# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/adaptive.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas_ta as pta
import talib.abstract as ta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
# --------------------------------
# Williams %R

def williams_r(dataframe: DataFrame, period: int=14) -> Series:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
        of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
        Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
        of its recent trading range.
        The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """
    highest_high = dataframe['high'].rolling(center=False, window=period).max()
    lowest_low = dataframe['low'].rolling(center=False, window=period).min()
    WR = Series((highest_high - dataframe['close']) / (highest_high - lowest_low), name=f'{period} Williams %R')
    return WR * -100

class Github_DerSalvador_freqtrade_helm_chart__adaptive__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author: @jilv220\n\n    '
    # Minimal ROI designed for the strategy.
    # adjust based on market conditions. We would recommend to keep it low for quick turn arounds
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.02}
    # Optimal stoploss designed for the strategy
    stoploss = -0.109
    # Optimal timeframe for the strategy
    timeframe = '5m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # MAMA, FAMA, KAMA
        dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
        dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.25, 0.025)
        dataframe['mama_diff'] = (dataframe['mama'] - dataframe['fama']) / dataframe['hl2']
        dataframe['kama'] = ta.KAMA(dataframe['close'], 84)
        # CTI
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)
        # Williams %R
        dataframe['r_14'] = williams_r(dataframe, period=14)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['kama'] > dataframe['fama']) & (dataframe['fama'] > dataframe['mama'] * 0.981) & (dataframe['r_14'] < -61.3) & (dataframe['mama_diff'] < -0.025) & (dataframe['cti'] < -0.715) & (dataframe['close'].rolling(48).max() >= dataframe['close'] * 1.05) & (dataframe['close'].rolling(288).max() >= dataframe['close'] * 1.125) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60), 'entry'] = 1
        return dataframe

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