# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/FrostAuraM315mStrategy.py
import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class Github_DerSalvador_freqtrade_helm_chart__FrostAuraM315mStrategy__20260115_122204(IStrategy):
    """
    This is FrostAura's mark 3 strategy which aims to make purchase decisions
    based on the BB, RSI and Stochastic.
    
    Last Optimization:
        Sharpe Ratio    : 6.23754
        Profit %        : 1311.96%
        Optimized for   : Last 115+ days, 15m
        Avg             : 986.6m
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3
    # Minimal ROI designed for the strategy.
    minimal_roi = {'0': 0.18418, '36': 0.10736, '91': 0.02998, '438': 0}
    # Optimal stoploss designed for the strategy.
    stoploss = -0.41114
    # Trailing stoploss
    trailing_stop = False
    # Optimal ticker interval for the strategy.
    timeframe = '15m'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals.
    startup_candle_count: int = 30
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    plot_config = {'main_plot': {'tema': {}, 'sar': {'color': 'white'}}, 'subplots': {'MACD': {'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}}, 'RSI': {'rsi': {'color': 'red'}}}}

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        # Stochastic Slow
        stoch = ta.STOCH(dataframe)
        dataframe['slowd'] = stoch['slowd']
        dataframe['slowk'] = stoch['slowk']
        # Bollinger Bands
        bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
        dataframe['bb_lowerband1'] = bollinger1['lower']
        dataframe['bb_middleband1'] = bollinger1['mid']
        dataframe['bb_upperband1'] = bollinger1['upper']
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']
        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb_lowerband3'] = bollinger3['lower']
        dataframe['bb_middleband3'] = bollinger3['mid']
        dataframe['bb_upperband3'] = bollinger3['upper']
        bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
        dataframe['bb_lowerband4'] = bollinger4['lower']
        dataframe['bb_middleband4'] = bollinger4['mid']
        dataframe['bb_upperband4'] = bollinger4['upper']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        minimum_coin_price = 1.5e-06
        #(dataframe['slowd'] > 30) &
        #(dataframe['slowk'] > 30) &
        dataframe.loc[(dataframe['rsi'] > 22) & (dataframe['slowk'] < dataframe['slowd']) & (dataframe['close'] < dataframe['bb_lowerband3']) & (dataframe['close'] > minimum_coin_price), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['slowk'] < dataframe['slowd']) & (dataframe['rsi'] > 72) & (dataframe['close'] > dataframe['bb_middleband1']), 'exit_long'] = 1
        return dataframe