# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Kamaflage.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair
from pandas import DataFrame, Series
from functools import reduce
from datetime import datetime
from freqtrade.persistence import Trade
from technical.indicators import RMI, VIDYA

class Github_DerSalvador_freqtrade_helm_chart__Kamaflage__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n    PASTE OUTPUT FROM HYPEROPT HERE\n    '
    entry_params = {'macd': 0, 'macdhist': 0, 'rmi': 50}
    exit_params = {}
    minimal_roi = {'0': 0.15, '10': 0.1, '20': 0.05, '30': 0.025, '60': 0.01}
    # Stoploss:
    stoploss = -1
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01125
    trailing_stop_positive_offset = 0.04673
    trailing_only_offset_is_reached = True
    '\n    END HYPEROPT\n    '
    timeframe = '5m'
    use_exit_signal = True
    exit_profit_only = False
    # exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True
    process_only_new_candles = False
    startup_candle_count: int = 20

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['sar'] = ta.SAR(dataframe)
        dataframe['rmi'] = RMI(dataframe)
        dataframe['kama-3'] = ta.KAMA(dataframe, timeperiod=3)
        dataframe['kama-21'] = ta.KAMA(dataframe, timeperiod=21)
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        dataframe['volume_ma'] = dataframe['volume'].rolling(window=24).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.entry_params
        conditions = []
        active_trade = False
        if self.config['runmode'].value in ('live', 'dry_run'):
            active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all()
        if not active_trade:
            conditions.append(dataframe['kama-3'] > dataframe['kama-21'])
            conditions.append(dataframe['macd'] > dataframe['macdsignal'])
            conditions.append(dataframe['macd'] > params['macd'])
            conditions.append(dataframe['macdhist'] > params['macdhist'])
            conditions.append(dataframe['rmi'] > dataframe['rmi'].shift())
            conditions.append(dataframe['rmi'] > params['rmi'])
            conditions.append(dataframe['volume'] < dataframe['volume_ma'] * 20)
        else:
            conditions.append(dataframe['close'] > dataframe['sar'])
            conditions.append(dataframe['rmi'] >= 75)
        conditions.append(dataframe['volume'] > 0)
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.exit_params
        conditions = []
        active_trade = False
        if self.config['runmode'].value in ('live', 'dry_run'):
            active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all()
        if active_trade:
            ob = self.dp.orderbook(metadata['pair'], 1)
            current_price = ob['asks'][0][0]
            current_profit = active_trade[0].calc_profit_ratio(rate=current_price)
            conditions.append((dataframe['entry'] == 0) & (dataframe['rmi'] < 30) & (current_profit > -0.03) & dataframe['volume'].gt(0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit'] = 1
        else:
            dataframe['exit'] = 0
        return dataframe

    def check_entry_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['bids'][0][0]
        # Cancel entry order if price is more than 1% above the order.
        if current_price > order['price'] * 1.01:
            return True
        return False

    def check_exit_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['asks'][0][0]
        # Cancel exit order if price is more than 1% below the order.
        if current_price < order['price'] * 0.99:
            return True
        return False

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['asks'][0][0]
        # Cancel entry order if price is more than 1% above the order.
        if current_price > rate * 1.01:
            return False
        return True
    '\n    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:\n        _, roi = self.min_roi_reached_entry(0)\n\n        if roi is None:\n           if Trade.max_rate >= Trade.rate * 0.8 and Trade.rate > Trade.open_rate: \n                return False\n            if Trade.max_rate < Trade.rate * 0.8 and Trade.rate < Trade.open_rate: \n                return False\n            if Trade.max_rate < Trade.rate * 0.8 and Trade.rate > Trade.open_rate: \n                return current_profit > roi\n        return False\n    '