# --- Do not remove these libs ---
from freqtrade.strategy 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
import numpy  # noqa


class Strategy002(IStrategy):
    """
    Strategy 002 - Multi-Indicator Confirmation
    author@: Gerald Lonlas
    github@: https://github.com/freqtrade/freqtrade-strategies

    How to use it?
    > python3 ./freqtrade/main.py -s Strategy002
    
    Entry: Low RSI + Low Stochastic + Price below BB Lower + Hammer pattern
    Exit: SAR reversal + High Fisher RSI
    
    This strategy uses multiple indicators for confirmation:
    - RSI (Relative Strength Index)
    - Stochastic oscillator
    - Bollinger Bands
    - SAR (Parabolic SAR)
    - Hammer candlestick pattern
    """

    INTERFACE_VERSION: int = 3
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "60":  0.01,
        "30":  0.03,
        "20":  0.04,
        "0":  0.05
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.10

    # Optimal timeframe for the strategy
    timeframe = '5m'

    # trailing stoploss
    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02

    # 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 = False

    # Optional order type mapping
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        """

        # Stochastic Oscillator
        stoch = ta.STOCH(dataframe)
        dataframe['slowk'] = stoch['slowk']

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)

        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']

        # SAR Parabolic
        dataframe['sar'] = ta.SAR(dataframe)

        # Hammer: values [0, 100]
        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        
        Buy conditions:
        - RSI < 30 (oversold)
        - Stochastic slowk < 20 (oversold)
        - Price below lower Bollinger Band
        - Hammer candlestick pattern detected
        """
        dataframe.loc[
            (
                (dataframe['rsi'] < 30) &
                (dataframe['slowk'] < 20) &
                (dataframe['bb_lowerband'] > dataframe['close']) &
                (dataframe['CDLHAMMER'] == 100)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        
        Sell conditions:
        - SAR crosses above price (reversal signal)
        - Fisher RSI > 0.3 (overbought condition)
        """
        dataframe.loc[
            (
                (dataframe['sar'] > dataframe['close']) &
                (dataframe['fisher_rsi'] > 0.3)
            ),
            'exit_long'] = 1
        return dataframe