# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/redditMA.py
"""
3. sma ema with complicated support
4. sma ema with simple support
4-0.2. sma ema with simple support with 0.2 SL
5. sma wma with simple support
6. sma wma with VWAP simple support
"""
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
from finta import TA as F
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np  # noqa

class Github_DerSalvador_freqtrade_helm_chart__redditMA__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.5, '30': 0.3, '60': 0.125, '120': 0.06, '180': 0.01}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.5
    # Optimal ticker interval for the strategy
    timeframe = '15m'
    # trailing stoploss
    trailing_stop = False
    # run "populate_indicators" only for new candle
    process_only_new_candles = True
    # Experimental settings (configuration will overide these if set)
    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = True
    # Optional order type mapping
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    startup_candle_count = 34

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['SLOWMA'] = F.EMA(dataframe, 13)
        dataframe['FASTMA'] = F.EMA(dataframe, 34)
        # dataframe = self.mods(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[qtpylib.crossed_above(dataframe['FASTMA'], dataframe['SLOWMA']), 'entry'] = 1
        return dataframe

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