# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/PlutoBUScalper1.py
# Hour Strategy
# In this strategy we try to find the best hours to buy and sell in a day.(in hourly timeframe)
# Because of that you should just use 1h timeframe on this strategy.
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# Requires hyperopt before running.
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy HourBasedStrategy -e 200

import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.strategy import DecimalParameter
from freqtrade.strategy import IntParameter, IStrategy
from pandas import DataFrame


# --------------------------------
# Add your lib to import here
# No need to These imports. just for who want to add more conditions:
# import talib.abstract as ta
# import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_rmallarapu_bc_brahma__PlutoBUScalper1__20240229_213751(IStrategy):
    # SHIB/USDT, 1000$x1:100days
    # 158/1000:     51 trades. 29/19/3 Wins/Draws/Losses. Avg profit   4.02%. Median profit   2.48%. Total profit  4867.53438466 USDT ( 486.75%). Avg duration 1 day, 19:38:00 min. Objective: -4.17276
    # buy_params = {"buy_hour_max": 18,"buy_hour_min": 7,}
    # sell_params = {"sell_hour_max": 9,"sell_hour_min": 21,}
    # minimal_roi = {"0": 0.18,"171": 0.155,"315": 0.075,"1035": 0}
    # stoploss = -0.292

    # SHIB/USDT, 1000$x1:100days
    # 36/1000:    113 trades. 55/14/44 Wins/Draws/Losses. Avg profit   2.06%. Median profit   0.00%. Total profit  5126.14785426 USDT ( 512.61%). Avg duration 16:48:00 min. Objective: -4.57837
    # buy_params = {"buy_hour_max": 21,"buy_hour_min": 6,}
    # sell_params = {"sell_hour_max": 6,"sell_hour_min": 4,}
    # minimal_roi = {"0": 0.247,"386": 0.186,"866": 0.052,"1119": 0}
    # stoploss = -0.302

    # SAND/USDT, 1000$x1:100days
    # 72/1000:    158 trades. 67/13/78 Wins/Draws/Losses. Avg profit   1.37%. Median profit   0.00%. Total profit  4274.73622346 USDT ( 427.47%). Avg duration 13:50:00 min. Objective: -4.87331
    # buy_params = {"buy_hour_max": 23,"buy_hour_min": 4,}
    # sell_params = {"sell_hour_max": 23,"sell_hour_min": 3,}
    # minimal_roi = {"0": 0.482,"266": 0.191,"474": 0.09,"1759": 0}
    # stoploss = -0.05

    # KDA/USDT, 1000$x1:100days
    # 7/1000:     65 trades. 40/23/2 Wins/Draws/Losses. Avg profit   6.42%. Median profit   7.59%. Total profit  41120.00939125 USDT ( 4112.00%). Avg duration 1 day, 9:40:00 min. Objective: -8.46089
    # buy_params = {"buy_hour_max": 22,"buy_hour_min": 9,}
    # sell_params = {"sell_hour_max": 1,"sell_hour_min": 7,}
    # minimal_roi = {"0": 0.517,"398": 0.206,"1003": 0.076,"1580": 0}
    # stoploss = -0.338

    # {KDA/USDT, BTC/USDT, DOGE/USDT, SAND/USDT, ETH/USDT, SOL/USDT}, 1000$x1:100days, ShuffleFilter42
    # 56/1000:     63 trades. 41/19/3 Wins/Draws/Losses. Avg profit   4.60%. Median profit   8.89%. Total profit  11596.50333022 USDT ( 1159.65%). Avg duration 1 day, 14:46:00 min. Objective: -5.76694

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_hour_max": 24,
        "buy_hour_min": 4,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_hour_max": 21,
        "sell_hour_min": 22,
    }

    minimal_roi = {
        "0": 0.02,
        "300": 0.01,
        "600": 0.005,
        "900": 0
    }
    stoploss = -0.3

    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.002
    trailing_stop_positive_offset = 0.0075
    trailing_only_offset_is_reached = True

    # Optimal timeframe
    timeframe = '1h'

    buy_hour_min = IntParameter(0, 24, default=0, space='buy')
    buy_hour_max = IntParameter(0, 24, default=24, space='buy')

    sell_hour_min = IntParameter(0, 24, default=0, space='sell')
    sell_hour_max = IntParameter(0, 24, default=24, space='sell')

    buy_limit = DecimalParameter(0.90, 0.98, default=0.98, space='buy', decimals=3, optimize=True, load=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['hour'] = dataframe['date'].dt.hour

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

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

        # TEMA - Triple Exponential Moving Average
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (dataframe['hour'].between(self.buy_hour_min.value, self.buy_hour_max.value))
                    & (dataframe['close'] <= self.buy_limit.value * dataframe['bb_lowerband'])
                    # & (qtpylib.crossed_below(dataframe["rsi"], 30))
                    # & (dataframe["tema"] <= dataframe["bb_middleband"])
                    & (dataframe["tema"] > dataframe["tema"].shift(1))
                    & (dataframe["volume"] > 0)
            ),
            'enter_long'] = 1

        # dataframe.loc[
        #     (
        #             (dataframe['hour'].between(self.buy_hour_min.value, self.buy_hour_max.value))
        #             & (dataframe['close'] > self.buy_limit.value * dataframe['bb_lowerband'])
        #             # & (qtpylib.crossed_below(dataframe["rsi"], 30))
        #             # & (dataframe["tema"] <= dataframe["bb_middleband"])
        #             # & (dataframe["tema"] > dataframe["tema"].shift(1))
        #             & (dataframe["volume"] > 0)
        #     ),
        #     'enter_short'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return super().populate_exit_trend(dataframe, metadata)