DefaultStrategy

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class DefaultStrategy(IStrategy):
    INTERFACE_VERSION = 3
    
    # Strategy parameters
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }
    
    stoploss = -0.10
    
    # Optimal timeframe for the strategy
    timeframe = '5m'
    
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_exit_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
    }
    
    def informative_pairs(self):
        return []
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        
        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        
        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        
        # EMA
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        
        return dataframe
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], 30)) &
                (dataframe['macd'] > dataframe['macdsignal']) &
                (dataframe['close'] > dataframe['ema10']) &
                (dataframe['volume'] > 0)
            ),
            'enter_long'] = 1
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], 70)) |
                (dataframe['close'] > dataframe['bb_upperband']) |
                (qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']))
            ),
            'exit_long'] = 1
        
        return dataframe