# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import math
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import IntParameter

def optimize(space: str):

    def fn(val: int):
        perc = 0.5
        low = math.floor(val * (1 - perc))
        high = math.floor(val * (1 + perc))
        return IntParameter(default=val, low=low, high=high, space=space, optimize=True, load=True)
    return fn
# This strategy is based on https://www.tradingview.com/script/i3Uc79fF-Flawless-Victory-Strategy-15min-BTC-Machine-Learning-Strategy/
# Author of the original Pinescript strategy: Robert Roman (https://github.com/TreborNamor)

class FlawlessVictory(IStrategy):
    entryOptimize = optimize('entry')
    exitOptimize = optimize('exit')
    entry_rsi_length = entryOptimize(14)
    entry_bb_window = entryOptimize(20)
    entry_rsi_lower = entryOptimize(43)
    exit_rsi_length = exitOptimize(14)
    exit_bb_window = exitOptimize(20)
    exit_rsi_upper = exitOptimize(70)
    # 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
    stoploss = -999999
    # Optimal timeframe for the strategy.
    timeframe = '15m'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 50
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    plot_config = {'main_plot': {'bb_upperband': {'color': 'blue'}, 'bb_lowerband': {'color': 'blue'}}, 'subplots': {'RSI': {'rsi': {'color': 'purple'}, 'rsi_lower': {'color': 'black'}, 'rsi_upper': {'color': 'black'}}}}

    def informative_pairs(self):
        return []

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

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe['close'], int(self.entry_rsi_length.value))
        dataframe['rsi_lower'] = int(self.entry_rsi_lower.value)
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=int(self.entry_bb_window.value), stds=1)
        dataframe['bb_lowerband'] = bollinger['lower']
        bb_long = dataframe['close'] < dataframe['bb_lowerband']
        rsi_long = dataframe['rsi'] > dataframe['rsi_lower']
        dataframe['enter_long'] = bb_long & rsi_long
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe['close'], int(self.exit_rsi_length.value))
        dataframe['rsi_upper'] = int(self.exit_rsi_upper.value)
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=int(self.exit_bb_window.value), stds=1)
        dataframe['bb_upperband'] = bollinger['upper']
        bb_short = dataframe['close'] > dataframe['bb_upperband']
        rsi_short = dataframe['rsi'] > dataframe['rsi_upper']
        dataframe['exit_long'] = bb_short & rsi_short
        return dataframe