# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/yoyo_action_strategy.py
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

class Github_DerSalvador_freqtrade_helm_chart__yoyo_action_strategy__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {'0': 10}
    timeframe = '4h'
    # Optional order type mapping
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False}
    # emaFast = 6
    # emaSlow = 18
    emaFast = 24
    emaSlow = 112
    rsiPeriod = 14
    overBought = 80
    overSold = 30
    #stoploss = -0.20
    # Fast Trail 
    atrFast = 6
    atrFM = 0.5  # fast ATR multiplier
    # Slow Trail 
    atrSlow = 18  # Slow ATR perod
    atrSM = 2  # Slow ATR multiplier
    # Trailing stoploss
    trailing_stop = False

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ohlc4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=self.emaFast)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=self.emaSlow)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.rsiPeriod)
        dataframe['macd'] = dataframe['ema_fast'] - dataframe['ema_slow']
        dataframe['bullish'] = dataframe['macd'] > 0
        dataframe['bearish'] = dataframe['macd'] < 0
        dataframe['sl1'] = self.atrFM * ta.ATR(dataframe.high, dataframe.low, dataframe.close, timeperiod=self.atrFast)  # Stop Loss
        dataframe['sl2'] = self.atrSM * ta.ATR(dataframe.high, dataframe.low, dataframe.close, timeperiod=self.atrSlow)
        dataframe.dropna(inplace=True)
        dataframe['red'] = False
        dataframe['brown'] = False
        dataframe['yellow'] = False
        dataframe['blue'] = False
        dataframe['green'] = False
        dataframe['long'] = False
        dataframe['preBuy'] = False
        dataframe['short'] = False
        dataframe['preSell'] = False
        dataframe['trail2'] = 0.0
        for index in range(len(dataframe)):
            # Green = bullish and mainSource>fast
            dataframe.green.iloc[index] = dataframe.bullish.iloc[index] and dataframe.ohlc4.iloc[index] > dataframe.ema_fast.iloc[index]
            # Blue = bearish and mainSource>fast and mainSource>slow
            dataframe.blue.iloc[index] = dataframe.bearish.iloc[index] and dataframe.ohlc4.iloc[index] > dataframe.ema_fast.iloc[index]
            # Yellow = bullish and mainSource<fast and mainSource>slow
            dataframe.yellow.iloc[index] = dataframe.bullish.iloc[index] and dataframe.ohlc4.iloc[index] < dataframe.ema_slow.iloc[index]
            # Brown = bullish and mainSource<fast and mainSource<slow
            dataframe.brown.iloc[index] = dataframe.bullish.iloc[index] and dataframe.ohlc4.iloc[index] < dataframe.ema_fast.iloc[index] and (dataframe.ohlc4.iloc[index] < dataframe.ema_slow.iloc[index])
            # Red = bearish and mainSource<fast
            dataframe.red.iloc[index] = dataframe.bearish.iloc[index] and dataframe.ohlc4.iloc[index] < dataframe.ema_fast.iloc[index]
            # iff(SC>nz(Trail2[1],0)                                    and SC[1]>nz(Trail2[1],0)
            if dataframe.close.iloc[index] > dataframe.trail2.iloc[index - 1] and dataframe.close.iloc[index - 1] > dataframe.trail2.iloc[index - 1]:
                dataframe.trail2.iloc[index] = max(dataframe.trail2.iloc[index - 1], dataframe.close.iloc[index] - dataframe.sl2.iloc[index])
            # iff(SC<nz(Trail2[1],0)                                        and SC[1]<nz(Trail2[1],0)
            elif dataframe.close.iloc[index] < dataframe.trail2.iloc[index - 1] and dataframe.close.iloc[index - 1] < dataframe.trail2.iloc[index - 1]:
                dataframe.trail2.iloc[index] = min(dataframe.trail2.iloc[index - 1], dataframe.close.iloc[index - 1] + dataframe.sl2.iloc[index - 1])
            # iff(SC>nz(Trail2[1],0),    
            elif dataframe.close.iloc[index] > dataframe.trail2.iloc[index - 1]:
                dataframe.trail2.iloc[index] = dataframe.close.iloc[index] - dataframe.sl2.iloc[index]
            else:
                dataframe.trail2.iloc[index] = dataframe.close.iloc[index] + dataframe.sl2.iloc[index]
            # it can use rolling
            dataframe.long.iloc[index] = dataframe.bullish.iloc[index] and dataframe.bullish.iloc[index - 1]
            dataframe.preBuy.iloc[index] = dataframe.bullish.iloc[index] and dataframe.bullish.iloc[index - 1]
            # dataframe.preSell.iloc[index] =  dataframe.yellow.iloc[index] and ta.
            dataframe.short.iloc[index] = dataframe.bearish.iloc[index] and dataframe.bearish.iloc[index - 1]
        # greenLine = SC>Trail2
        dataframe['greenLine'] = False
        dataframe.loc[dataframe['close'] > dataframe['trail2'], 'greenLine'] = True
        dataframe['greenLine_last'] = dataframe.greenLine.shift(1)
        dataframe['short_last'] = dataframe.short.shift(1)
        dataframe['green_last'] = dataframe.green.shift(1)
        dataframe['red_last'] = dataframe.red.shift(1)
        dataframe['hold_state'] = False
        dataframe.dropna(inplace=True)
        dataframe
        # greenLine = SC>Trail2
        dataframe['greenLine'] = False
        dataframe.loc[dataframe['close'] > dataframe['trail2'], 'greenLine'] = True
        dataframe['greenLine_last'] = dataframe.greenLine.shift(1)
        dataframe['short_last'] = dataframe.short.shift(1)
        dataframe['green_last'] = dataframe.green.shift(1)
        dataframe['red_last'] = dataframe.red.shift(1)
        dataframe['hold_state'] = False
        dataframe.dropna(inplace=True)  # Green entry
        # Over ATR and blue
        dataframe.loc[(dataframe['green_last'] == False) & (dataframe['green'] == True) | (dataframe['greenLine'] == True) & (dataframe['blue'] == True), 'signal_entry'] = True  # Red Sell
        # | ((dataframe['greenLine_last'] == True) & (dataframe['greenLine'] == False)) # Stop lost
        dataframe.loc[(dataframe['red_last'] == False) & (dataframe['red'] == True), 'signal_exit'] = True
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['signal_entry'] == True, 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['signal_exit'] == True, 'exit_long'] = 1
        return dataframe