# source: https://raw.githubusercontent.com/theo-wq/Freqtrad-strategy-and-order/0aa0c17d89870f7d0f828cf6dc1caf20b3651d7f/freqtrad%20strategy/user_data/strategies/leadlag.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from typing import Optional, Union

import os
import os
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter, merge_informative_pair)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


# This class is a sample. Feel free to customize it.
class Github_theo_wq_Freqtrad_strategy_and_order__leadlag__20241115_125230(IStrategy):
    """
    This is a sample strategy to inspire you.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # 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

    # Can this strategy go short?
    can_short: bool = False

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "2400": 0.007,
        "1400": 0.009,
        "60": 0.01,
        "30": 0.015
    }

    stoploss = -0.08

    # Trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.08
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

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

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Hyperoptable parameters
    buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
    short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
    exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200

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

    def informative_pairs(self):
        return [(f"BTC/USDT", '1d')]

    # 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # Momentum Indicators
        # ------------------------------------

        dataframe['adx'] = ta.ADX(dataframe)

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

        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        # MFI
        dataframe['mfi'] = ta.MFI(dataframe)

        # OverlapStudies
        # ------------------------------------

        # 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']
        dataframe["bb_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"]) /
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        )

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

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

        # Cycle Indicator
        # ------------------------------------
        # Hilbert Transform Indicator - SineWave
        hilbert = ta.HT_SINE(dataframe)
        dataframe['htsine'] = hilbert['sine']
        dataframe['htleadsine'] = hilbert['leadsine']


        informativebtc = self.dp.get_pair_dataframe(pair=f"BTC/USDT", timeframe="1d")
        informativebtc5 = self.dp.get_pair_dataframe(pair=f"BTC/USDT", timeframe="5m")
        # calculate SMA20 on informative pair
        # Combine the 2 dataframe
        # This will result in a column named 'closeETH' or 'closeBTC' - depending on stake_currency.
        dataframe = merge_informative_pair(dataframe, informativebtc, self.timeframe, "1d", ffill=True,append_timeframe =False, suffix="BTC")
        dataframe = merge_informative_pair(dataframe, informativebtc5, self.timeframe, "5m", ffill=True,append_timeframe =False, suffix="BTC5")

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
                (dataframe['tema'] <= dataframe['bb_middleband']) &
                ((dataframe['close_BTC5'].shift(100)) > dataframe['close_BTC'])&
                (dataframe['volume'] > 0)
                #(dataframe['tema'] > dataframe['tema'].shift(1)) &
                #((dataframe['close_BTC'] * 0.99)<(dataframe['close_BTC5']))&
                #(dataframe['close_BTC5'] > dataframe['close_BTC'])&
            ),
            'enter_long'] = 1

        return dataframe

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

        dataframe.loc[
            (
                # Signal: RSI crosses above 70
                ((dataframe['close_BTC5'].shift(5)*0.97) > dataframe['close_BTC5'])&
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),

            'exit_long'] = 1


        return dataframe