# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/conny.py
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.consensus import Consensus

class Github_DerSalvador_freqtrade_helm_chart__conny__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.025, '10': 0.015, '20': 0.01, '30': 0.005, '120': 0}
    stoploss = -0.0203
    timeframe = '15m'
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    startup_candle_count: int = 30

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Consensus strategy
        # add c.evaluate_indicator bellow to include it in the consensus score (look at
        # consensus.py in freqtrade technical)
        # add custom indicator with c.evaluate_consensus(prefix=<indicator name>)
        c = Consensus(dataframe)
        c.evaluate_rsi()
        c.evaluate_stoch()
        c.evaluate_macd_cross_over()
        c.evaluate_macd()
        c.evaluate_hull()
        c.evaluate_vwma()
        c.evaluate_tema(period=12)
        c.evaluate_ema(period=24)
        c.evaluate_sma(period=12)
        c.evaluate_laguerre()
        c.evaluate_osc()
        c.evaluate_cmf()
        c.evaluate_cci()
        c.evaluate_cmo()
        c.evaluate_ichimoku()
        c.evaluate_ultimate_oscilator()
        c.evaluate_williams()
        c.evaluate_momentum()
        c.evaluate_adx()
        dataframe['consensus_entry'] = c.score()['entry']
        dataframe['consensus_exit'] = c.score()['exit']
        print(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['consensus_entry'] > 45) & (dataframe['volume'] > 0), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['consensus_exit'] > 88) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe