
### Version 0.1
###  to do list / things to be implemented:
### Hyperopt not profitable currently
### sorting of pairs by profit spread
### find a faster/better way to average the High/low for 1 day data for 5 days
### if a day passes after entrying with no exit rerun average high based on 5 day average high
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime
from freqtrade.persistence import Trade
from technical.util import resample_to_interval, resampled_merge
import logging
logger = logging.getLogger(__name__)

class Github_DerSalvador_freqtrade_helm_chart__SlowPotato__20260416_224245(IStrategy):
    INTERFACE_VERSION = 3
    '\n    This strategy uses the averages for the last 5 days high/low and sets up entry and exit orders acordingly\n    Currently developing and testing this strategy\n    '  #if you overide this it will exit once it reaches a certain ROI threshold rather than the exit logic
    minimal_roi = {'0': 99}
    stoploss = -0.99
    # Optimal timeframe for the strategy 
    timeframe = '5m'
    # trailing stoploss 
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    # Experimental settings (configuration will overide these if set)
    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = True
    # Optional order type mapping
    order_types = {'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    # run "populate_indicators" only for new candle
    process_only_new_candles = False

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

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        If close candle breaks lower or equal to average low for last 5 days entry it
        """
        #(dataframe['high'].rolling(1440).mean() / dataframe['low'].rolling(1440).mean() >= 1.05) & ## average spread is #% of profit
        ## current dataframe is below average low
        # volume above zero
        dataframe.loc[(dataframe['low'] <= dataframe['low'].rolling(1440).mean()) & (dataframe['volume'] > 0), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        If open candle breaks higher or equal to average high for last 5 days exit it
        """  ## current dataframe is above average high
        # volume above zero
        dataframe.loc[(dataframe['high'] >= dataframe['high'].rolling(1440).mean()) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe